stepan
/
dynare
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

gsa: converted DOS end of lines in Unix end of lines

time-shift
Michel Juillard 2011-12-09 21:13:16 +01:00
parent ec0af45fc8
commit bc8d4d8f08
33 changed files with 5855 additions and 5855 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1024
matlab/gsa/LPTAU.m
View File

File diff suppressed because it is too large Load Diff

260
matlab/gsa/Morris_Measure_Groups.m
View File

@ -1,130 +1,130 @@
function [SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p, Group)
% [SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p, Group)
%
% Given the Morris sample matrix, the output values and the group matrix compute the Morris measures
% -------------------------------------------------------------------------
% INPUTS
% -------------------------------------------------------------------------
% Group [NumFactor, NumGroups] := Matrix describing the groups.
% Each column represents one group.
% The element of each column are zero if the factor is not in the
% group. Otherwise it is 1.
% Sample := Matrix of the Morris sampled trajectories
% Output := Matrix of the output(s) values in correspondence of each point
% of each trajectory
% k = Number of factors
% -------------------------------------------------------------------------
% OUTPUTS
% OutMatrix (NumFactor*NumOutputs, 3)= [Mu*, Mu, StDev]
% for each output it gives the three measures of each factor
% -------------------------------------------------------------------------
if nargin==0,
disp(' ')
disp('[SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p, Group);')
return
end
OutMatrix=[];
if nargin < 5, Group=[]; end
NumGroups = size(Group,2);
if nargin < 4 | isempty(p),
p = 4;
end
Delt = p/(2*p-2);
if NumGroups ~ 0
sizea = NumGroups; % Number of groups
GroupMat=Group;
GroupMat = GroupMat';
else
sizea = NumFact;
end
r=size(Sample,1)/(sizea+1); % Number of trajectories
% For Each Output
for k=1:size(Output,2)
OutValues=Output(:,k);
% For each r trajectory
for i=1:r
% For each step j in the trajectory
% Read the orientation matrix fact for the r-th sampling
% Read the corresponding output values
Single_Sample = Sample(i+(i-1)*sizea:i+(i-1)*sizea+sizea,:);
Single_OutValues = OutValues(i+(i-1)*sizea:i+(i-1)*sizea+sizea,:);
A = (Single_Sample(2:sizea+1,:)-Single_Sample(1:sizea,:))';
Delta = A(find(A));
% For each point of the fixed trajectory compute the values of the Morris function. The function
% is partitioned in four parts, from order zero to order 4th.
for j=1:sizea % For each point in the trajectory i.e for each factor
% matrix of factor which changes
if NumGroups ~ 0;
AuxFind (:,1) = A(:,j);
% AuxFind(find(A(:,j)),1)=1;
% Pippo = sum((Group - repmat(AuxFind,1,NumGroups)),1);
% Change_factor(j,i) = find(Pippo==0);
Change_factor = find(abs(AuxFind)>1e-010);
% If we deal with groups we can only estimate the new mu*
% measure since factors in the same groups can move in
% opposite direction and the definition of the standard
% Morris mu cannopt be applied.
% In the new version the elementary effect is defined with
% the absolute value.
%SAmeas(find(GroupMat(Change_factor(j,i),:)),i) = abs((Single_OutValues(j) - Single_OutValues(j+1) )/Delt); %(2/3));
SAmeas(i,Change_factor') = abs((Single_OutValues(j) - Single_OutValues(j+1) )/Delt);
else
Change_factor(j,i) = find(Single_Sample(j+1,:)-Single_Sample(j,:));
% If no groups --> we compute both the original and
% modified measure
if Delta(j) > 0 %=> +Delta
SAmeas(Change_factor(j,i),i) = (Single_OutValues(j+1) - Single_OutValues(j) )/Delt; %(2/3);
else %=> -Delta
SAmeas(Change_factor(j,i),i) = (Single_OutValues(j) - Single_OutValues(j+1) )/Delt; %(2/3);
end
end
end %for j=1:sizea
end %for i=1:r
if NumGroups ~ 0
SAmeas = SAmeas';
end
% Compute Mu AbsMu and StDev
if any(any(isnan(SAmeas)))
for j=1:NumFact,
SAm = SAmeas(j,:);
SAm = SAm(find(~isnan(SAm)));
rr=length(SAm);
AbsMu(j,1) = sum(abs(SAm),2)/rr;
if NumGroups == 0
Mu(j,1) = sum(SAm,2)/rr;
StDev(j,1) = sum((SAm - repmat(Mu(j),1,rr)).^2/(rr*(rr-1)),2).^0.5;
end
end
else
AbsMu = sum(abs(SAmeas),2)/r;
if NumGroups == 0
Mu = sum(SAmeas,2)/r;
StDev = sum((SAmeas - repmat(Mu,1,r)).^2/(r*(r-1)),2).^0.5;
end
end
% Define the output Matrix - if we have groups we cannot define the old
% measure mu, only mu* makes sense
if NumGroups > 0
OutMatrix = [OutMatrix; AbsMu];
else
OutMatrix = [OutMatrix; AbsMu, Mu, StDev];
end
end % For Each Output
function [SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p, Group)
% [SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p, Group)
%
% Given the Morris sample matrix, the output values and the group matrix compute the Morris measures
% -------------------------------------------------------------------------
% INPUTS
% -------------------------------------------------------------------------
% Group [NumFactor, NumGroups] := Matrix describing the groups.
% Each column represents one group.
% The element of each column are zero if the factor is not in the
% group. Otherwise it is 1.
% Sample := Matrix of the Morris sampled trajectories
% Output := Matrix of the output(s) values in correspondence of each point
% of each trajectory
% k = Number of factors
% -------------------------------------------------------------------------
% OUTPUTS
% OutMatrix (NumFactor*NumOutputs, 3)= [Mu*, Mu, StDev]
% for each output it gives the three measures of each factor
% -------------------------------------------------------------------------
if nargin==0,
disp(' ')
disp('[SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p, Group);')
return
end
OutMatrix=[];
if nargin < 5, Group=[]; end
NumGroups = size(Group,2);
if nargin < 4 | isempty(p),
p = 4;
end
Delt = p/(2*p-2);
if NumGroups ~ 0
sizea = NumGroups; % Number of groups
GroupMat=Group;
GroupMat = GroupMat';
else
sizea = NumFact;
end
r=size(Sample,1)/(sizea+1); % Number of trajectories
% For Each Output
for k=1:size(Output,2)
OutValues=Output(:,k);
% For each r trajectory
for i=1:r
% For each step j in the trajectory
% Read the orientation matrix fact for the r-th sampling
% Read the corresponding output values
Single_Sample = Sample(i+(i-1)*sizea:i+(i-1)*sizea+sizea,:);
Single_OutValues = OutValues(i+(i-1)*sizea:i+(i-1)*sizea+sizea,:);
A = (Single_Sample(2:sizea+1,:)-Single_Sample(1:sizea,:))';
Delta = A(find(A));
% For each point of the fixed trajectory compute the values of the Morris function. The function
% is partitioned in four parts, from order zero to order 4th.
for j=1:sizea % For each point in the trajectory i.e for each factor
% matrix of factor which changes
if NumGroups ~ 0;
AuxFind (:,1) = A(:,j);
% AuxFind(find(A(:,j)),1)=1;
% Pippo = sum((Group - repmat(AuxFind,1,NumGroups)),1);
% Change_factor(j,i) = find(Pippo==0);
Change_factor = find(abs(AuxFind)>1e-010);
% If we deal with groups we can only estimate the new mu*
% measure since factors in the same groups can move in
% opposite direction and the definition of the standard
% Morris mu cannopt be applied.
% In the new version the elementary effect is defined with
% the absolute value.
%SAmeas(find(GroupMat(Change_factor(j,i),:)),i) = abs((Single_OutValues(j) - Single_OutValues(j+1) )/Delt); %(2/3));
SAmeas(i,Change_factor') = abs((Single_OutValues(j) - Single_OutValues(j+1) )/Delt);
else
Change_factor(j,i) = find(Single_Sample(j+1,:)-Single_Sample(j,:));
% If no groups --> we compute both the original and
% modified measure
if Delta(j) > 0 %=> +Delta
SAmeas(Change_factor(j,i),i) = (Single_OutValues(j+1) - Single_OutValues(j) )/Delt; %(2/3);
else %=> -Delta
SAmeas(Change_factor(j,i),i) = (Single_OutValues(j) - Single_OutValues(j+1) )/Delt; %(2/3);
end
end
end %for j=1:sizea
end %for i=1:r
if NumGroups ~ 0
SAmeas = SAmeas';
end
% Compute Mu AbsMu and StDev
if any(any(isnan(SAmeas)))
for j=1:NumFact,
SAm = SAmeas(j,:);
SAm = SAm(find(~isnan(SAm)));
rr=length(SAm);
AbsMu(j,1) = sum(abs(SAm),2)/rr;
if NumGroups == 0
Mu(j,1) = sum(SAm,2)/rr;
StDev(j,1) = sum((SAm - repmat(Mu(j),1,rr)).^2/(rr*(rr-1)),2).^0.5;
end
end
else
AbsMu = sum(abs(SAmeas),2)/r;
if NumGroups == 0
Mu = sum(SAmeas,2)/r;
StDev = sum((SAmeas - repmat(Mu,1,r)).^2/(r*(r-1)),2).^0.5;
end
end
% Define the output Matrix - if we have groups we cannot define the old
% measure mu, only mu* makes sense
if NumGroups > 0
OutMatrix = [OutMatrix; AbsMu];
else
OutMatrix = [OutMatrix; AbsMu, Mu, StDev];
end
end % For Each Output

346
matlab/gsa/Sampling_Function_2.m
View File

@ -1,174 +1,174 @@
function [Outmatrix, OutFact] = Sampling_Function_2(p, k, r, UB, LB, GroupMat)
%[Outmatrix, OutFact] = Sampling_Function_2(p, k, r, UB, LB, GroupMat)
% Inputs: k (1,1) := number of factors examined or number of groups examined.
% In case the groups are chosen the number of factors is stores in NumFact and
% sizea becomes the number of created groups.
% NumFact (1,1) := number of factors examined in the case when groups are chosen
% r (1,1) := sample size
% p (1,1) := number of intervals considered in [0, 1]
% UB(sizea,1) := Upper Bound for each factor
% LB(sizea,1) := Lower Bound for each factor
% GroupNumber(1,1) := Number of groups (eventually 0)
% GroupMat(NumFact,GroupNumber):= Matrix which describes the chosen groups. Each column represents a group and its elements
% are set to 1 in correspondence of the factors that belong to the fixed group. All
% the other elements are zero.
% Local Variables:
% sizeb (1,1) := sizea+1
% sizec (1,1) := 1
% randmult (sizea,1) := vector of random +1 and -1
% perm_e(1,sizea) := vector of sizea random permutated indeces
% fact(sizea) := vector containing the factor varied within each traj
% DDo(sizea,sizea) := D* in Morris, 1991
% A(sizeb,sizea) := Jk+1,k in Morris, 1991
% B(sizeb,sizea) := B in Morris, 1991
% Po(sizea,sizea) := P* in Morris, 1991
% Bo(sizeb,sizea) := B* in Morris, 1991
% Ao(sizeb,sizec) := Jk+1,1 in Morris, 1991
% xo(sizec,sizea) := x* in Morris, 1991 (starting point for the trajectory)
% In(sizeb,sizea) := for each loop orientation matrix. It corresponds to a trajectory
% of k step in the parameter space and it provides a single elementary
% effect per factor
% MyInt()
% Fact(sizea,1) := for each loop vector indicating which factor or group of factors has been changed
% in each step of the trajectory
% AuxMat(sizeb,sizea) := Delta*0.5*((2*B - A) * DD0 + A) in Morris, 1991. The AuxMat is used as in Morris design
% for single factor analysis, while it constitutes an intermediate step for the group analysis.
%
% Output: Outmatrix(sizeb*r, sizea) := for the entire sample size computed In(i,j) matrices
% OutFact(sizea*r,1) := for the entire sample size computed Fact(i,1) vectors
%
% Note: B0 is constructed as in Morris design when groups are not considered. When groups are considered the routine
% follows the following steps:
% 1- Creation of P0 and DD0 matrices defined in Morris for the groups. This means that the dimensions of these
% 2 matrices are (GroupNumber,GroupNumber).
% 2- Creation of AuxMat matrix with (GroupNumber+1,GroupNumber) elements.
% 3- Definition of GroupB0 starting from AuxMat, GroupMat and P0.
% 4- The final B0 for groups is obtained as [ones(sizeb,1)*x0' + GroupB0]. The P0 permutation is present in GroupB0
% and it's not necessary to permute the matrix (ones(sizeb,1)*x0') because it's already randomly created.
% Reference:
% A. Saltelli, K. Chan, E.M. Scott, "Sensitivity Analysis" on page 68 ss
%
% F. Campolongo, J. Cariboni, JRC - IPSC Ispra, Varese, IT
% Last Update: 15 November 2005 by J.Cariboni
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Parameters and initialisation of the output matrix
sizea = k;
Delta = p/(2*p-2);
%Delta = 1/3
NumFact = sizea;
GroupNumber = size(GroupMat,2);
if GroupNumber ~ 0;
sizea = size(GroupMat,2);
end
sizeb = sizea + 1;
sizec = 1;
Outmatrix = [];
OutFact = [];
% For each i generate a trajectory
for i=1:r
% Construct DD0 - OLD VERSION - it does not need communication toolbox
% RAND(N,M) is an NXM matrix with random entries, chosen from a uniform distribution on the interval (0.0,1.0).
% Note that DD0 tells if the factor have to be increased or ddecreased
% by Delta.
randmult = ones(k,1);
v = rand(k,1);
randmult (find(v < 0.5))=-1;
randmult = repmat(randmult,1,k);
DD0 = randmult .* eye(k);
% Construct DD0 - NEW VERSION - it needs communication toolbox
% randsrc(m) generates an m-by-m matrix, each of whose entries independently takes the value -1 with probability 1/2,
% and 1 with probability 1/2.
% DD0 = randsrc(NumFact) .* eye(NumFact);
% Construct B (lower triangular)
B = ones(sizeb,sizea);
for j = 1:sizea
B(1:j,j)=0;
end
% Construct A0, A
A0 = ones(sizeb,1);
A = ones(sizeb,NumFact);
% Construct the permutation matrix P0. In each column of P0 one randomly chosen element equals 1
% while all the others equal zero.
% P0 tells the order in which order factors are changed in each
% trajectory. P0 is created as it follows:
% 1) All the elements of P0 are set equal to zero ==> P0 = zeros (sizea, sizea);
% 2) The function randperm create a random permutation of integer 1:sizea, without repetitions ==> perm_e;
% 3) In each column of P0 the element indicated in perm_e is set equal to one.
% Note that P0 is then used reading it by rows.
P0 = zeros (sizea, sizea);
perm_e = randperm(sizea); % RANDPERM(n) is a random permutation of the integers from 1 to n.
for j = 1:sizea
P0(perm_e(j),j) = 1;
end
% When groups are present the random permutation is done only on B. The effect is the same since
% the added part (A0*x0') is completely random.
if GroupNumber ~ 0
B = B * (GroupMat*P0')';
end
% Compute AuxMat both for single factors and groups analysis. For Single factors analysis
% AuxMat is added to (A0*X0) and then permutated through P0. When groups are active AuxMat is
% used to build GroupB0. AuxMat is created considering DD0. If the element on DD0 diagonal
% is 1 then AuxMat will start with zero and add Delta. If the element on DD0 diagonal is -1
% then DD0 will start Delta and goes to zero.
AuxMat = Delta*0.5*((2*B - A) * DD0 + A);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% a --> Define the random vector x0 for the factors. Note that x0 takes value in the hypercube
% [0,...,1-Delta]*[0,...,1-Delta]*[0,...,1-Delta]*[0,...,1-Delta]
MyInt = repmat([0:(1/(p-1)):(1-Delta)],NumFact,1); % Construct all possible values of the factors
% OLD VERSION - it needs communication toolbox
% w = randint(NumFact,1,[1,size(MyInt,2)]);
% NEW VERSION - construct a version of random integers
% 1) create a vector of random integers
% 2) divide [0,1] into the needed steps
% 3) check in which interval the random numbers fall
% 4) generate the corresponding integer
v = repmat(rand(NumFact,1),1,size(MyInt,2)+1); % 1)
IntUsed = repmat([0:1/size(MyInt,2):1],NumFact,1); % 2)
DiffAuxVec = IntUsed - v; % 3)
for ii = 1:size(DiffAuxVec,1)
w(1,ii) = max(find(DiffAuxVec(ii,:)<0)); % 4)
end
x0 = MyInt(1,w)'; % Define x0
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% b --> Compute the matrix B*, here indicated as B0. Each row in B0 is a
% trajectory for Morris Calculations. The dimension of B0 is (Numfactors+1,Numfactors)
if GroupNumber ~ 0
B0 = (A0*x0' + AuxMat);
else
B0 = (A0*x0' + AuxMat)*P0;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% c --> Compute values in the original intervals
% B0 has values x(i,j) in [0, 1/(p -1), 2/(p -1), ... , 1].
% To obtain values in the original intervals [LB, UB] we compute
% LB(j) + x(i,j)*(UB(j)-LB(j))
In = repmat(LB,1,sizeb)' + B0 .* repmat((UB-LB),1,sizeb)';
% Create the Factor vector. Each component of this vector indicate which factor or group of factor
% has been changed in each step of the trajectory.
for j=1:sizea
Fact(1,j) = find(P0(j,:));
end
Fact(1,sizea+1) = 0;
Outmatrix = [Outmatrix; In];
OutFact = [OutFact; Fact'];
function [Outmatrix, OutFact] = Sampling_Function_2(p, k, r, UB, LB, GroupMat)
%[Outmatrix, OutFact] = Sampling_Function_2(p, k, r, UB, LB, GroupMat)
% Inputs: k (1,1) := number of factors examined or number of groups examined.
% In case the groups are chosen the number of factors is stores in NumFact and
% sizea becomes the number of created groups.
% NumFact (1,1) := number of factors examined in the case when groups are chosen
% r (1,1) := sample size
% p (1,1) := number of intervals considered in [0, 1]
% UB(sizea,1) := Upper Bound for each factor
% LB(sizea,1) := Lower Bound for each factor
% GroupNumber(1,1) := Number of groups (eventually 0)
% GroupMat(NumFact,GroupNumber):= Matrix which describes the chosen groups. Each column represents a group and its elements
% are set to 1 in correspondence of the factors that belong to the fixed group. All
% the other elements are zero.
% Local Variables:
% sizeb (1,1) := sizea+1
% sizec (1,1) := 1
% randmult (sizea,1) := vector of random +1 and -1
% perm_e(1,sizea) := vector of sizea random permutated indeces
% fact(sizea) := vector containing the factor varied within each traj
% DDo(sizea,sizea) := D* in Morris, 1991
% A(sizeb,sizea) := Jk+1,k in Morris, 1991
% B(sizeb,sizea) := B in Morris, 1991
% Po(sizea,sizea) := P* in Morris, 1991
% Bo(sizeb,sizea) := B* in Morris, 1991
% Ao(sizeb,sizec) := Jk+1,1 in Morris, 1991
% xo(sizec,sizea) := x* in Morris, 1991 (starting point for the trajectory)
% In(sizeb,sizea) := for each loop orientation matrix. It corresponds to a trajectory
% of k step in the parameter space and it provides a single elementary
% effect per factor
% MyInt()
% Fact(sizea,1) := for each loop vector indicating which factor or group of factors has been changed
% in each step of the trajectory
% AuxMat(sizeb,sizea) := Delta*0.5*((2*B - A) * DD0 + A) in Morris, 1991. The AuxMat is used as in Morris design
% for single factor analysis, while it constitutes an intermediate step for the group analysis.
%
% Output: Outmatrix(sizeb*r, sizea) := for the entire sample size computed In(i,j) matrices
% OutFact(sizea*r,1) := for the entire sample size computed Fact(i,1) vectors
%
% Note: B0 is constructed as in Morris design when groups are not considered. When groups are considered the routine
% follows the following steps:
% 1- Creation of P0 and DD0 matrices defined in Morris for the groups. This means that the dimensions of these
% 2 matrices are (GroupNumber,GroupNumber).
% 2- Creation of AuxMat matrix with (GroupNumber+1,GroupNumber) elements.
% 3- Definition of GroupB0 starting from AuxMat, GroupMat and P0.
% 4- The final B0 for groups is obtained as [ones(sizeb,1)*x0' + GroupB0]. The P0 permutation is present in GroupB0
% and it's not necessary to permute the matrix (ones(sizeb,1)*x0') because it's already randomly created.
% Reference:
% A. Saltelli, K. Chan, E.M. Scott, "Sensitivity Analysis" on page 68 ss
%
% F. Campolongo, J. Cariboni, JRC - IPSC Ispra, Varese, IT
% Last Update: 15 November 2005 by J.Cariboni
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Parameters and initialisation of the output matrix
sizea = k;
Delta = p/(2*p-2);
%Delta = 1/3
NumFact = sizea;
GroupNumber = size(GroupMat,2);
if GroupNumber ~ 0;
sizea = size(GroupMat,2);
end
sizeb = sizea + 1;
sizec = 1;
Outmatrix = [];
OutFact = [];
% For each i generate a trajectory
for i=1:r
% Construct DD0 - OLD VERSION - it does not need communication toolbox
% RAND(N,M) is an NXM matrix with random entries, chosen from a uniform distribution on the interval (0.0,1.0).
% Note that DD0 tells if the factor have to be increased or ddecreased
% by Delta.
randmult = ones(k,1);
v = rand(k,1);
randmult (find(v < 0.5))=-1;
randmult = repmat(randmult,1,k);
DD0 = randmult .* eye(k);
% Construct DD0 - NEW VERSION - it needs communication toolbox
% randsrc(m) generates an m-by-m matrix, each of whose entries independently takes the value -1 with probability 1/2,
% and 1 with probability 1/2.
% DD0 = randsrc(NumFact) .* eye(NumFact);
% Construct B (lower triangular)
B = ones(sizeb,sizea);
for j = 1:sizea
B(1:j,j)=0;
end
% Construct A0, A
A0 = ones(sizeb,1);
A = ones(sizeb,NumFact);
% Construct the permutation matrix P0. In each column of P0 one randomly chosen element equals 1
% while all the others equal zero.
% P0 tells the order in which order factors are changed in each
% trajectory. P0 is created as it follows:
% 1) All the elements of P0 are set equal to zero ==> P0 = zeros (sizea, sizea);
% 2) The function randperm create a random permutation of integer 1:sizea, without repetitions ==> perm_e;
% 3) In each column of P0 the element indicated in perm_e is set equal to one.
% Note that P0 is then used reading it by rows.
P0 = zeros (sizea, sizea);
perm_e = randperm(sizea); % RANDPERM(n) is a random permutation of the integers from 1 to n.
for j = 1:sizea
P0(perm_e(j),j) = 1;
end
% When groups are present the random permutation is done only on B. The effect is the same since
% the added part (A0*x0') is completely random.
if GroupNumber ~ 0
B = B * (GroupMat*P0')';
end
% Compute AuxMat both for single factors and groups analysis. For Single factors analysis
% AuxMat is added to (A0*X0) and then permutated through P0. When groups are active AuxMat is
% used to build GroupB0. AuxMat is created considering DD0. If the element on DD0 diagonal
% is 1 then AuxMat will start with zero and add Delta. If the element on DD0 diagonal is -1
% then DD0 will start Delta and goes to zero.
AuxMat = Delta*0.5*((2*B - A) * DD0 + A);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% a --> Define the random vector x0 for the factors. Note that x0 takes value in the hypercube
% [0,...,1-Delta]*[0,...,1-Delta]*[0,...,1-Delta]*[0,...,1-Delta]
MyInt = repmat([0:(1/(p-1)):(1-Delta)],NumFact,1); % Construct all possible values of the factors
% OLD VERSION - it needs communication toolbox
% w = randint(NumFact,1,[1,size(MyInt,2)]);
% NEW VERSION - construct a version of random integers
% 1) create a vector of random integers
% 2) divide [0,1] into the needed steps
% 3) check in which interval the random numbers fall
% 4) generate the corresponding integer
v = repmat(rand(NumFact,1),1,size(MyInt,2)+1); % 1)
IntUsed = repmat([0:1/size(MyInt,2):1],NumFact,1); % 2)
DiffAuxVec = IntUsed - v; % 3)
for ii = 1:size(DiffAuxVec,1)
w(1,ii) = max(find(DiffAuxVec(ii,:)<0)); % 4)
end
x0 = MyInt(1,w)'; % Define x0
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% b --> Compute the matrix B*, here indicated as B0. Each row in B0 is a
% trajectory for Morris Calculations. The dimension of B0 is (Numfactors+1,Numfactors)
if GroupNumber ~ 0
B0 = (A0*x0' + AuxMat);
else
B0 = (A0*x0' + AuxMat)*P0;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% c --> Compute values in the original intervals
% B0 has values x(i,j) in [0, 1/(p -1), 2/(p -1), ... , 1].
% To obtain values in the original intervals [LB, UB] we compute
% LB(j) + x(i,j)*(UB(j)-LB(j))
In = repmat(LB,1,sizeb)' + B0 .* repmat((UB-LB),1,sizeb)';
% Create the Factor vector. Each component of this vector indicate which factor or group of factor
% has been changed in each step of the trajectory.
for j=1:sizea
Fact(1,j) = find(P0(j,:));
end
Fact(1,sizea+1) = 0;
Outmatrix = [Outmatrix; In];
OutFact = [OutFact; Fact'];
end

28
matlab/gsa/cumplot.m
View File

@ -1,14 +1,14 @@
function h = cumplot(x);
%function h =cumplot(x)
% Copyright (C) 2005 Marco Ratto
n=length(x);
x=[-inf; sort(x); Inf];
y=[0:n n]./n;
h0 = stairs(x,y);
grid on,
if nargout,
h=h0;
end
function h = cumplot(x);
%function h =cumplot(x)
% Copyright (C) 2005 Marco Ratto
n=length(x);
x=[-inf; sort(x); Inf];
y=[0:n n]./n;
h0 = stairs(x,y);
grid on,
if nargout,
h=h0;
end

58
matlab/gsa/dat_fil_.m
View File

@ -1,30 +1,30 @@
function c = dat_fil_(data_file);
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
try
eval(data_file);
catch
load(data_file);
end
clear data_file;
a=who;
for j=1:length(a)
eval(['c.',a{j},'=',a{j},';']);
function c = dat_fil_(data_file);
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
try
eval(data_file);
catch
load(data_file);
end
clear data_file;
a=who;
for j=1:length(a)
eval(['c.',a{j},'=',a{j},';']);
end

1452
matlab/gsa/filt_mc_.m
View File

File diff suppressed because it is too large Load Diff

108
matlab/gsa/ghx2transition.m
View File

@ -1,54 +1,54 @@
function [A,B] = ghx2transition(mm,iv,ic,aux)
% [A,B] = ghx2transition(mm,iv,ic,aux)
%
% Adapted by M. Ratto from kalman_transition_matrix.m
% (kalman_transition_matrix.m is part of DYNARE, copyright M. Juillard)
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
global oo_ M_
[nr1, nc1] = size(mm);
ghx = mm(:, [1:(nc1-M_.exo_nbr)]);
ghu = mm(:, [(nc1-M_.exo_nbr+1):end] );
if nargin == 1
oo_.dr.ghx = ghx;
oo_.dr.ghu = ghu;
endo_nbr = M_.endo_nbr;
nstatic = oo_.dr.nstatic;
npred = oo_.dr.npred;
iv = (1:endo_nbr)';
ic = [ nstatic+(1:npred) endo_nbr+(1:size(oo_.dr.ghx,2)-npred) ]';
aux = oo_.dr.transition_auxiliary_variables;
k = find(aux(:,2) > npred);
aux(:,2) = aux(:,2) + nstatic;
aux(k,2) = aux(k,2) + oo_.dr.nfwrd;
end
n_iv = length(iv);
n_ir1 = size(aux,1);
nr = n_iv + n_ir1;
A = zeros(nr,nr);
B = zeros(nr,M_.exo_nbr);
i_n_iv = 1:n_iv;
A(i_n_iv,ic) = ghx(iv,:);
if n_ir1 > 0
A(n_iv+1:end,:) = sparse(aux(:,1),aux(:,2),ones(n_ir1,1),n_ir1,nr);
end
B(i_n_iv,:) = ghu(iv,:);
function [A,B] = ghx2transition(mm,iv,ic,aux)
% [A,B] = ghx2transition(mm,iv,ic,aux)
%
% Adapted by M. Ratto from kalman_transition_matrix.m
% (kalman_transition_matrix.m is part of DYNARE, copyright M. Juillard)
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
global oo_ M_
[nr1, nc1] = size(mm);
ghx = mm(:, [1:(nc1-M_.exo_nbr)]);
ghu = mm(:, [(nc1-M_.exo_nbr+1):end] );
if nargin == 1
oo_.dr.ghx = ghx;
oo_.dr.ghu = ghu;
endo_nbr = M_.endo_nbr;
nstatic = oo_.dr.nstatic;
npred = oo_.dr.npred;
iv = (1:endo_nbr)';
ic = [ nstatic+(1:npred) endo_nbr+(1:size(oo_.dr.ghx,2)-npred) ]';
aux = oo_.dr.transition_auxiliary_variables;
k = find(aux(:,2) > npred);
aux(:,2) = aux(:,2) + nstatic;
aux(k,2) = aux(k,2) + oo_.dr.nfwrd;
end
n_iv = length(iv);
n_ir1 = size(aux,1);
nr = n_iv + n_ir1;
A = zeros(nr,nr);
B = zeros(nr,M_.exo_nbr);
i_n_iv = 1:n_iv;
A(i_n_iv,ic) = ghx(iv,:);
if n_ir1 > 0
A(n_iv+1:end,:) = sparse(aux(:,1),aux(:,2),ones(n_ir1,1),n_ir1,nr);
end
B(i_n_iv,:) = ghu(iv,:);

188
matlab/gsa/gsa_plotmatrix.m
View File

@ -1,94 +1,94 @@
function gsa_plotmatrix(type,varargin)
% function gsa_plotmatrix(type,varargin)
% extended version of the standard MATLAB plotmatrix
% Copyright (C) 2011-2011 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global bayestopt_ options_ M_
RootDirectoryName = CheckPath('gsa');
if options_.opt_gsa.pprior
load([ RootDirectoryName filesep M_.fname '_prior.mat'],'lpmat0','lpmat','istable','iunstable','iindeterm','iwrong')
else
load([ RootDirectoryName filesep M_.fname '_mc.mat'],'lpmat0','lpmat','istable','iunstable','iindeterm','iwrong')
eval(['load ' options_.mode_file ' xparam1;']');
end
iexplosive = iunstable(~ismember(iunstable,[iindeterm;iwrong]));
switch type
case 'all'
x=[lpmat0 lpmat];
NumberOfDraws=size(x,1);
B=NumberOfDraws;
case 'stable'
x=[lpmat0(istable,:) lpmat(istable,:)];
NumberOfDraws=size(x,1);
B=NumberOfDraws;
case 'nosolution'
x=[lpmat0(iunstable,:) lpmat(iunstable,:)];
NumberOfDraws=size(x,1);
B=NumberOfDraws;
case 'unstable'
x=[lpmat0(iexplosive,:) lpmat(iexplosive,:)];
NumberOfDraws=size(x,1);
B=NumberOfDraws;
case 'indeterm'
x=[lpmat0(iindeterm,:) lpmat(iindeterm,:)];
NumberOfDraws=size(x,1);
B=NumberOfDraws;
case 'wrong'
x=[lpmat0(iwrong,:) lpmat(iwrong,:)];
NumberOfDraws=size(x,1);
B=NumberOfDraws;
end
if isempty(x),
disp('Empty parameter set!')
return
end
for j=1:length(varargin),
jcol(j)=strmatch(varargin{j},bayestopt_.name,'exact');
end
[H,AX,BigA,P,PAx]=plotmatrix(x(:,jcol));
for j=1:length(varargin),
% axes(AX(1,j)), title(varargin{j})
% axes(AX(j,1)), ylabel(varargin{j})
% set(AX(1,j),'title',varargin{j}),
set(get(AX(j,1),'ylabel'),'string',varargin{j})
set(get(AX(end,j),'xlabel'),'string',varargin{j})
end
if options_.opt_gsa.pprior==0,
xparam1=xparam1(jcol);
for j=1:length(varargin),
for i=1:j-1,
axes(AX(j,i)),
hold on, plot(xparam1(i),xparam1(j),'*r')
end
for i=j+1:length(varargin),
axes(AX(j,i)),
hold on, plot(xparam1(i),xparam1(j),'*r')
end
end
end
function gsa_plotmatrix(type,varargin)
% function gsa_plotmatrix(type,varargin)
% extended version of the standard MATLAB plotmatrix
% Copyright (C) 2011-2011 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global bayestopt_ options_ M_
RootDirectoryName = CheckPath('gsa');
if options_.opt_gsa.pprior
load([ RootDirectoryName filesep M_.fname '_prior.mat'],'lpmat0','lpmat','istable','iunstable','iindeterm','iwrong')
else
load([ RootDirectoryName filesep M_.fname '_mc.mat'],'lpmat0','lpmat','istable','iunstable','iindeterm','iwrong')
eval(['load ' options_.mode_file ' xparam1;']');
end
iexplosive = iunstable(~ismember(iunstable,[iindeterm;iwrong]));
switch type
case 'all'
x=[lpmat0 lpmat];
NumberOfDraws=size(x,1);
B=NumberOfDraws;
case 'stable'
x=[lpmat0(istable,:) lpmat(istable,:)];
NumberOfDraws=size(x,1);
B=NumberOfDraws;
case 'nosolution'
x=[lpmat0(iunstable,:) lpmat(iunstable,:)];
NumberOfDraws=size(x,1);
B=NumberOfDraws;
case 'unstable'
x=[lpmat0(iexplosive,:) lpmat(iexplosive,:)];
NumberOfDraws=size(x,1);
B=NumberOfDraws;
case 'indeterm'
x=[lpmat0(iindeterm,:) lpmat(iindeterm,:)];
NumberOfDraws=size(x,1);
B=NumberOfDraws;
case 'wrong'
x=[lpmat0(iwrong,:) lpmat(iwrong,:)];
NumberOfDraws=size(x,1);
B=NumberOfDraws;
end
if isempty(x),
disp('Empty parameter set!')
return
end
for j=1:length(varargin),
jcol(j)=strmatch(varargin{j},bayestopt_.name,'exact');
end
[H,AX,BigA,P,PAx]=plotmatrix(x(:,jcol));
for j=1:length(varargin),
% axes(AX(1,j)), title(varargin{j})
% axes(AX(j,1)), ylabel(varargin{j})
% set(AX(1,j),'title',varargin{j}),
set(get(AX(j,1),'ylabel'),'string',varargin{j})
set(get(AX(end,j),'xlabel'),'string',varargin{j})
end
if options_.opt_gsa.pprior==0,
xparam1=xparam1(jcol);
for j=1:length(varargin),
for i=1:j-1,
axes(AX(j,i)),
hold on, plot(xparam1(i),xparam1(j),'*r')
end
for i=j+1:length(varargin),
axes(AX(j,i)),
hold on, plot(xparam1(i),xparam1(j),'*r')
end
end
end

108
matlab/gsa/log_trans_.m
View File

@ -1,54 +1,54 @@
function [yy, xdir, isig, lam]=log_trans_(y0,xdir0)
if nargin==1,
xdir0='';
end
f=inline('skewness(log(y+lam))','lam','y');
isig=1;
if ~(max(y0)<0 | min(y0)>0)
if skewness(y0)<0,
isig=-1;
y0=-y0;
end
n=hist(y0,10);
if n(1)>20*n(end),
try lam=fzero(f,[-min(y0)+10*eps -min(y0)+abs(median(y0))],[],y0);
catch
yl(1)=f(-min(y0)+10*eps,y0);
yl(2)=f(-min(y0)+abs(median(y0)),y0);
if abs(yl(1))<abs(yl(2))
lam=-min(y0)+eps;
else
lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
end
end
yy = log(y0+lam);
xdir=[xdir0,'_logskew'];
else
isig=0;
lam=0;
yy = log(y0.^2);
xdir=[xdir0,'_logsquared'];
end
else
if max(y0)<0
isig=-1;
y0=-y0;
%yy=log(-y0);
xdir=[xdir0,'_minuslog'];
elseif min(y0)>0
%yy=log(y0);
xdir=[xdir0,'_log'];
end
try lam=fzero(f,[-min(y0)+10*eps -min(y0)+median(y0)],[],y0);
catch
yl(1)=f(-min(y0)+10*eps,y0);
yl(2)=f(-min(y0)+abs(median(y0)),y0);
if abs(yl(1))<abs(yl(2))
lam=-min(y0)+eps;
else
lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
end
end
yy = log(y0+lam);
end
function [yy, xdir, isig, lam]=log_trans_(y0,xdir0)
if nargin==1,
xdir0='';
end
f=inline('skewness(log(y+lam))','lam','y');
isig=1;
if ~(max(y0)<0 | min(y0)>0)
if skewness(y0)<0,
isig=-1;
y0=-y0;
end
n=hist(y0,10);
if n(1)>20*n(end),
try lam=fzero(f,[-min(y0)+10*eps -min(y0)+abs(median(y0))],[],y0);
catch
yl(1)=f(-min(y0)+10*eps,y0);
yl(2)=f(-min(y0)+abs(median(y0)),y0);
if abs(yl(1))<abs(yl(2))
lam=-min(y0)+eps;
else
lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
end
end
yy = log(y0+lam);
xdir=[xdir0,'_logskew'];
else
isig=0;
lam=0;
yy = log(y0.^2);
xdir=[xdir0,'_logsquared'];
end
else
if max(y0)<0
isig=-1;
y0=-y0;
%yy=log(-y0);
xdir=[xdir0,'_minuslog'];
elseif min(y0)>0
%yy=log(y0);
xdir=[xdir0,'_log'];
end
try lam=fzero(f,[-min(y0)+10*eps -min(y0)+median(y0)],[],y0);
catch
yl(1)=f(-min(y0)+10*eps,y0);
yl(2)=f(-min(y0)+abs(median(y0)),y0);
if abs(yl(1))<abs(yl(2))
lam=-min(y0)+eps;
else
lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
end
end
yy = log(y0+lam);
end

50
matlab/gsa/lptauSEQ.m
View File

@ -1,25 +1,25 @@
function [lpmat] = lptauSEQ(Nsam,Nvar)
% [lpmat] = lptauSEQ(Nsam,Nvar)
%
% function call LPTAU and generates a sample of dimension Nsam for a
% number of parameters Nvar
%
% Copyright (C) 2005 Marco Ratto
% THIS PROGRAM WAS WRITTEN FOR MATLAB BY
% Marco Ratto,
% Unit of Econometrics and Statistics AF
% (http://www.jrc.cec.eu.int/uasa/),
% IPSC, Joint Research Centre
% The European Commission,
% TP 361, 21020 ISPRA(VA), ITALY
% marco.ratto@jrc.it
%
clear lptau
lpmat = zeros(Nsam, Nvar);
for j=1:Nsam,
lpmat(j,:)=LPTAU(j,Nvar);
end
return
function [lpmat] = lptauSEQ(Nsam,Nvar)
% [lpmat] = lptauSEQ(Nsam,Nvar)
%
% function call LPTAU and generates a sample of dimension Nsam for a
% number of parameters Nvar
%
% Copyright (C) 2005 Marco Ratto
% THIS PROGRAM WAS WRITTEN FOR MATLAB BY
% Marco Ratto,
% Unit of Econometrics and Statistics AF
% (http://www.jrc.cec.eu.int/uasa/),
% IPSC, Joint Research Centre
% The European Commission,
% TP 361, 21020 ISPRA(VA), ITALY
% marco.ratto@jrc.it
%
clear lptau
lpmat = zeros(Nsam, Nvar);
for j=1:Nsam,
lpmat(j,:)=LPTAU(j,Nvar);
end
return

3054
matlab/gsa/map_ident_.m
View File

File diff suppressed because it is too large Load Diff

50
matlab/gsa/mc_moments.m
View File

@ -1,25 +1,25 @@
function [vdec, cc, ac] = mc_moments(mm, ss, dr)
global options_ M_
[nr1, nc1, nsam] = size(mm);
disp('Computing theoretical moments ...')
h = waitbar(0,'Theoretical moments ...');
for j=1:nsam,
dr.ghx = mm(:, [1:(nc1-M_.exo_nbr)],j);
dr.ghu = mm(:, [(nc1-M_.exo_nbr+1):end], j);
if ~isempty(ss),
set_shocks_param(ss(j,:));
end
[vdec(:,:,j), corr, autocorr, z, zz] = th_moments(dr,options_.varobs);
cc(:,:,j)=triu(corr);
dum=[];
for i=1:options_.ar
dum=[dum, autocorr{i}];
end
ac(:,:,j)=dum;
waitbar(j/nsam,h)
end
close(h)
disp(' ')
disp('... done !')
function [vdec, cc, ac] = mc_moments(mm, ss, dr)
global options_ M_
[nr1, nc1, nsam] = size(mm);
disp('Computing theoretical moments ...')
h = waitbar(0,'Theoretical moments ...');
for j=1:nsam,
dr.ghx = mm(:, [1:(nc1-M_.exo_nbr)],j);
dr.ghu = mm(:, [(nc1-M_.exo_nbr+1):end], j);
if ~isempty(ss),
set_shocks_param(ss(j,:));
end
[vdec(:,:,j), corr, autocorr, z, zz] = th_moments(dr,options_.varobs);
cc(:,:,j)=triu(corr);
dum=[];
for i=1:options_.ar
dum=[dum, autocorr{i}];
end
ac(:,:,j)=dum;
waitbar(j/nsam,h)
end
close(h)
disp(' ')
disp('... done !')

314
matlab/gsa/myboxplot.m
View File

@ -1,158 +1,158 @@
function sout = myboxplot (data,notched,symbol,vertical,maxwhisker)
% sout = myboxplot (data,notched,symbol,vertical,maxwhisker)
% % % % endif
if nargin < 5 | isempty(maxwhisker), maxwhisker = 1.5; end
if nargin < 4 | isempty(vertical), vertical = 1; end
if nargin < 3 | isempty(symbol), symbol = ['+','o']; end
if nargin < 2 | isempty(notched), notched = 0; end
if length(symbol)==1, symbol(2)=symbol(1); end
if notched==1, notched=0.25; end
a=1-notched;
% ## figure out how many data sets we have
if iscell(data),
nc = length(data);
else
% if isvector(data), data = data(:); end
nc = size(data,2);
end
% ## compute statistics
% ## s will contain
% ## 1,5 min and max
% ## 2,3,4 1st, 2nd and 3rd quartile
% ## 6,7 lower and upper confidence intervals for median
s = zeros(7,nc);
box = zeros(1,nc);
whisker_x = ones(2,1)*[1:nc,1:nc];
whisker_y = zeros(2,2*nc);
outliers_x = [];
outliers_y = [];
outliers2_x = [];
outliers2_y = [];
for i=1:nc
% ## Get the next data set from the array or cell array
if iscell(data)
col = data{i}(:);
else
col = data(:,i);
end
% ## Skip missing data
% % % % % % % col(isnan(col) | isna (col)) = [];
col(isnan(col)) = [];
% ## Remember the data length
nd = length(col);
box(i) = nd;
if (nd > 1)
% ## min,max and quartiles
% s(1:5,i) = statistics(col)(1:5);
s(1,i)=min(col);
s(5,i)=max(col);
s(2,i)=myprctilecol(col,25);
s(3,i)=myprctilecol(col,50);
s(4,i)=myprctilecol(col,75);
% ## confidence interval for the median
est = 1.57*(s(4,i)-s(2,i))/sqrt(nd);
s(6,i) = max([s(3,i)-est, s(2,i)]);
s(7,i) = min([s(3,i)+est, s(4,i)]);
% ## whiskers out to the last point within the desired inter-quartile range
IQR = maxwhisker*(s(4,i)-s(2,i));
whisker_y(:,i) = [min(col(col >= s(2,i)-IQR)); s(2,i)];
whisker_y(:,nc+i) = [max(col(col <= s(4,i)+IQR)); s(4,i)];
% ## outliers beyond 1 and 2 inter-quartile ranges
outliers = col((col < s(2,i)-IQR & col >= s(2,i)-2*IQR) | (col > s(4,i)+IQR & col <= s(4,i)+2*IQR));
outliers2 = col(col < s(2,i)-2*IQR | col > s(4,i)+2*IQR);
outliers_x = [outliers_x; i*ones(size(outliers))];
outliers_y = [outliers_y; outliers];
outliers2_x = [outliers2_x; i*ones(size(outliers2))];
outliers2_y = [outliers2_y; outliers2];
elseif (nd == 1)
% ## all statistics collapse to the value of the point
s(:,i) = col;
% ## single point data sets are plotted as outliers.
outliers_x = [outliers_x; i];
outliers_y = [outliers_y; col];
else
% ## no statistics if no points
s(:,i) = NaN;
end
end
% % % % if isempty(outliers2_y)
% % % % outliers2_y=
% ## Note which boxes don't have enough stats
chop = find(box <= 1);
% ## Draw a box around the quartiles, with width proportional to the number of
% ## items in the box. Draw notches if desired.
box = box*0.23/max(box);
quartile_x = ones(11,1)*[1:nc] + [-a;-1;-1;1;1;a;1;1;-1;-1;-a]*box;
quartile_y = s([3,7,4,4,7,3,6,2,2,6,3],:);
% ## Draw a line through the median
median_x = ones(2,1)*[1:nc] + [-a;+a]*box;
% median_x=median(col);
median_y = s([3,3],:);
% ## Chop all boxes which don't have enough stats
quartile_x(:,chop) = [];
quartile_y(:,chop) = [];
whisker_x(:,[chop,chop+nc]) = [];
whisker_y(:,[chop,chop+nc]) = [];
median_x(:,chop) = [];
median_y(:,chop) = [];
% % % %
% ## Add caps to the remaining whiskers
cap_x = whisker_x;
cap_x(1,:) =cap_x(1,:)- 0.05;
cap_x(2,:) =cap_x(2,:)+ 0.05;
cap_y = whisker_y([1,1],:);
% #quartile_x,quartile_y
% #whisker_x,whisker_y
% #median_x,median_y
% #cap_x,cap_y
%
% ## Do the plot
mm=min(min(data));
MM=max(max(data));
if vertical
plot (quartile_x, quartile_y, 'b', ...
whisker_x, whisker_y, 'b--', ...
cap_x, cap_y, 'k', ...
median_x, median_y, 'r', ...
outliers_x, outliers_y, [symbol(1),'r'], ...
outliers2_x, outliers2_y, [symbol(2),'r']);
set(gca,'XTick',1:nc);
set(gca, 'XLim', [0.5, nc+0.5]);
set(gca, 'YLim', [mm-(MM-mm)*0.05-eps, MM+(MM-mm)*0.05+eps]);
else
% % % % % plot (quartile_y, quartile_x, "b;;",
% % % % % whisker_y, whisker_x, "b;;",
% % % % % cap_y, cap_x, "b;;",
% % % % % median_y, median_x, "r;;",
% % % % % outliers_y, outliers_x, [symbol(1),"r;;"],
% % % % % outliers2_y, outliers2_x, [symbol(2),"r;;"]);
end
if nargout,
sout=s;
end
function sout = myboxplot (data,notched,symbol,vertical,maxwhisker)
% sout = myboxplot (data,notched,symbol,vertical,maxwhisker)
% % % % endif
if nargin < 5 | isempty(maxwhisker), maxwhisker = 1.5; end
if nargin < 4 | isempty(vertical), vertical = 1; end
if nargin < 3 | isempty(symbol), symbol = ['+','o']; end
if nargin < 2 | isempty(notched), notched = 0; end
if length(symbol)==1, symbol(2)=symbol(1); end
if notched==1, notched=0.25; end
a=1-notched;
% ## figure out how many data sets we have
if iscell(data),
nc = length(data);
else
% if isvector(data), data = data(:); end
nc = size(data,2);
end
% ## compute statistics
% ## s will contain
% ## 1,5 min and max
% ## 2,3,4 1st, 2nd and 3rd quartile
% ## 6,7 lower and upper confidence intervals for median
s = zeros(7,nc);
box = zeros(1,nc);
whisker_x = ones(2,1)*[1:nc,1:nc];
whisker_y = zeros(2,2*nc);
outliers_x = [];
outliers_y = [];
outliers2_x = [];
outliers2_y = [];
for i=1:nc
% ## Get the next data set from the array or cell array
if iscell(data)
col = data{i}(:);
else
col = data(:,i);
end
% ## Skip missing data
% % % % % % % col(isnan(col) | isna (col)) = [];
col(isnan(col)) = [];
% ## Remember the data length
nd = length(col);
box(i) = nd;
if (nd > 1)
% ## min,max and quartiles
% s(1:5,i) = statistics(col)(1:5);
s(1,i)=min(col);
s(5,i)=max(col);
s(2,i)=myprctilecol(col,25);
s(3,i)=myprctilecol(col,50);
s(4,i)=myprctilecol(col,75);
% ## confidence interval for the median
est = 1.57*(s(4,i)-s(2,i))/sqrt(nd);
s(6,i) = max([s(3,i)-est, s(2,i)]);
s(7,i) = min([s(3,i)+est, s(4,i)]);
% ## whiskers out to the last point within the desired inter-quartile range
IQR = maxwhisker*(s(4,i)-s(2,i));
whisker_y(:,i) = [min(col(col >= s(2,i)-IQR)); s(2,i)];
whisker_y(:,nc+i) = [max(col(col <= s(4,i)+IQR)); s(4,i)];
% ## outliers beyond 1 and 2 inter-quartile ranges
outliers = col((col < s(2,i)-IQR & col >= s(2,i)-2*IQR) | (col > s(4,i)+IQR & col <= s(4,i)+2*IQR));
outliers2 = col(col < s(2,i)-2*IQR | col > s(4,i)+2*IQR);
outliers_x = [outliers_x; i*ones(size(outliers))];
outliers_y = [outliers_y; outliers];
outliers2_x = [outliers2_x; i*ones(size(outliers2))];
outliers2_y = [outliers2_y; outliers2];
elseif (nd == 1)
% ## all statistics collapse to the value of the point
s(:,i) = col;
% ## single point data sets are plotted as outliers.
outliers_x = [outliers_x; i];
outliers_y = [outliers_y; col];
else
% ## no statistics if no points
s(:,i) = NaN;
end
end
% % % % if isempty(outliers2_y)
% % % % outliers2_y=
% ## Note which boxes don't have enough stats
chop = find(box <= 1);
% ## Draw a box around the quartiles, with width proportional to the number of
% ## items in the box. Draw notches if desired.
box = box*0.23/max(box);
quartile_x = ones(11,1)*[1:nc] + [-a;-1;-1;1;1;a;1;1;-1;-1;-a]*box;
quartile_y = s([3,7,4,4,7,3,6,2,2,6,3],:);
% ## Draw a line through the median
median_x = ones(2,1)*[1:nc] + [-a;+a]*box;
% median_x=median(col);
median_y = s([3,3],:);
% ## Chop all boxes which don't have enough stats
quartile_x(:,chop) = [];
quartile_y(:,chop) = [];
whisker_x(:,[chop,chop+nc]) = [];
whisker_y(:,[chop,chop+nc]) = [];
median_x(:,chop) = [];
median_y(:,chop) = [];
% % % %
% ## Add caps to the remaining whiskers
cap_x = whisker_x;
cap_x(1,:) =cap_x(1,:)- 0.05;
cap_x(2,:) =cap_x(2,:)+ 0.05;
cap_y = whisker_y([1,1],:);
% #quartile_x,quartile_y
% #whisker_x,whisker_y
% #median_x,median_y
% #cap_x,cap_y
%
% ## Do the plot
mm=min(min(data));
MM=max(max(data));
if vertical
plot (quartile_x, quartile_y, 'b', ...
whisker_x, whisker_y, 'b--', ...
cap_x, cap_y, 'k', ...
median_x, median_y, 'r', ...
outliers_x, outliers_y, [symbol(1),'r'], ...
outliers2_x, outliers2_y, [symbol(2),'r']);
set(gca,'XTick',1:nc);
set(gca, 'XLim', [0.5, nc+0.5]);
set(gca, 'YLim', [mm-(MM-mm)*0.05-eps, MM+(MM-mm)*0.05+eps]);
else
% % % % % plot (quartile_y, quartile_x, "b;;",
% % % % % whisker_y, whisker_x, "b;;",
% % % % % cap_y, cap_x, "b;;",
% % % % % median_y, median_x, "r;;",
% % % % % outliers_y, outliers_x, [symbol(1),"r;;"],
% % % % % outliers2_y, outliers2_x, [symbol(2),"r;;"]);
end
if nargout,
sout=s;
end
% % % endfunction

38
matlab/gsa/myprctilecol.m
View File

@ -1,20 +1,20 @@
function y = myprctilecol(x,p);
xx = sort(x);
[m,n] = size(x);
if m==1 | n==1
m = max(m,n);
if m == 1,
y = x*ones(length(p),1);
return;
end
n = 1;
q = 100*(0.5:m - 0.5)./m;
xx = [min(x); xx(:); max(x)];
else
q = 100*(0.5:m - 0.5)./m;
xx = [min(x); xx; max(x)];
end
q = [0 q 100];
function y = myprctilecol(x,p);
xx = sort(x);
[m,n] = size(x);
if m==1 | n==1
m = max(m,n);
if m == 1,
y = x*ones(length(p),1);
return;
end
n = 1;
q = 100*(0.5:m - 0.5)./m;
xx = [min(x); xx(:); max(x)];
else
q = 100*(0.5:m - 0.5)./m;
xx = [min(x); xx; max(x)];
end
q = [0 q 100];
y = interp1(q,xx,p);

78
matlab/gsa/read_data.m
View File

@ -1,39 +1,39 @@
function [gend, data] = read_data
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
global options_ bayestopt_
rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
gend = options_.nobs;
rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
if options_.loglinear == 1 & ~options_.logdata
rawdata = log(rawdata);
end
if options_.prefilter == 1
bayestopt_.mean_varobs = mean(rawdata,1);
data = transpose(rawdata-ones(gend,1)*bayestopt_.mean_varobs);
else
data = transpose(rawdata);
end
if ~isreal(rawdata)
error(['There are complex values in the data. Probably a wrong' ...
' transformation'])
end
function [gend, data] = read_data
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
global options_ bayestopt_
rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
gend = options_.nobs;
rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
if options_.loglinear == 1 & ~options_.logdata
rawdata = log(rawdata);
end
if options_.prefilter == 1
bayestopt_.mean_varobs = mean(rawdata,1);
data = transpose(rawdata-ones(gend,1)*bayestopt_.mean_varobs);
else
data = transpose(rawdata);
end
if ~isreal(rawdata)
error(['There are complex values in the data. Probably a wrong' ...
' transformation'])
end

702
matlab/gsa/redform_map.m
View File

@ -1,351 +1,351 @@
function redform_map(dirname)
%function redform_map(dirname)
% inputs (from opt_gsa structure
% anamendo = options_gsa_.namendo;
% anamlagendo = options_gsa_.namlagendo;
% anamexo = options_gsa_.namexo;
% iload = options_gsa_.load_redform;
% pprior = options_gsa_.pprior;
% ilog = options_gsa_.logtrans_redform;
% threshold = options_gsa_.threshold_redform;
% ksstat = options_gsa_.ksstat_redform;
% alpha2 = options_gsa_.alpha2_redform;
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
global M_ oo_ estim_params_ options_ bayestopt_
options_gsa_ = options_.opt_gsa;
anamendo = options_gsa_.namendo;
anamlagendo = options_gsa_.namlagendo;
anamexo = options_gsa_.namexo;
iload = options_gsa_.load_redform;
pprior = options_gsa_.pprior;
ilog = options_gsa_.logtrans_redform;
threshold = options_gsa_.threshold_redform;
ksstat = options_gsa_.ksstat_redform;
alpha2 = options_gsa_.alpha2_redform;
pnames = M_.param_names(estim_params_.param_vals(:,1),:);
if nargin==0,
dirname='';
end
if pprior
load([dirname,'/',M_.fname,'_prior']);
adir=[dirname '/redform_stab'];
else
load([dirname,'/',M_.fname,'_mc']);
adir=[dirname '/redform_mc'];
end
if ~exist('T')
stab_map_(dirname);
if pprior
load([dirname,'/',M_.fname,'_prior'],'T');
else
load([dirname,'/',M_.fname,'_mc'],'T');
end
end
if isempty(dir(adir))
mkdir(adir)
end
adir0=pwd;
%cd(adir)
nspred=size(T,2)-M_.exo_nbr;
x0=lpmat(istable,:);
[kn, np]=size(x0);
offset = length(bayestopt_.pshape)-np;
if options_gsa_.prior_range,
pshape=5*(ones(np,1));
pd = [NaN(np,1) NaN(np,1) bayestopt_.lb(offset+1:end) bayestopt_.ub(offset+1:end)];
else
pshape = bayestopt_.pshape(offset+1:end);
pd = [bayestopt_.p6(offset+1:end) bayestopt_.p7(offset+1:end) bayestopt_.p3(offset+1:end) bayestopt_.p4(offset+1:end)];
end
nsok = length(find(M_.lead_lag_incidence(M_.maximum_lag,:)));
clear lpmat lpmat0 egg iunstable yys
js=0;
for j=1:size(anamendo,1)
namendo=deblank(anamendo(j,:));
iendo=strmatch(namendo,M_.endo_names(oo_.dr.order_var,:),'exact');
ifig=0;
iplo=0;
for jx=1:size(anamexo,1)
namexo=deblank(anamexo(jx,:));
iexo=strmatch(namexo,M_.exo_names,'exact');
if ~isempty(iexo),
%y0=squeeze(T(iendo,iexo+nspred,istable));
y0=squeeze(T(iendo,iexo+nspred,:));
if (max(y0)-min(y0))>1.e-10,
if mod(iplo,9)==0,
ifig=ifig+1;
hfig = figure('name',[namendo,' vs. shocks ',int2str(ifig)]);
iplo=0;
end
iplo=iplo+1;
js=js+1;
xdir0 = [adir,'/',namendo,'_vs_', namexo];
if ilog==0,
if isempty(threshold)
si(:,js) = redform_private(x0, y0, pshape, pd, iload, pnames, namendo, namexo, xdir0);
else
iy=find( (y0>threshold(1)) & (y0<threshold(2)));
iyc=find( (y0<=threshold(1)) | (y0>=threshold(2)));
xdir = [xdir0,'_cut'];
if ~isempty(iy),
si(:,js) = redform_private(x0(iy,:), y0(iy), pshape, pd, iload, pnames, namendo, namexo, xdir);
end
if ~isempty(iy) & ~isempty(iyc)
delete([xdir, '/*cut*.*'])
[proba, dproba] = stab_map_1(x0, iy, iyc, 'cut',0);
indsmirnov = find(dproba>ksstat);
stab_map_1(x0, iy, iyc, 'cut',1,indsmirnov,xdir);
stab_map_2(x0(iy,:),alpha2,'cut',xdir)
stab_map_2(x0(iyc,:),alpha2,'trim',xdir)
end
end
else
[yy, xdir] = log_trans_(y0,xdir0);
silog(:,js) = redform_private(x0, yy, pshape, pd, iload, pnames, namendo, namexo, xdir);
end
figure(hfig)
subplot(3,3,iplo),
if ilog,
[saso, iso] = sort(-silog(:,js));
bar([silog(iso(1:min(np,10)),js)])
logflag='log';
else
[saso, iso] = sort(-si(:,js));
bar(si(iso(1:min(np,10)),js))
logflag='';
end
%set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
set(gca,'xticklabel',' ','fontsize',10)
set(gca,'xlim',[0.5 10.5])
for ip=1:min(np,10),
text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title([logflag,' ',namendo,' vs. ',namexo],'interpreter','none')
if iplo==9,
saveas(gcf,[dirname,'/',M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)]);
close(gcf)
end
end
end
end
if iplo<9 & iplo>0 & ifig,
saveas(gcf,[dirname,'/',M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)]);
close(gcf)
end
ifig=0;
iplo=0;
for je=1:size(anamlagendo,1)
namlagendo=deblank(anamlagendo(je,:));
ilagendo=strmatch(namlagendo,M_.endo_names(oo_.dr.order_var(oo_.dr.nstatic+1:oo_.dr.nstatic+nsok),:),'exact');
if ~isempty(ilagendo),
%y0=squeeze(T(iendo,ilagendo,istable));
y0=squeeze(T(iendo,ilagendo,:));
if (max(y0)-min(y0))>1.e-10,
if mod(iplo,9)==0,
ifig=ifig+1;
hfig = figure('name',[namendo,' vs. lags ',int2str(ifig)]);
iplo=0;
end
iplo=iplo+1;
js=js+1;
xdir0 = [adir,'/',namendo,'_vs_', namlagendo];
if ilog==0,
if isempty(threshold)
si(:,js) = redform_private(x0, y0, pshape, pd, iload, pnames, namendo, namlagendo, xdir0);
else
iy=find( (y0>threshold(1)) & (y0<threshold(2)));
iyc=find( (y0<=threshold(1)) | (y0>=threshold(2)));
xdir = [xdir0,'_cut'];
if ~isempty(iy)
si(:,js) = redform_private(x0(iy,:), y0(iy), pshape, pd, iload, pnames, namendo, namlagendo, xdir);
end
if ~isempty(iy) & ~isempty(iyc),
delete([xdir, '/*cut*.*'])
[proba, dproba] = stab_map_1(x0, iy, iyc, 'cut',0);
indsmirnov = find(dproba>ksstat);
stab_map_1(x0, iy, iyc, 'cut',1,indsmirnov,xdir);
stab_map_2(x0(iy,:),alpha2,'cut',xdir)
stab_map_2(x0(iyc,:),alpha2,'trim',xdir)
end
end
else
[yy, xdir] = log_trans_(y0,xdir0);
silog(:,js) = redform_private(x0, yy, pshape, pd, iload, pnames, namendo, namlagendo, xdir);
end
figure(hfig),
subplot(3,3,iplo),
if ilog,
[saso, iso] = sort(-silog(:,js));
bar([silog(iso(1:min(np,10)),js)])
logflag='log';
else
[saso, iso] = sort(-si(:,js));
bar(si(iso(1:min(np,10)),js))
logflag='';
end
%set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
set(gca,'xticklabel',' ','fontsize',10)
set(gca,'xlim',[0.5 10.5])
for ip=1:min(np,10),
text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title([logflag,' ',namendo,' vs. ',namlagendo,'(-1)'],'interpreter','none')
if iplo==9,
saveas(gcf,[dirname,'/',M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)]);
close(gcf)
end
end
end
end
if iplo<9 & iplo>0 & ifig,
saveas(gcf,[dirname,'/',M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)]);
close(gcf)
end
end
if ilog==0,
figure, %bar(si)
% boxplot(si','whis',10,'symbol','r.')
myboxplot(si',[],'.',[],10)
xlabel(' ')
set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
set(gca,'xlim',[0.5 np+0.5])
set(gca,'ylim',[0 1])
set(gca,'position',[0.13 0.2 0.775 0.7])
for ip=1:np,
text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title('Reduced form GSA')
saveas(gcf,[dirname,'/',M_.fname,'_redform_gsa'])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_gsa']);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_gsa']);
else
figure, %bar(silog)
% boxplot(silog','whis',10,'symbol','r.')
myboxplot(silog',[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
xlabel(' ')
set(gca,'xlim',[0.5 np+0.5])
set(gca,'ylim',[0 1])
set(gca,'position',[0.13 0.2 0.775 0.7])
for ip=1:np,
text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title('Reduced form GSA - Log-transformed elements')
saveas(gcf,[dirname,'/',M_.fname,'_redform_gsa_log'])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_gsa_log']);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_gsa_log']);
end
function si = redform_private(x0, y0, pshape, pd, iload, pnames, namy, namx, xdir)
global bayestopt_ options_
opt_gsa=options_.opt_gsa;
np=size(x0,2);
x00=x0;
if opt_gsa.prior_range,
for j=1:np,
x0(:,j)=(x0(:,j)-pd(j,3))./(pd(j,4)-pd(j,3));
end
else
x0=priorcdf(x0,pshape, pd(:,1), pd(:,2), pd(:,3), pd(:,4));
end
fname=[xdir,'/map'];
if iload==0,
figure, hist(y0,30), title([namy,' vs. ', namx])
if isempty(dir(xdir))
mkdir(xdir)
end
saveas(gcf,[xdir,'/', namy,'_vs_', namx])
eval(['print -depsc2 ' xdir,'/', namy,'_vs_', namx]);
eval(['print -dpdf ' xdir,'/', namy,'_vs_', namx]);
close(gcf)
% gsa_ = gsa_sdp_dyn(y0, x0, -2, [],[],[],1,fname, pnames);
nrun=length(y0);
nest=min(250,nrun);
nfit=min(1000,nrun);
% dotheplots = (nfit<=nest);
gsa_ = gsa_sdp(y0(1:nest), x0(1:nest,:), 2, [],[-1 -1 -1 -1 -1 0],[],0,[fname,'_est'], pnames);
if nfit>nest,
gsa_ = gsa_sdp(y0(1:nfit), x0(1:nfit,:), -2, gsa_.nvr*nest^3/nfit^3,[-1 -1 -1 -1 -1 0],[],0,fname, pnames);
end
save([fname,'.mat'],'gsa_')
[sidum, iii]=sort(-gsa_.si);
gsa_.x0=x00(1:nfit,:);
hfig=gsa_sdp_plot(gsa_,fname,pnames,iii(1:min(12,np)));
close(hfig);
gsa_.x0=x0(1:nfit,:);
% copyfile([fname,'_est.mat'],[fname,'.mat'])
figure,
plot(y0(1:nfit),[gsa_.fit y0(1:nfit)],'.'),
title([namy,' vs. ', namx,' fit'])
saveas(gcf,[xdir,'/', namy,'_vs_', namx,'_fit'])
eval(['print -depsc2 ' xdir,'/', namy,'_vs_', namx,'_fit']);
eval(['print -dpdf ' xdir,'/', namy,'_vs_', namx,'_fit']);
close(gcf)
if nfit<nrun,
npred=[nfit+1:nrun];
yf = ss_anova_fcast(x0(npred,:), gsa_);
figure,
plot(y0(npred),[yf y0(npred)],'.'),
title([namy,' vs. ', namx,' pred'])
saveas(gcf,[xdir,'/', namy,'_vs_', namx,'_pred'])
eval(['print -depsc2 ' xdir,'/', namy,'_vs_', namx,'_pred']);
eval(['print -dpdf ' xdir,'/', namy,'_vs_', namx,'_pred']);
close(gcf)
end
else
% gsa_ = gsa_sdp_dyn(y0, x0, 0, [],[],[],0,fname, pnames);
gsa_ = gsa_sdp(y0, x0, 0, [],[],[],0,fname, pnames);
yf = ss_anova_fcast(x0, gsa_);
figure,
plot(y0,[yf y0],'.'),
title([namy,' vs. ', namx,' pred'])
saveas(gcf,[xdir,'/', namy,'_vs_', namx,'_pred'])
eval(['print -depsc2 ' xdir,'/', namy,'_vs_', namx,'_pred']);
eval(['print -dpdf ' xdir,'/', namy,'_vs_', namx,'_pred']);
close(gcf)
end
% si = gsa_.multivariate.si;
si = gsa_.si;
function redform_map(dirname)
%function redform_map(dirname)
% inputs (from opt_gsa structure
% anamendo = options_gsa_.namendo;
% anamlagendo = options_gsa_.namlagendo;
% anamexo = options_gsa_.namexo;
% iload = options_gsa_.load_redform;
% pprior = options_gsa_.pprior;
% ilog = options_gsa_.logtrans_redform;
% threshold = options_gsa_.threshold_redform;
% ksstat = options_gsa_.ksstat_redform;
% alpha2 = options_gsa_.alpha2_redform;
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
global M_ oo_ estim_params_ options_ bayestopt_
options_gsa_ = options_.opt_gsa;
anamendo = options_gsa_.namendo;
anamlagendo = options_gsa_.namlagendo;
anamexo = options_gsa_.namexo;
iload = options_gsa_.load_redform;
pprior = options_gsa_.pprior;
ilog = options_gsa_.logtrans_redform;
threshold = options_gsa_.threshold_redform;
ksstat = options_gsa_.ksstat_redform;
alpha2 = options_gsa_.alpha2_redform;
pnames = M_.param_names(estim_params_.param_vals(:,1),:);
if nargin==0,
dirname='';
end
if pprior
load([dirname,'/',M_.fname,'_prior']);
adir=[dirname '/redform_stab'];
else
load([dirname,'/',M_.fname,'_mc']);
adir=[dirname '/redform_mc'];
end
if ~exist('T')
stab_map_(dirname);
if pprior
load([dirname,'/',M_.fname,'_prior'],'T');
else
load([dirname,'/',M_.fname,'_mc'],'T');
end
end
if isempty(dir(adir))
mkdir(adir)
end
adir0=pwd;
%cd(adir)
nspred=size(T,2)-M_.exo_nbr;
x0=lpmat(istable,:);
[kn, np]=size(x0);
offset = length(bayestopt_.pshape)-np;
if options_gsa_.prior_range,
pshape=5*(ones(np,1));
pd = [NaN(np,1) NaN(np,1) bayestopt_.lb(offset+1:end) bayestopt_.ub(offset+1:end)];
else
pshape = bayestopt_.pshape(offset+1:end);
pd = [bayestopt_.p6(offset+1:end) bayestopt_.p7(offset+1:end) bayestopt_.p3(offset+1:end) bayestopt_.p4(offset+1:end)];
end
nsok = length(find(M_.lead_lag_incidence(M_.maximum_lag,:)));
clear lpmat lpmat0 egg iunstable yys
js=0;
for j=1:size(anamendo,1)
namendo=deblank(anamendo(j,:));
iendo=strmatch(namendo,M_.endo_names(oo_.dr.order_var,:),'exact');
ifig=0;
iplo=0;
for jx=1:size(anamexo,1)
namexo=deblank(anamexo(jx,:));
iexo=strmatch(namexo,M_.exo_names,'exact');
if ~isempty(iexo),
%y0=squeeze(T(iendo,iexo+nspred,istable));
y0=squeeze(T(iendo,iexo+nspred,:));
if (max(y0)-min(y0))>1.e-10,
if mod(iplo,9)==0,
ifig=ifig+1;
hfig = figure('name',[namendo,' vs. shocks ',int2str(ifig)]);
iplo=0;
end
iplo=iplo+1;
js=js+1;
xdir0 = [adir,'/',namendo,'_vs_', namexo];
if ilog==0,
if isempty(threshold)
si(:,js) = redform_private(x0, y0, pshape, pd, iload, pnames, namendo, namexo, xdir0);
else
iy=find( (y0>threshold(1)) & (y0<threshold(2)));
iyc=find( (y0<=threshold(1)) | (y0>=threshold(2)));
xdir = [xdir0,'_cut'];
if ~isempty(iy),
si(:,js) = redform_private(x0(iy,:), y0(iy), pshape, pd, iload, pnames, namendo, namexo, xdir);
end
if ~isempty(iy) & ~isempty(iyc)
delete([xdir, '/*cut*.*'])
[proba, dproba] = stab_map_1(x0, iy, iyc, 'cut',0);
indsmirnov = find(dproba>ksstat);
stab_map_1(x0, iy, iyc, 'cut',1,indsmirnov,xdir);
stab_map_2(x0(iy,:),alpha2,'cut',xdir)
stab_map_2(x0(iyc,:),alpha2,'trim',xdir)
end
end
else
[yy, xdir] = log_trans_(y0,xdir0);
silog(:,js) = redform_private(x0, yy, pshape, pd, iload, pnames, namendo, namexo, xdir);
end
figure(hfig)
subplot(3,3,iplo),
if ilog,
[saso, iso] = sort(-silog(:,js));
bar([silog(iso(1:min(np,10)),js)])
logflag='log';
else
[saso, iso] = sort(-si(:,js));
bar(si(iso(1:min(np,10)),js))
logflag='';
end
%set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
set(gca,'xticklabel',' ','fontsize',10)
set(gca,'xlim',[0.5 10.5])
for ip=1:min(np,10),
text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title([logflag,' ',namendo,' vs. ',namexo],'interpreter','none')
if iplo==9,
saveas(gcf,[dirname,'/',M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)]);
close(gcf)
end
end
end
end
if iplo<9 & iplo>0 & ifig,
saveas(gcf,[dirname,'/',M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)]);
close(gcf)
end
ifig=0;
iplo=0;
for je=1:size(anamlagendo,1)
namlagendo=deblank(anamlagendo(je,:));
ilagendo=strmatch(namlagendo,M_.endo_names(oo_.dr.order_var(oo_.dr.nstatic+1:oo_.dr.nstatic+nsok),:),'exact');
if ~isempty(ilagendo),
%y0=squeeze(T(iendo,ilagendo,istable));
y0=squeeze(T(iendo,ilagendo,:));
if (max(y0)-min(y0))>1.e-10,
if mod(iplo,9)==0,
ifig=ifig+1;
hfig = figure('name',[namendo,' vs. lags ',int2str(ifig)]);
iplo=0;
end
iplo=iplo+1;
js=js+1;
xdir0 = [adir,'/',namendo,'_vs_', namlagendo];
if ilog==0,
if isempty(threshold)
si(:,js) = redform_private(x0, y0, pshape, pd, iload, pnames, namendo, namlagendo, xdir0);
else
iy=find( (y0>threshold(1)) & (y0<threshold(2)));
iyc=find( (y0<=threshold(1)) | (y0>=threshold(2)));
xdir = [xdir0,'_cut'];
if ~isempty(iy)
si(:,js) = redform_private(x0(iy,:), y0(iy), pshape, pd, iload, pnames, namendo, namlagendo, xdir);
end
if ~isempty(iy) & ~isempty(iyc),
delete([xdir, '/*cut*.*'])
[proba, dproba] = stab_map_1(x0, iy, iyc, 'cut',0);
indsmirnov = find(dproba>ksstat);
stab_map_1(x0, iy, iyc, 'cut',1,indsmirnov,xdir);
stab_map_2(x0(iy,:),alpha2,'cut',xdir)
stab_map_2(x0(iyc,:),alpha2,'trim',xdir)
end
end
else
[yy, xdir] = log_trans_(y0,xdir0);
silog(:,js) = redform_private(x0, yy, pshape, pd, iload, pnames, namendo, namlagendo, xdir);
end
figure(hfig),
subplot(3,3,iplo),
if ilog,
[saso, iso] = sort(-silog(:,js));
bar([silog(iso(1:min(np,10)),js)])
logflag='log';
else
[saso, iso] = sort(-si(:,js));
bar(si(iso(1:min(np,10)),js))
logflag='';
end
%set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
set(gca,'xticklabel',' ','fontsize',10)
set(gca,'xlim',[0.5 10.5])
for ip=1:min(np,10),
text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title([logflag,' ',namendo,' vs. ',namlagendo,'(-1)'],'interpreter','none')
if iplo==9,
saveas(gcf,[dirname,'/',M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)]);
close(gcf)
end
end
end
end
if iplo<9 & iplo>0 & ifig,
saveas(gcf,[dirname,'/',M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)]);
close(gcf)
end
end
if ilog==0,
figure, %bar(si)
% boxplot(si','whis',10,'symbol','r.')
myboxplot(si',[],'.',[],10)
xlabel(' ')
set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
set(gca,'xlim',[0.5 np+0.5])
set(gca,'ylim',[0 1])
set(gca,'position',[0.13 0.2 0.775 0.7])
for ip=1:np,
text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title('Reduced form GSA')
saveas(gcf,[dirname,'/',M_.fname,'_redform_gsa'])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_gsa']);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_gsa']);
else
figure, %bar(silog)
% boxplot(silog','whis',10,'symbol','r.')
myboxplot(silog',[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
xlabel(' ')
set(gca,'xlim',[0.5 np+0.5])
set(gca,'ylim',[0 1])
set(gca,'position',[0.13 0.2 0.775 0.7])
for ip=1:np,
text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title('Reduced form GSA - Log-transformed elements')
saveas(gcf,[dirname,'/',M_.fname,'_redform_gsa_log'])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_gsa_log']);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_gsa_log']);
end
function si = redform_private(x0, y0, pshape, pd, iload, pnames, namy, namx, xdir)
global bayestopt_ options_
opt_gsa=options_.opt_gsa;
np=size(x0,2);
x00=x0;
if opt_gsa.prior_range,
for j=1:np,
x0(:,j)=(x0(:,j)-pd(j,3))./(pd(j,4)-pd(j,3));
end
else
x0=priorcdf(x0,pshape, pd(:,1), pd(:,2), pd(:,3), pd(:,4));
end
fname=[xdir,'/map'];
if iload==0,
figure, hist(y0,30), title([namy,' vs. ', namx])
if isempty(dir(xdir))
mkdir(xdir)
end
saveas(gcf,[xdir,'/', namy,'_vs_', namx])
eval(['print -depsc2 ' xdir,'/', namy,'_vs_', namx]);
eval(['print -dpdf ' xdir,'/', namy,'_vs_', namx]);
close(gcf)
% gsa_ = gsa_sdp_dyn(y0, x0, -2, [],[],[],1,fname, pnames);
nrun=length(y0);
nest=min(250,nrun);
nfit=min(1000,nrun);
% dotheplots = (nfit<=nest);
gsa_ = gsa_sdp(y0(1:nest), x0(1:nest,:), 2, [],[-1 -1 -1 -1 -1 0],[],0,[fname,'_est'], pnames);
if nfit>nest,
gsa_ = gsa_sdp(y0(1:nfit), x0(1:nfit,:), -2, gsa_.nvr*nest^3/nfit^3,[-1 -1 -1 -1 -1 0],[],0,fname, pnames);
end
save([fname,'.mat'],'gsa_')
[sidum, iii]=sort(-gsa_.si);
gsa_.x0=x00(1:nfit,:);
hfig=gsa_sdp_plot(gsa_,fname,pnames,iii(1:min(12,np)));
close(hfig);
gsa_.x0=x0(1:nfit,:);
% copyfile([fname,'_est.mat'],[fname,'.mat'])
figure,
plot(y0(1:nfit),[gsa_.fit y0(1:nfit)],'.'),
title([namy,' vs. ', namx,' fit'])
saveas(gcf,[xdir,'/', namy,'_vs_', namx,'_fit'])
eval(['print -depsc2 ' xdir,'/', namy,'_vs_', namx,'_fit']);
eval(['print -dpdf ' xdir,'/', namy,'_vs_', namx,'_fit']);
close(gcf)
if nfit<nrun,
npred=[nfit+1:nrun];
yf = ss_anova_fcast(x0(npred,:), gsa_);
figure,
plot(y0(npred),[yf y0(npred)],'.'),
title([namy,' vs. ', namx,' pred'])
saveas(gcf,[xdir,'/', namy,'_vs_', namx,'_pred'])
eval(['print -depsc2 ' xdir,'/', namy,'_vs_', namx,'_pred']);
eval(['print -dpdf ' xdir,'/', namy,'_vs_', namx,'_pred']);
close(gcf)
end
else
% gsa_ = gsa_sdp_dyn(y0, x0, 0, [],[],[],0,fname, pnames);
gsa_ = gsa_sdp(y0, x0, 0, [],[],[],0,fname, pnames);
yf = ss_anova_fcast(x0, gsa_);
figure,
plot(y0,[yf y0],'.'),
title([namy,' vs. ', namx,' pred'])
saveas(gcf,[xdir,'/', namy,'_vs_', namx,'_pred'])
eval(['print -depsc2 ' xdir,'/', namy,'_vs_', namx,'_pred']);
eval(['print -dpdf ' xdir,'/', namy,'_vs_', namx,'_pred']);
close(gcf)
end
% si = gsa_.multivariate.si;
si = gsa_.si;

330
matlab/gsa/redform_screen.m
View File

@ -1,165 +1,165 @@
function redform_screen(dirname)
%function redform_map(dirname)
% inputs (from opt_gsa structure
% anamendo = options_gsa_.namendo;
% anamlagendo = options_gsa_.namlagendo;
% anamexo = options_gsa_.namexo;
% iload = options_gsa_.load_redform;
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
global M_ oo_ estim_params_ options_ bayestopt_
options_gsa_ = options_.opt_gsa;
anamendo = options_gsa_.namendo;
anamlagendo = options_gsa_.namlagendo;
anamexo = options_gsa_.namexo;
iload = options_gsa_.load_redform;
nliv = options_gsa_.morris_nliv;
pnames = M_.param_names(estim_params_.param_vals(:,1),:);
if nargin==0,
dirname='';
end
load([dirname,'/',M_.fname,'_prior'],'lpmat','lpmat0','istable','T');
nspred=oo_.dr.nspred;
[kn, np]=size(lpmat);
nshock = length(bayestopt_.pshape)-np;
nsok = length(find(M_.lead_lag_incidence(M_.maximum_lag,:)));
js=0;
for j=1:size(anamendo,1),
namendo=deblank(anamendo(j,:));
iendo=strmatch(namendo,M_.endo_names(oo_.dr.order_var,:),'exact');
iplo=0;
ifig=0;
for jx=1:size(anamexo,1)
namexo=deblank(anamexo(jx,:));
iexo=strmatch(namexo,M_.exo_names,'exact');
if ~isempty(iexo),
y0=teff(T(iendo,iexo+nspred,:),kn,istable);
if ~isempty(y0),
if mod(iplo,9)==0,
ifig=ifig+1;
figure('name',[namendo,' vs. shocks ',int2str(ifig)]),
iplo=0;
end
iplo=iplo+1;
js=js+1;
subplot(3,3,iplo),
[SAmeas, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
SAM = squeeze(SAMorris(nshock+1:end,1));
SA(:,js)=SAM./(max(SAM)+eps);
[saso, iso] = sort(-SA(:,js));
bar(SA(iso(1:min(np,10)),js))
%set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
set(gca,'xticklabel',' ','fontsize',10)
set(gca,'xlim',[0.5 10.5])
for ip=1:min(np,10),
text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title([namendo,' vs. ',namexo],'interpreter','none')
if iplo==9,
saveas(gcf,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)]);
close(gcf)
end
end
end
end
if iplo<9 & iplo>0 & ifig,
saveas(gcf,[dirname,'/',M_.fname,'_', namendo,'_vs_shocks_',num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_', namendo,'_vs_shocks_',num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_', namendo,'_vs_shocks_',num2str(ifig)]);
close(gcf)
end
iplo=0;
ifig=0;
for je=1:size(anamlagendo,1)
namlagendo=deblank(anamlagendo(je,:));
ilagendo=strmatch(namlagendo,M_.endo_names(oo_.dr.order_var(oo_.dr.nstatic+1:oo_.dr.nstatic+nsok),:),'exact');
if ~isempty(ilagendo),
y0=teff(T(iendo,ilagendo,:),kn,istable);
if ~isempty(y0),
if mod(iplo,9)==0,
ifig=ifig+1;
figure('name',[namendo,' vs. lagged endogenous ',int2str(ifig)]),
iplo=0;
end
iplo=iplo+1;
js=js+1;
subplot(3,3,iplo),
[SAmeas, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
SAM = squeeze(SAMorris(nshock+1:end,1));
SA(:,js)=SAM./(max(SAM)+eps);
[saso, iso] = sort(-SA(:,js));
bar(SA(iso(1:min(np,10)),js))
%set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
set(gca,'xticklabel',' ','fontsize',10)
set(gca,'xlim',[0.5 10.5])
for ip=1:min(np,10),
text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title([namendo,' vs. ',namlagendo,'(-1)'],'interpreter','none')
if iplo==9,
saveas(gcf,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)]);
close(gcf)
end
end
end
end
if iplo<9 & iplo>0 & ifig,
saveas(gcf,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)]);
close(gcf)
end
end
figure,
%bar(SA)
% boxplot(SA','whis',10,'symbol','r.')
myboxplot(SA',[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
set(gca,'xlim',[0.5 np+0.5])
set(gca,'ylim',[0 1])
set(gca,'position',[0.13 0.2 0.775 0.7])
for ip=1:np,
text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
xlabel(' ')
ylabel('Elementary Effects')
title('Reduced form screening')
saveas(gcf,[dirname,'/',M_.fname,'_redform_screen'])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_screen']);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_screen']);
function redform_screen(dirname)
%function redform_map(dirname)
% inputs (from opt_gsa structure
% anamendo = options_gsa_.namendo;
% anamlagendo = options_gsa_.namlagendo;
% anamexo = options_gsa_.namexo;
% iload = options_gsa_.load_redform;
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
global M_ oo_ estim_params_ options_ bayestopt_
options_gsa_ = options_.opt_gsa;
anamendo = options_gsa_.namendo;
anamlagendo = options_gsa_.namlagendo;
anamexo = options_gsa_.namexo;
iload = options_gsa_.load_redform;
nliv = options_gsa_.morris_nliv;
pnames = M_.param_names(estim_params_.param_vals(:,1),:);
if nargin==0,
dirname='';
end
load([dirname,'/',M_.fname,'_prior'],'lpmat','lpmat0','istable','T');
nspred=oo_.dr.nspred;
[kn, np]=size(lpmat);
nshock = length(bayestopt_.pshape)-np;
nsok = length(find(M_.lead_lag_incidence(M_.maximum_lag,:)));
js=0;
for j=1:size(anamendo,1),
namendo=deblank(anamendo(j,:));
iendo=strmatch(namendo,M_.endo_names(oo_.dr.order_var,:),'exact');
iplo=0;
ifig=0;
for jx=1:size(anamexo,1)
namexo=deblank(anamexo(jx,:));
iexo=strmatch(namexo,M_.exo_names,'exact');
if ~isempty(iexo),
y0=teff(T(iendo,iexo+nspred,:),kn,istable);
if ~isempty(y0),
if mod(iplo,9)==0,
ifig=ifig+1;
figure('name',[namendo,' vs. shocks ',int2str(ifig)]),
iplo=0;
end
iplo=iplo+1;
js=js+1;
subplot(3,3,iplo),
[SAmeas, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
SAM = squeeze(SAMorris(nshock+1:end,1));
SA(:,js)=SAM./(max(SAM)+eps);
[saso, iso] = sort(-SA(:,js));
bar(SA(iso(1:min(np,10)),js))
%set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
set(gca,'xticklabel',' ','fontsize',10)
set(gca,'xlim',[0.5 10.5])
for ip=1:min(np,10),
text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title([namendo,' vs. ',namexo],'interpreter','none')
if iplo==9,
saveas(gcf,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)]);
close(gcf)
end
end
end
end
if iplo<9 & iplo>0 & ifig,
saveas(gcf,[dirname,'/',M_.fname,'_', namendo,'_vs_shocks_',num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_', namendo,'_vs_shocks_',num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_', namendo,'_vs_shocks_',num2str(ifig)]);
close(gcf)
end
iplo=0;
ifig=0;
for je=1:size(anamlagendo,1)
namlagendo=deblank(anamlagendo(je,:));
ilagendo=strmatch(namlagendo,M_.endo_names(oo_.dr.order_var(oo_.dr.nstatic+1:oo_.dr.nstatic+nsok),:),'exact');
if ~isempty(ilagendo),
y0=teff(T(iendo,ilagendo,:),kn,istable);
if ~isempty(y0),
if mod(iplo,9)==0,
ifig=ifig+1;
figure('name',[namendo,' vs. lagged endogenous ',int2str(ifig)]),
iplo=0;
end
iplo=iplo+1;
js=js+1;
subplot(3,3,iplo),
[SAmeas, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
SAM = squeeze(SAMorris(nshock+1:end,1));
SA(:,js)=SAM./(max(SAM)+eps);
[saso, iso] = sort(-SA(:,js));
bar(SA(iso(1:min(np,10)),js))
%set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
set(gca,'xticklabel',' ','fontsize',10)
set(gca,'xlim',[0.5 10.5])
for ip=1:min(np,10),
text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title([namendo,' vs. ',namlagendo,'(-1)'],'interpreter','none')
if iplo==9,
saveas(gcf,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)]);
close(gcf)
end
end
end
end
if iplo<9 & iplo>0 & ifig,
saveas(gcf,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)]);
eval(['print -dpdf ' dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)]);
close(gcf)
end
end
figure,
%bar(SA)
% boxplot(SA','whis',10,'symbol','r.')
myboxplot(SA',[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
set(gca,'xlim',[0.5 np+0.5])
set(gca,'ylim',[0 1])
set(gca,'position',[0.13 0.2 0.775 0.7])
for ip=1:np,
text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
xlabel(' ')
ylabel('Elementary Effects')
title('Reduced form screening')
saveas(gcf,[dirname,'/',M_.fname,'_redform_screen'])
eval(['print -depsc2 ' dirname,'/',M_.fname,'_redform_screen']);
eval(['print -dpdf ' dirname,'/',M_.fname,'_redform_screen']);

58
matlab/gsa/set_shocks_param.m
View File

@ -1,30 +1,30 @@
function set_shocks_param(xparam1)
global estim_params_ M_
nvx = estim_params_.nvx;
ncx = estim_params_.ncx;
np = estim_params_.np;
Sigma_e = M_.Sigma_e;
offset = 0;
if nvx
offset = offset + nvx;
var_exo = estim_params_.var_exo;
for i=1:nvx
k = var_exo(i,1);
Sigma_e(k,k) = xparam1(i)^2;
end
end
if ncx
offset = offset + estim_params_.nvn;
corrx = estim_params_.corrx;
for i=1:ncx
k1 = corrx(i,1);
k2 = corrx(i,2);
Sigma_e(k1,k2) = xparam1(i+offset)*sqrt(Sigma_e_(k1,k1)*Sigma_e_(k2,k2));
Sigma_e(k2,k1) = Sigma_e_(k1,k2);
end
end
function set_shocks_param(xparam1)
global estim_params_ M_
nvx = estim_params_.nvx;
ncx = estim_params_.ncx;
np = estim_params_.np;
Sigma_e = M_.Sigma_e;
offset = 0;
if nvx
offset = offset + nvx;
var_exo = estim_params_.var_exo;
for i=1:nvx
k = var_exo(i,1);
Sigma_e(k,k) = xparam1(i)^2;
end
end
if ncx
offset = offset + estim_params_.nvn;
corrx = estim_params_.corrx;
for i=1:ncx
k1 = corrx(i,1);
k2 = corrx(i,2);
Sigma_e(k1,k2) = xparam1(i+offset)*sqrt(Sigma_e_(k1,k1)*Sigma_e_(k2,k2));
Sigma_e(k2,k1) = Sigma_e_(k1,k2);
end
end
M_.Sigma_e = Sigma_e;

12
matlab/gsa/skewness.m
View File

@ -1,7 +1,7 @@
function s=skewness(y),
% y=stand_(y);
% s=mean(y.^3);
m2=mean((y-mean(y)).^2);
m3=mean((y-mean(y)).^3);
function s=skewness(y),
% y=stand_(y);
% s=mean(y.^3);
m2=mean((y-mean(y)).^2);
m3=mean((y-mean(y)).^3);
s=m3/m2^1.5;

146
matlab/gsa/smirnov.m
View File

@ -1,73 +1,73 @@
function [H,prob,d] = smirnov(x1 , x2 , alpha, iflag )
% Smirnov test for 2 distributions
% [H,prob,d] = smirnov(x1 , x2 , alpha, iflag )
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
if nargin<3
alpha = 0.05;
end
if nargin<4,
iflag=0;
end
% empirical cdfs.
xmix= [x1;x2];
bin = [-inf ; sort(xmix) ; inf];
ncount1 = histc (x1 , bin);
ncount1 = ncount1(:);
ncount2 = histc (x2 , bin);
ncount2 = ncount2(:);
cum1 = cumsum(ncount1)./sum(ncount1);
cum1 = cum1(1:end-1);
cum2 = cumsum(ncount2)./sum(ncount2);
cum2 = cum2(1:end-1);
n1= length(x1);
n2= length(x2);
n = n1*n2 /(n1+n2);
% Compute the d(n1,n2) statistics.
if iflag==0,
d = max(abs(cum1 - cum2));
elseif iflag==-1
d = max(cum2 - cum1);
elseif iflag==1
d = max(cum1 - cum2);
end
%
% Compute P-value check H0 hypothesis
%
lam = max((sqrt(n) + 0.12 + 0.11/sqrt(n)) * d , 0);
if iflag == 0
j = [1:101]';
prob = 2 * sum((-1).^(j-1).*exp(-2*lam*lam*j.^2));
prob=max(prob,0);
prob=min(1,prob);
else
prob = exp(-2*lam*lam);
end
H = (alpha >= prob);
function [H,prob,d] = smirnov(x1 , x2 , alpha, iflag )
% Smirnov test for 2 distributions
% [H,prob,d] = smirnov(x1 , x2 , alpha, iflag )
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
if nargin<3
alpha = 0.05;
end
if nargin<4,
iflag=0;
end
% empirical cdfs.
xmix= [x1;x2];
bin = [-inf ; sort(xmix) ; inf];
ncount1 = histc (x1 , bin);
ncount1 = ncount1(:);
ncount2 = histc (x2 , bin);
ncount2 = ncount2(:);
cum1 = cumsum(ncount1)./sum(ncount1);
cum1 = cum1(1:end-1);
cum2 = cumsum(ncount2)./sum(ncount2);
cum2 = cum2(1:end-1);
n1= length(x1);
n2= length(x2);
n = n1*n2 /(n1+n2);
% Compute the d(n1,n2) statistics.
if iflag==0,
d = max(abs(cum1 - cum2));
elseif iflag==-1
d = max(cum2 - cum1);
elseif iflag==1
d = max(cum1 - cum2);
end
%
% Compute P-value check H0 hypothesis
%
lam = max((sqrt(n) + 0.12 + 0.11/sqrt(n)) * d , 0);
if iflag == 0
j = [1:101]';
prob = 2 * sum((-1).^(j-1).*exp(-2*lam*lam*j.^2));
prob=max(prob,0);
prob=min(1,prob);
else
prob = exp(-2*lam*lam);
end
H = (alpha >= prob);

104
matlab/gsa/speed.m
View File

@ -1,52 +1,52 @@
function [tadj, iff] = speed(A,B,mf,p),
% [tadj, iff] = speed(A,B,mf,p),
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
nvar=length(mf);
nstate= size(A,1);
nshock = size(B,2);
nrun=size(B,3);
iff=zeros(nvar,nshock,nrun);
tadj=iff;
disp('Computing speed of adjustement ...')
h = waitbar(0,'Speed of adjustement...');
for i=1:nrun,
irf=zeros(nvar,nshock);
a=squeeze(A(:,:,i));
b=squeeze(B(:,:,i));
IFF=inv(eye(nstate)-a)*b;
iff(:,:,i)=IFF(mf,:);
IF=IFF-b;
t=0;
while any(any(irf<0.5))
t=t+1;
IFT=((eye(nstate)-a^(t+1))*inv(eye(nstate)-a))*b-b;
irf=IFT(mf,:)./(IF(mf,:)+eps);
irf = irf.*(abs(IF(mf,:))>1.e-7)+(abs(IF(mf,:))<=1.e-7);
%irf=ft(mf,:);
tt=(irf>0.5).*t;
tadj(:,:,i)=((tt-tadj(:,:,i))==tt).*tt+tadj(:,:,i);
end
waitbar(i/nrun,h)
end
disp(' ')
disp('.. done !')
close(h)
function [tadj, iff] = speed(A,B,mf,p),
% [tadj, iff] = speed(A,B,mf,p),
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
nvar=length(mf);
nstate= size(A,1);
nshock = size(B,2);
nrun=size(B,3);
iff=zeros(nvar,nshock,nrun);
tadj=iff;
disp('Computing speed of adjustement ...')
h = waitbar(0,'Speed of adjustement...');
for i=1:nrun,
irf=zeros(nvar,nshock);
a=squeeze(A(:,:,i));
b=squeeze(B(:,:,i));
IFF=inv(eye(nstate)-a)*b;
iff(:,:,i)=IFF(mf,:);
IF=IFF-b;
t=0;
while any(any(irf<0.5))
t=t+1;
IFT=((eye(nstate)-a^(t+1))*inv(eye(nstate)-a))*b-b;
irf=IFT(mf,:)./(IF(mf,:)+eps);
irf = irf.*(abs(IF(mf,:))>1.e-7)+(abs(IF(mf,:))<=1.e-7);
%irf=ft(mf,:);
tt=(irf>0.5).*t;
tadj(:,:,i)=((tt-tadj(:,:,i))==tt).*tt+tadj(:,:,i);
end
waitbar(i/nrun,h)
end
disp(' ')
disp('.. done !')
close(h)

1242
matlab/gsa/stab_map_.m
View File

File diff suppressed because it is too large Load Diff

196
matlab/gsa/stab_map_1.m
View File

@ -1,98 +1,98 @@
function [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, iplot, ipar, dirname, pcrit)
%function [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, iplot, ipar, dirname, pcrit)
%
% lpmat = Monte Carlo matrix
% ibehaviour = index of behavioural runs
% inonbehaviour = index of non-behavioural runs
% aname = label of the analysis
% iplot = 1 plot cumulative distributions (default)
% iplot = 0 no plots
% ipar = index array of parameters to plot
% dirname = (OPTIONAL) path of the directory where to save
% (default: current directory)
% pcrit = (OPTIONAL) critical value of the pvalue below which show the plots
%
% Plots: dotted lines for BEHAVIOURAL
% solid lines for NON BEHAVIOURAL
% USES smirnov
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
global estim_params_ bayestopt_ M_ options_
if nargin<5,
iplot=1;
end
fname_ = M_.fname;
if nargin<7,
dirname='';;
end
nshock = estim_params_.nvx;
nshock = nshock + estim_params_.nvn;
nshock = nshock + estim_params_.ncx;
nshock = nshock + estim_params_.ncn;
npar=size(lpmat,2);
ishock= npar>estim_params_.np;
if nargin<6,
ipar=[];
end
if nargin<8 || isempty(pcrit),
pcrit=1;
end
% Smirnov test for Blanchard;
for j=1:npar,
[H,P,KSSTAT] = smirnov(lpmat(ibehaviour,j),lpmat(inonbehaviour,j));
proba(j)=P;
dproba(j)=KSSTAT;
end
if isempty(ipar),
% ipar=find(dproba>dcrit);
ipar=find(proba<pcrit);
end
nparplot=length(ipar);
if iplot
lpmat=lpmat(:,ipar);
ftit=bayestopt_.name(ipar+nshock*(1-ishock));
for i=1:ceil(nparplot/12),
hh=figure('name',aname);
for j=1+12*(i-1):min(nparplot,12*i),
subplot(3,4,j-12*(i-1))
if ~isempty(ibehaviour),
h=cumplot(lpmat(ibehaviour,j));
set(h,'color',[0 0 0], 'linestyle',':')
end
hold on,
if ~isempty(inonbehaviour),
h=cumplot(lpmat(inonbehaviour,j));
set(h,'color',[0 0 0])
end
% title([ftit{j},'. D-stat ', num2str(dproba(ipar(j)),2)],'interpreter','none')
title([ftit{j},'. p-value ', num2str(proba(ipar(j)),2)],'interpreter','none')
end
eval(['print -depsc2 ' dirname '/' fname_ '_' aname '_SA_' int2str(i) '.eps']);
if ~exist('OCTAVE_VERSION'),
saveas(hh,[dirname,'/',fname_,'_',aname,'_SA_',int2str(i)])
eval(['print -dpdf ' dirname '/' fname_ '_' aname '_SA_' int2str(i)]);
end
if options_.nograph, close(hh), end
end
end
function [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, iplot, ipar, dirname, pcrit)
%function [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, iplot, ipar, dirname, pcrit)
%
% lpmat = Monte Carlo matrix
% ibehaviour = index of behavioural runs
% inonbehaviour = index of non-behavioural runs
% aname = label of the analysis
% iplot = 1 plot cumulative distributions (default)
% iplot = 0 no plots
% ipar = index array of parameters to plot
% dirname = (OPTIONAL) path of the directory where to save
% (default: current directory)
% pcrit = (OPTIONAL) critical value of the pvalue below which show the plots
%
% Plots: dotted lines for BEHAVIOURAL
% solid lines for NON BEHAVIOURAL
% USES smirnov
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
global estim_params_ bayestopt_ M_ options_
if nargin<5,
iplot=1;
end
fname_ = M_.fname;
if nargin<7,
dirname='';;
end
nshock = estim_params_.nvx;
nshock = nshock + estim_params_.nvn;
nshock = nshock + estim_params_.ncx;
nshock = nshock + estim_params_.ncn;
npar=size(lpmat,2);
ishock= npar>estim_params_.np;
if nargin<6,
ipar=[];
end
if nargin<8 || isempty(pcrit),
pcrit=1;
end
% Smirnov test for Blanchard;
for j=1:npar,
[H,P,KSSTAT] = smirnov(lpmat(ibehaviour,j),lpmat(inonbehaviour,j));
proba(j)=P;
dproba(j)=KSSTAT;
end
if isempty(ipar),
% ipar=find(dproba>dcrit);
ipar=find(proba<pcrit);
end
nparplot=length(ipar);
if iplot
lpmat=lpmat(:,ipar);
ftit=bayestopt_.name(ipar+nshock*(1-ishock));
for i=1:ceil(nparplot/12),
hh=figure('name',aname);
for j=1+12*(i-1):min(nparplot,12*i),
subplot(3,4,j-12*(i-1))
if ~isempty(ibehaviour),
h=cumplot(lpmat(ibehaviour,j));
set(h,'color',[0 0 0], 'linestyle',':')
end
hold on,
if ~isempty(inonbehaviour),
h=cumplot(lpmat(inonbehaviour,j));
set(h,'color',[0 0 0])
end
% title([ftit{j},'. D-stat ', num2str(dproba(ipar(j)),2)],'interpreter','none')
title([ftit{j},'. p-value ', num2str(proba(ipar(j)),2)],'interpreter','none')
end
eval(['print -depsc2 ' dirname '/' fname_ '_' aname '_SA_' int2str(i) '.eps']);
if ~exist('OCTAVE_VERSION'),
saveas(hh,[dirname,'/',fname_,'_',aname,'_SA_',int2str(i)])
eval(['print -dpdf ' dirname '/' fname_ '_' aname '_SA_' int2str(i)]);
end
if options_.nograph, close(hh), end
end
end

210
matlab/gsa/stab_map_2.m
View File

@ -1,105 +1,105 @@
function stab_map_2(x,alpha2, pvalue, fnam, dirname)
% function stab_map_2(x, alpha2, pvalue, fnam, dirname)
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
%global bayestopt_ estim_params_ dr_ options_ ys_ fname_
global bayestopt_ estim_params_ options_ oo_ M_
npar=size(x,2);
nsam=size(x,1);
ishock= npar>estim_params_.np;
if nargin<4,
fnam='';
end
if nargin<5,
dirname='';
end
ys_ = oo_.dr.ys;
dr_ = oo_.dr;
fname_ = M_.fname;
nshock = estim_params_.nvx;
nshock = nshock + estim_params_.nvn;
nshock = nshock + estim_params_.ncx;
nshock = nshock + estim_params_.ncn;
c0=corrcoef(x);
c00=tril(c0,-1);
fig_nam_=[fname_,'_',fnam,'_corr_'];
ifig=0;
j2=0;
if ishock==0
npar=estim_params_.np;
else
npar=estim_params_.np+nshock;
end
for j=1:npar,
i2=find(abs(c00(:,j))>alpha2);
if length(i2)>0,
for jx=1:length(i2),
tval = abs(c00(i2(jx),j)*sqrt( (nsam-2)/(1-c00(i2(jx),j)^2) ));
tcr = tcrit(nsam-2,pvalue);
if tval>tcr,
j2=j2+1;
if mod(j2,12)==1,
ifig=ifig+1;
hh=figure('name',['Correlations in the ',fnam,' sample ', num2str(ifig)]);
end
subplot(3,4,j2-(ifig-1)*12)
% bar(c0(i2,j)),
% set(gca,'xticklabel',bayestopt_.name(i2)),
% set(gca,'xtick',[1:length(i2)])
%plot(stock_par(ixx(nfilt+1:end,i),j),stock_par(ixx(nfilt+1:end,i),i2(jx)),'.k')
%hold on,
plot(x(:,j),x(:,i2(jx)),'.')
% xlabel(deblank(estim_params_.param_names(j,:)),'interpreter','none'),
% ylabel(deblank(estim_params_.param_names(i2(jx),:)),'interpreter','none'),
if ishock,
xlabel(bayestopt_.name{j},'interpreter','none'),
ylabel(bayestopt_.name{i2(jx)},'interpreter','none'),
else
xlabel(bayestopt_.name{j+nshock},'interpreter','none'),
ylabel(bayestopt_.name{i2(jx)+nshock},'interpreter','none'),
end
title(['cc = ',num2str(c0(i2(jx),j))])
if (mod(j2,12)==0) && j2>0,
eval(['print -depsc2 ' dirname '/' fig_nam_ int2str(ifig) '.eps']);
if ~exist('OCTAVE_VERSION'),
saveas(hh,[dirname,'/',fig_nam_,int2str(ifig)])
eval(['print -dpdf ' dirname '/' fig_nam_ int2str(ifig)]);
end
if options_.nograph, close(hh), end
end
end
end
end
if (j==(npar)) && j2>0,
eval(['print -depsc2 ' dirname '/' fig_nam_ int2str(ifig) '.eps']);
if ~exist('OCTAVE_VERSION'),
saveas(gcf,[dirname,'/',fig_nam_,int2str(ifig)])
eval(['print -dpdf ' dirname '/' fig_nam_ int2str(ifig)]);
end
if options_.nograph, close(gcf), end
end
end
if ifig==0,
disp(['No correlation term >', num2str(alpha2),' found for ',fnam])
end
%close all
function stab_map_2(x,alpha2, pvalue, fnam, dirname)
% function stab_map_2(x, alpha2, pvalue, fnam, dirname)
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
%global bayestopt_ estim_params_ dr_ options_ ys_ fname_
global bayestopt_ estim_params_ options_ oo_ M_
npar=size(x,2);
nsam=size(x,1);
ishock= npar>estim_params_.np;
if nargin<4,
fnam='';
end
if nargin<5,
dirname='';
end
ys_ = oo_.dr.ys;
dr_ = oo_.dr;
fname_ = M_.fname;
nshock = estim_params_.nvx;
nshock = nshock + estim_params_.nvn;
nshock = nshock + estim_params_.ncx;
nshock = nshock + estim_params_.ncn;
c0=corrcoef(x);
c00=tril(c0,-1);
fig_nam_=[fname_,'_',fnam,'_corr_'];
ifig=0;
j2=0;
if ishock==0
npar=estim_params_.np;
else
npar=estim_params_.np+nshock;
end
for j=1:npar,
i2=find(abs(c00(:,j))>alpha2);
if length(i2)>0,
for jx=1:length(i2),
tval = abs(c00(i2(jx),j)*sqrt( (nsam-2)/(1-c00(i2(jx),j)^2) ));
tcr = tcrit(nsam-2,pvalue);
if tval>tcr,
j2=j2+1;
if mod(j2,12)==1,
ifig=ifig+1;
hh=figure('name',['Correlations in the ',fnam,' sample ', num2str(ifig)]);
end
subplot(3,4,j2-(ifig-1)*12)
% bar(c0(i2,j)),
% set(gca,'xticklabel',bayestopt_.name(i2)),
% set(gca,'xtick',[1:length(i2)])
%plot(stock_par(ixx(nfilt+1:end,i),j),stock_par(ixx(nfilt+1:end,i),i2(jx)),'.k')
%hold on,
plot(x(:,j),x(:,i2(jx)),'.')
% xlabel(deblank(estim_params_.param_names(j,:)),'interpreter','none'),
% ylabel(deblank(estim_params_.param_names(i2(jx),:)),'interpreter','none'),
if ishock,
xlabel(bayestopt_.name{j},'interpreter','none'),
ylabel(bayestopt_.name{i2(jx)},'interpreter','none'),
else
xlabel(bayestopt_.name{j+nshock},'interpreter','none'),
ylabel(bayestopt_.name{i2(jx)+nshock},'interpreter','none'),
end
title(['cc = ',num2str(c0(i2(jx),j))])
if (mod(j2,12)==0) && j2>0,
eval(['print -depsc2 ' dirname '/' fig_nam_ int2str(ifig) '.eps']);
if ~exist('OCTAVE_VERSION'),
saveas(hh,[dirname,'/',fig_nam_,int2str(ifig)])
eval(['print -dpdf ' dirname '/' fig_nam_ int2str(ifig)]);
end
if options_.nograph, close(hh), end
end
end
end
end
if (j==(npar)) && j2>0,
eval(['print -depsc2 ' dirname '/' fig_nam_ int2str(ifig) '.eps']);
if ~exist('OCTAVE_VERSION'),
saveas(gcf,[dirname,'/',fig_nam_,int2str(ifig)])
eval(['print -dpdf ' dirname '/' fig_nam_ int2str(ifig)]);
end
if options_.nograph, close(gcf), end
end
end
if ifig==0,
disp(['No correlation term >', num2str(alpha2),' found for ',fnam])
end
%close all

48
matlab/gsa/stand_.m
View File

@ -1,25 +1,25 @@
function [y, meany, stdy] = stand_(x)
% STAND_ Standardise a matrix by columns
%
% [x,my,sy]=stand(y)
%
% y: Time series (column matrix)
%
% x: standardised equivalent of y
% my: Vector of mean values for each column of y
% sy: Vector of standard deviations for each column of y
%
% Copyright (c) 2006 by JRC, European Commission, United Kingdom
% Author : Marco Ratto
if nargin==0,
return;
end
for j=1:size(x,2);
meany(j)=mean(x(find(~isnan(x(:,j))),j));
stdy(j)=std(x(find(~isnan(x(:,j))),j));
y(:,j)=(x(:,j)-meany(j))./stdy(j);
end
function [y, meany, stdy] = stand_(x)
% STAND_ Standardise a matrix by columns
%
% [x,my,sy]=stand(y)
%
% y: Time series (column matrix)
%
% x: standardised equivalent of y
% my: Vector of mean values for each column of y
% sy: Vector of standard deviations for each column of y
%
% Copyright (c) 2006 by JRC, European Commission, United Kingdom
% Author : Marco Ratto
if nargin==0,
return;
end
for j=1:size(x,2);
meany(j)=mean(x(find(~isnan(x(:,j))),j));
stdy(j)=std(x(find(~isnan(x(:,j))),j));
y(:,j)=(x(:,j)-meany(j))./stdy(j);
end
% end of m-file

296
matlab/gsa/tcrit.m
View File

@ -1,149 +1,149 @@
function t_crit = tcrit(n,pval0)
% function t_crit = tcrit(n,pval0)
%
% given the p-value pval0, the function givese the
% critical value t_crit of the t-distribution with n degress of freedom
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Copyright (C) 2011-2011 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if nargin==1 || isempty(pval0),
pval0=0.05;
end
pval = [ 0.10 0.05 0.025 0.01 0.005 0.001];
pval0=max(pval0,min(pval));
ncol=min(find(pval<=pval0))+1;
t_crit=[
1 3.078 6.314 12.706 31.821 63.657 318.313
2 1.886 2.920 4.303 6.965 9.925 22.327
3 1.638 2.353 3.182 4.541 5.841 10.215
4 1.533 2.132 2.776 3.747 4.604 7.173
5 1.476 2.015 2.571 3.365 4.032 5.893
6 1.440 1.943 2.447 3.143 3.707 5.208
7 1.415 1.895 2.365 2.998 3.499 4.782
8 1.397 1.860 2.306 2.896 3.355 4.499
9 1.383 1.833 2.262 2.821 3.250 4.296
10 1.372 1.812 2.228 2.764 3.169 4.143
11 1.363 1.796 2.201 2.718 3.106 4.024
12 1.356 1.782 2.179 2.681 3.055 3.929
13 1.350 1.771 2.160 2.650 3.012 3.852
14 1.345 1.761 2.145 2.624 2.977 3.787
15 1.341 1.753 2.131 2.602 2.947 3.733
16 1.337 1.746 2.120 2.583 2.921 3.686
17 1.333 1.740 2.110 2.567 2.898 3.646
18 1.330 1.734 2.101 2.552 2.878 3.610
19 1.328 1.729 2.093 2.539 2.861 3.579
20 1.325 1.725 2.086 2.528 2.845 3.552
21 1.323 1.721 2.080 2.518 2.831 3.527
22 1.321 1.717 2.074 2.508 2.819 3.505
23 1.319 1.714 2.069 2.500 2.807 3.485
24 1.318 1.711 2.064 2.492 2.797 3.467
25 1.316 1.708 2.060 2.485 2.787 3.450
26 1.315 1.706 2.056 2.479 2.779 3.435
27 1.314 1.703 2.052 2.473 2.771 3.421
28 1.313 1.701 2.048 2.467 2.763 3.408
29 1.311 1.699 2.045 2.462 2.756 3.396
30 1.310 1.697 2.042 2.457 2.750 3.385
31 1.309 1.696 2.040 2.453 2.744 3.375
32 1.309 1.694 2.037 2.449 2.738 3.365
33 1.308 1.692 2.035 2.445 2.733 3.356
34 1.307 1.691 2.032 2.441 2.728 3.348
35 1.306 1.690 2.030 2.438 2.724 3.340
36 1.306 1.688 2.028 2.434 2.719 3.333
37 1.305 1.687 2.026 2.431 2.715 3.326
38 1.304 1.686 2.024 2.429 2.712 3.319
39 1.304 1.685 2.023 2.426 2.708 3.313
40 1.303 1.684 2.021 2.423 2.704 3.307
41 1.303 1.683 2.020 2.421 2.701 3.301
42 1.302 1.682 2.018 2.418 2.698 3.296
43 1.302 1.681 2.017 2.416 2.695 3.291
44 1.301 1.680 2.015 2.414 2.692 3.286
45 1.301 1.679 2.014 2.412 2.690 3.281
46 1.300 1.679 2.013 2.410 2.687 3.277
47 1.300 1.678 2.012 2.408 2.685 3.273
48 1.299 1.677 2.011 2.407 2.682 3.269
49 1.299 1.677 2.010 2.405 2.680 3.265
50 1.299 1.676 2.009 2.403 2.678 3.261
51 1.298 1.675 2.008 2.402 2.676 3.258
52 1.298 1.675 2.007 2.400 2.674 3.255
53 1.298 1.674 2.006 2.399 2.672 3.251
54 1.297 1.674 2.005 2.397 2.670 3.248
55 1.297 1.673 2.004 2.396 2.668 3.245
56 1.297 1.673 2.003 2.395 2.667 3.242
57 1.297 1.672 2.002 2.394 2.665 3.239
58 1.296 1.672 2.002 2.392 2.663 3.237
59 1.296 1.671 2.001 2.391 2.662 3.234
60 1.296 1.671 2.000 2.390 2.660 3.232
61 1.296 1.670 2.000 2.389 2.659 3.229
62 1.295 1.670 1.999 2.388 2.657 3.227
63 1.295 1.669 1.998 2.387 2.656 3.225
64 1.295 1.669 1.998 2.386 2.655 3.223
65 1.295 1.669 1.997 2.385 2.654 3.220
66 1.295 1.668 1.997 2.384 2.652 3.218
67 1.294 1.668 1.996 2.383 2.651 3.216
68 1.294 1.668 1.995 2.382 2.650 3.214
69 1.294 1.667 1.995 2.382 2.649 3.213
70 1.294 1.667 1.994 2.381 2.648 3.211
71 1.294 1.667 1.994 2.380 2.647 3.209
72 1.293 1.666 1.993 2.379 2.646 3.207
73 1.293 1.666 1.993 2.379 2.645 3.206
74 1.293 1.666 1.993 2.378 2.644 3.204
75 1.293 1.665 1.992 2.377 2.643 3.202
76 1.293 1.665 1.992 2.376 2.642 3.201
77 1.293 1.665 1.991 2.376 2.641 3.199
78 1.292 1.665 1.991 2.375 2.640 3.198
79 1.292 1.664 1.990 2.374 2.640 3.197
80 1.292 1.664 1.990 2.374 2.639 3.195
81 1.292 1.664 1.990 2.373 2.638 3.194
82 1.292 1.664 1.989 2.373 2.637 3.193
83 1.292 1.663 1.989 2.372 2.636 3.191
84 1.292 1.663 1.989 2.372 2.636 3.190
85 1.292 1.663 1.988 2.371 2.635 3.189
86 1.291 1.663 1.988 2.370 2.634 3.188
87 1.291 1.663 1.988 2.370 2.634 3.187
88 1.291 1.662 1.987 2.369 2.633 3.185
89 1.291 1.662 1.987 2.369 2.632 3.184
90 1.291 1.662 1.987 2.368 2.632 3.183
91 1.291 1.662 1.986 2.368 2.631 3.182
92 1.291 1.662 1.986 2.368 2.630 3.181
93 1.291 1.661 1.986 2.367 2.630 3.180
94 1.291 1.661 1.986 2.367 2.629 3.179
95 1.291 1.661 1.985 2.366 2.629 3.178
96 1.290 1.661 1.985 2.366 2.628 3.177
97 1.290 1.661 1.985 2.365 2.627 3.176
98 1.290 1.661 1.984 2.365 2.627 3.175
99 1.290 1.660 1.984 2.365 2.626 3.175
100 1.290 1.660 1.984 2.364 2.626 3.174
inf 1.282 1.645 1.960 2.326 2.576 3.090
];
if n<=100,
t_crit=t_crit(n,ncol);
else
t_crit=t_crit(end,ncol);
function t_crit = tcrit(n,pval0)
% function t_crit = tcrit(n,pval0)
%
% given the p-value pval0, the function givese the
% critical value t_crit of the t-distribution with n degress of freedom
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Copyright (C) 2011-2011 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if nargin==1 || isempty(pval0),
pval0=0.05;
end
pval = [ 0.10 0.05 0.025 0.01 0.005 0.001];
pval0=max(pval0,min(pval));
ncol=min(find(pval<=pval0))+1;
t_crit=[
1 3.078 6.314 12.706 31.821 63.657 318.313
2 1.886 2.920 4.303 6.965 9.925 22.327
3 1.638 2.353 3.182 4.541 5.841 10.215
4 1.533 2.132 2.776 3.747 4.604 7.173
5 1.476 2.015 2.571 3.365 4.032 5.893
6 1.440 1.943 2.447 3.143 3.707 5.208
7 1.415 1.895 2.365 2.998 3.499 4.782
8 1.397 1.860 2.306 2.896 3.355 4.499
9 1.383 1.833 2.262 2.821 3.250 4.296
10 1.372 1.812 2.228 2.764 3.169 4.143
11 1.363 1.796 2.201 2.718 3.106 4.024
12 1.356 1.782 2.179 2.681 3.055 3.929
13 1.350 1.771 2.160 2.650 3.012 3.852
14 1.345 1.761 2.145 2.624 2.977 3.787
15 1.341 1.753 2.131 2.602 2.947 3.733
16 1.337 1.746 2.120 2.583 2.921 3.686
17 1.333 1.740 2.110 2.567 2.898 3.646
18 1.330 1.734 2.101 2.552 2.878 3.610
19 1.328 1.729 2.093 2.539 2.861 3.579
20 1.325 1.725 2.086 2.528 2.845 3.552
21 1.323 1.721 2.080 2.518 2.831 3.527
22 1.321 1.717 2.074 2.508 2.819 3.505
23 1.319 1.714 2.069 2.500 2.807 3.485
24 1.318 1.711 2.064 2.492 2.797 3.467
25 1.316 1.708 2.060 2.485 2.787 3.450
26 1.315 1.706 2.056 2.479 2.779 3.435
27 1.314 1.703 2.052 2.473 2.771 3.421
28 1.313 1.701 2.048 2.467 2.763 3.408
29 1.311 1.699 2.045 2.462 2.756 3.396
30 1.310 1.697 2.042 2.457 2.750 3.385
31 1.309 1.696 2.040 2.453 2.744 3.375
32 1.309 1.694 2.037 2.449 2.738 3.365
33 1.308 1.692 2.035 2.445 2.733 3.356
34 1.307 1.691 2.032 2.441 2.728 3.348
35 1.306 1.690 2.030 2.438 2.724 3.340
36 1.306 1.688 2.028 2.434 2.719 3.333
37 1.305 1.687 2.026 2.431 2.715 3.326
38 1.304 1.686 2.024 2.429 2.712 3.319
39 1.304 1.685 2.023 2.426 2.708 3.313
40 1.303 1.684 2.021 2.423 2.704 3.307
41 1.303 1.683 2.020 2.421 2.701 3.301
42 1.302 1.682 2.018 2.418 2.698 3.296
43 1.302 1.681 2.017 2.416 2.695 3.291
44 1.301 1.680 2.015 2.414 2.692 3.286
45 1.301 1.679 2.014 2.412 2.690 3.281
46 1.300 1.679 2.013 2.410 2.687 3.277
47 1.300 1.678 2.012 2.408 2.685 3.273
48 1.299 1.677 2.011 2.407 2.682 3.269
49 1.299 1.677 2.010 2.405 2.680 3.265
50 1.299 1.676 2.009 2.403 2.678 3.261
51 1.298 1.675 2.008 2.402 2.676 3.258
52 1.298 1.675 2.007 2.400 2.674 3.255
53 1.298 1.674 2.006 2.399 2.672 3.251
54 1.297 1.674 2.005 2.397 2.670 3.248
55 1.297 1.673 2.004 2.396 2.668 3.245
56 1.297 1.673 2.003 2.395 2.667 3.242
57 1.297 1.672 2.002 2.394 2.665 3.239
58 1.296 1.672 2.002 2.392 2.663 3.237
59 1.296 1.671 2.001 2.391 2.662 3.234
60 1.296 1.671 2.000 2.390 2.660 3.232
61 1.296 1.670 2.000 2.389 2.659 3.229
62 1.295 1.670 1.999 2.388 2.657 3.227
63 1.295 1.669 1.998 2.387 2.656 3.225
64 1.295 1.669 1.998 2.386 2.655 3.223
65 1.295 1.669 1.997 2.385 2.654 3.220
66 1.295 1.668 1.997 2.384 2.652 3.218
67 1.294 1.668 1.996 2.383 2.651 3.216
68 1.294 1.668 1.995 2.382 2.650 3.214
69 1.294 1.667 1.995 2.382 2.649 3.213
70 1.294 1.667 1.994 2.381 2.648 3.211
71 1.294 1.667 1.994 2.380 2.647 3.209
72 1.293 1.666 1.993 2.379 2.646 3.207
73 1.293 1.666 1.993 2.379 2.645 3.206
74 1.293 1.666 1.993 2.378 2.644 3.204
75 1.293 1.665 1.992 2.377 2.643 3.202
76 1.293 1.665 1.992 2.376 2.642 3.201
77 1.293 1.665 1.991 2.376 2.641 3.199
78 1.292 1.665 1.991 2.375 2.640 3.198
79 1.292 1.664 1.990 2.374 2.640 3.197
80 1.292 1.664 1.990 2.374 2.639 3.195
81 1.292 1.664 1.990 2.373 2.638 3.194
82 1.292 1.664 1.989 2.373 2.637 3.193
83 1.292 1.663 1.989 2.372 2.636 3.191
84 1.292 1.663 1.989 2.372 2.636 3.190
85 1.292 1.663 1.988 2.371 2.635 3.189
86 1.291 1.663 1.988 2.370 2.634 3.188
87 1.291 1.663 1.988 2.370 2.634 3.187
88 1.291 1.662 1.987 2.369 2.633 3.185
89 1.291 1.662 1.987 2.369 2.632 3.184
90 1.291 1.662 1.987 2.368 2.632 3.183
91 1.291 1.662 1.986 2.368 2.631 3.182
92 1.291 1.662 1.986 2.368 2.630 3.181
93 1.291 1.661 1.986 2.367 2.630 3.180
94 1.291 1.661 1.986 2.367 2.629 3.179
95 1.291 1.661 1.985 2.366 2.629 3.178
96 1.290 1.661 1.985 2.366 2.628 3.177
97 1.290 1.661 1.985 2.365 2.627 3.176
98 1.290 1.661 1.984 2.365 2.627 3.175
99 1.290 1.660 1.984 2.365 2.626 3.175
100 1.290 1.660 1.984 2.364 2.626 3.174
inf 1.282 1.645 1.960 2.326 2.576 3.090
];
if n<=100,
t_crit=t_crit(n,ncol);
else
t_crit=t_crit(end,ncol);
end

102
matlab/gsa/teff.m
View File

@ -1,51 +1,51 @@
function [yt, j0, ir, ic]=teff(T,Nsam,istable)
%
% [yt, j0, ir, ic]=teff(T,Nsam,istable)
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
ndim = (length(size(T)));
if ndim==3,
if nargin==1,
Nsam=size(T,3);
istable = [1:Nsam]';
end
tmax=max(T,[],3);
tmin=min(T,[],3);
[ir, ic]=(find( (tmax-tmin)>1.e-8));
j0 = length(ir);
yt=zeros(Nsam, j0);
for j=1:j0,
y0=squeeze(T(ir(j),ic(j),:));
%y1=ones(size(lpmat,1),1)*NaN;
y1=ones(Nsam,1)*NaN;
y1(istable,1)=y0;
yt(:,j)=y1;
end
else
tmax=max(T,[],2);
tmin=min(T,[],2);
ir=(find( (tmax-tmin)>1.e-8));
j0 = length(ir);
yt=NaN(Nsam, j0);
yt(istable,:)=T(ir,:)';
end
%clear y0 y1;
function [yt, j0, ir, ic]=teff(T,Nsam,istable)
%
% [yt, j0, ir, ic]=teff(T,Nsam,istable)
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
ndim = (length(size(T)));
if ndim==3,
if nargin==1,
Nsam=size(T,3);
istable = [1:Nsam]';
end
tmax=max(T,[],3);
tmin=min(T,[],3);
[ir, ic]=(find( (tmax-tmin)>1.e-8));
j0 = length(ir);
yt=zeros(Nsam, j0);
for j=1:j0,
y0=squeeze(T(ir(j),ic(j),:));
%y1=ones(size(lpmat,1),1)*NaN;
y1=ones(Nsam,1)*NaN;
y1(istable,1)=y0;
yt(:,j)=y1;
end
else
tmax=max(T,[],2);
tmin=min(T,[],2);
ir=(find( (tmax-tmin)>1.e-8));
j0 = length(ir);
yt=NaN(Nsam, j0);
yt(istable,:)=T(ir,:)';
end
%clear y0 y1;

86
matlab/gsa/thet2tau.m
View File

@ -1,44 +1,44 @@
function tau = thet2tau(params, indx, indexo, flagmoments,mf,nlags,useautocorr)
global M_ oo_ options_
if nargin==1,
indx = [1:M_.param_nbr];
indexo = [];
end
if nargin<4,
flagmoments=0;
end
if nargin<7 | isempty(useautocorr),
useautocorr=0;
end
M_.params(indx) = params(length(indexo)+1:end);
if ~isempty(indexo)
M_.Sigma_e(indexo,indexo) = diag(params(1:length(indexo)).^2);
end
[A,B,plouf,plouf,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
if flagmoments==0,
tau = [oo_.dr.ys(oo_.dr.order_var); A(:); dyn_vech(B*M_.Sigma_e*B')];
else
GAM = lyapunov_symm(A,B*M_.Sigma_e*B',options_.qz_criterium,options_.lyapunov_complex_threshold);
k = find(abs(GAM) < 1e-12);
GAM(k) = 0;
if useautocorr,
sy = sqrt(diag(GAM));
sy = sy*sy';
sy0 = sy-diag(diag(sy))+eye(length(sy));
dum = GAM./sy0;
tau = dyn_vech(dum(mf,mf));
else
tau = dyn_vech(GAM(mf,mf));
end
for ii = 1:nlags
dum = A^(ii)*GAM;
if useautocorr,
dum = dum./sy;
end
tau = [tau;vec(dum(mf,mf))];
end
tau = [ oo_.dr.ys(oo_.dr.order_var(mf)); tau];
function tau = thet2tau(params, indx, indexo, flagmoments,mf,nlags,useautocorr)
global M_ oo_ options_
if nargin==1,
indx = [1:M_.param_nbr];
indexo = [];
end
if nargin<4,
flagmoments=0;
end
if nargin<7 | isempty(useautocorr),
useautocorr=0;
end
M_.params(indx) = params(length(indexo)+1:end);
if ~isempty(indexo)
M_.Sigma_e(indexo,indexo) = diag(params(1:length(indexo)).^2);
end
[A,B,plouf,plouf,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
if flagmoments==0,
tau = [oo_.dr.ys(oo_.dr.order_var); A(:); dyn_vech(B*M_.Sigma_e*B')];
else
GAM = lyapunov_symm(A,B*M_.Sigma_e*B',options_.qz_criterium,options_.lyapunov_complex_threshold);
k = find(abs(GAM) < 1e-12);
GAM(k) = 0;
if useautocorr,
sy = sqrt(diag(GAM));
sy = sy*sy';
sy0 = sy-diag(diag(sy))+eye(length(sy));
dum = GAM./sy0;
tau = dyn_vech(dum(mf,mf));
else
tau = dyn_vech(GAM(mf,mf));
end
for ii = 1:nlags
dum = A^(ii)*GAM;
if useautocorr,
dum = dum./sy;
end
tau = [tau;vec(dum(mf,mf))];
end
tau = [ oo_.dr.ys(oo_.dr.order_var(mf)); tau];
end

50
matlab/gsa/trank.m
View File

@ -1,25 +1,25 @@
function yr = trank(y);
% yr = trank(y);
% yr is the rank transformation of y
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
[nr, nc] = size(y);
for j=1:nc,
[dum, is]=sort(y(:,j));
yr(is,j)=[1:nr]'./nr;
end
function yr = trank(y);
% yr = trank(y);
% yr is the rank transformation of y
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%
[nr, nc] = size(y);
for j=1:nc,
[dum, is]=sort(y(:,j));
yr(is,j)=[1:nr]'./nr;
end

200
tests/gsa/data_ca1.m
View File

@ -1,100 +1,100 @@
data = [0.928467646476 11.8716889412 20 0.418037507392 0.227382377518 ...
-0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
-0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
-0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
-0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
-0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
-0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
-0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
-0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
-0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
-0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
-0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
-1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
-0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
-0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
-0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
];
data = reshape(data,5,86)';
y_obs = data(:,1);
pie_obs = data(:,2);
R_obs = data(:,3);
de = data(:,4);
dq = data(:,5);
%Country: Canada
%Sample Range: 1981:2 to 2002:3
%Observations: 86
%Variables: Real GDP Growth [%], Inflation [annualized %], Nom Rate [%],
% Exchange Rate Change [%], Terms of Trade Change [%]
data = [0.928467646476 11.8716889412 20 0.418037507392 0.227382377518 ...
-0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
-0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
-0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
-0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
-0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
-0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
-0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
-0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
-0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
-0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
-0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
-1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
-0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
-0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
-0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
];
data = reshape(data,5,86)';
y_obs = data(:,1);
pie_obs = data(:,2);
R_obs = data(:,3);
de = data(:,4);
dq = data(:,5);
%Country: Canada
%Sample Range: 1981:2 to 2002:3
%Observations: 86
%Variables: Real GDP Growth [%], Inflation [annualized %], Nom Rate [%],
% Exchange Rate Change [%], Terms of Trade Change [%]

452
tests/gsa/ls2003.mod
View File

@ -1,226 +1,226 @@
var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
varexo e_R e_q e_ys e_pies e_A;
parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
psi1 = 1.54;
psi2 = 0.25;
psi3 = 0.25;
rho_R = 0.5;
alpha = 0.3;
rr = 2.51;
k = 0.5;
tau = 0.5;
rho_q = 0.4;
rho_A = 0.2;
rho_ys = 0.9;
rho_pies = 0.7;
model(linear);
y = y(+1) - (tau +alpha*(2-alpha)*(1-tau))*(R-pie(+1))-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq(+1) + alpha*(2-alpha)*((1-tau)/tau)*(y_s-y_s(+1))-A(+1);
pie = exp(-rr/400)*pie(+1)+alpha*exp(-rr/400)*dq(+1)-alpha*dq+(k/(tau+alpha*(2-alpha)*(1-tau)))*y+k*alpha*(2-alpha)*(1-tau)/(tau*(tau+alpha*(2-alpha)*(1-tau)))*y_s;
pie = de+(1-alpha)*dq+pie_s;
R = rho_R*R(-1)+(1-rho_R)*(psi1*pie+psi2*(y+alpha*(2-alpha)*((1-tau)/tau)*y_s)+psi3*de)+e_R;
dq = rho_q*dq(-1)+e_q;
y_s = rho_ys*y_s(-1)+e_ys;
pie_s = rho_pies*pie_s(-1)+e_pies;
A = rho_A*A(-1)+e_A;
y_obs = y-y(-1)+A;
pie_obs = 4*pie;
R_obs = 4*R;
end;
shocks;
var e_R = 1.25^2;
var e_q = 2.5^2;
var e_A = 1.89;
var e_ys = 1.89;
var e_pies = 1.89;
end;
varobs y_obs R_obs pie_obs dq de;
estimated_params;
psi1 , gamma_pdf,1.5,0.5;
psi2 , gamma_pdf,0.25,0.125;
psi3 , gamma_pdf,0.25,0.125;
rho_R ,beta_pdf,0.5,0.2;
alpha ,beta_pdf,0.3,0.1;
rr ,gamma_pdf,2.5,1;
k , gamma_pdf,0.5,0.25;
tau ,gamma_pdf,0.5,0.2;
rho_q ,beta_pdf,0.4,0.2;
rho_A ,beta_pdf,0.5,0.2;
rho_ys ,beta_pdf,0.8,0.1;
rho_pies,beta_pdf,0.7,0.15;
stderr e_R,inv_gamma_pdf,1.2533,0.6551;
stderr e_q,inv_gamma_pdf,2.5066,1.3103;
stderr e_A,inv_gamma_pdf,1.2533,0.6551;
stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
stderr e_pies,inv_gamma_pdf,1.88,0.9827;
end;
disp(' ');
disp('NOW I DO STABILITY MAPPING and prepare sample for Reduced form Mapping');
disp(' ');
disp('Press ENTER to continue'); pause(5);
dynare_sensitivity(redform=1, //create sample of reduced form coefficients
alpha2_stab=0.4,
ksstat=0);
// NOTE: since namendo is emppty by default,
// this call does not perform the mapping of reduced form coefficient: just prepares the sample
/*
disp(' ');
disp('ANALYSIS OF REDUCED FORM COEFFICIENTS');
disp(' ');
disp('Press ENTER to continue'); pause(5);
dynare_sensitivity(load_stab=1, // loead previously generated sample analysed for stability
redform=1, // do the reduced form mapping
logtrans_redform=1, // estimate log-transformed reduced form coefficients (default=0)
namendo=(pie,R), // evaluate relationships for pie and R (namendo=(:) for all variables)
namexo=(e_R), // evaluate relationships with exogenous e_R (use namexo=(:) for all shocks)
namlagendo=(R), // evaluate relationships with lagged R (use namlagendo=(:) for all lagged endogenous)
stab=0 // don't repeat again the stability mapping
);
*/
disp(' ');
disp('THE PREVIOUS TWO CALLS COULD BE DONE TOGETHER');
disp('BY USING THE COMBINED CALL');
disp(' ');
disp('dynare_sensitivity(alpha2_stab=0.4, ksstat=0, redform=1,')
disp('logtrans_redform=1, namendo=(pie,R), namexo=(e_R), namlagendo=(R));')
disp(' ');
disp('Press ENTER to continue'); pause(5);
//dynare_sensitivity(
//alpha2_stab=0.4,
//ksstat=0,
//redform=1, //create sample of reduced form coefficients
//logtrans_redform=1, // estimate log-transformed reduced form coefficients (default=0)
//namendo=(pie,R), // evaluate relationships for pie and R (namendo=(:) for all variables)
//namexo=(e_R), // evaluate relationships with exogenous e_R (use namexo=(:) for all shocks)
//namlagendo=(R) // evaluate relationships with lagged R (use namlagendo=(:) for all lagged endogenous)
//);
disp(' ');
disp('MC FILTERING(rmse=1), TO MAP THE FIT FROM PRIORS');
disp('Press ENTER to continue'); pause(5);
dynare_sensitivity(datafile=data_ca1,first_obs=8,nobs=79,prefilter=1, // also presample=2,loglinear, are admissible
load_stab=1, // load prior sample
istart_rmse=2, //start computing rmse from second observation (i.e. rmse does not inlude initial big error)
stab=0, // don't plot again stability analysis results
rmse=1, // do rmse analysis
pfilt_rmse=0.1, // filtering criterion, i.e. filter the best 10 percent rmse's
alpha2_rmse=0.3, // critical value for correlations in the rmse filterting analysis:
// if ==1, means no corrleation analysis done
alpha_rmse=1 // critical value for smirnov statistics of filtered samples
// if ==1, no smornov analysis is done
);
disp(' ');
disp('THE PREVIOUS THREE CALLS COULD BE DONE TOGETHER');
disp('BY USING THE COMBINED CALL');
disp(' ');
disp('dynare_sensitivity(alpha2_stab=0.4, ksstat=0, redform=1,')
disp('logtrans_redform=1, namendo=(pie,R), namexo=(e_R), namlagendo=(R),')
disp('datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,')
disp('istart_rmse=2, rmse=1, pfilt_rmse=0.1, alpha2_rmse=0.3, alpha_rmse=1);')
disp(' ');
disp('Press ENTER to continue'); pause(5);
//dynare_sensitivity(
//alpha2_stab=0.4,
//ksstat=0,
//redform=1, //create sample of reduced form coefficients
//logtrans_redform=1, // estimate log-transformed reduced form coefficients (default=0)
//namendo=(pie,R), // evaluate relationships for pie and R (namendo=(:) for all variables)
//namexo=(e_R), // evaluate relationships with exogenous e_R (use namexo=(:) for all shocks)
//namlagendo=(R), // evaluate relationships with lagged R (use namlagendo=(:) for all lagged endogenous)
//datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
//istart_rmse=2, //start computing rmse from second observation (i.e. rmse does not inlude initial big error)
//rmse=1, // do rmse analysis
//pfilt_rmse=0.1, // filtering criterion, i.e. filter the best 10 percent rmse's
//alpha2_rmse=0.3, // critical value for correlations in the rmse filterting analysis:
// // if ==1, means no corrleation analysis done
//alpha_rmse=1 // critical value for smirnov statistics of filtered samples
// // if ==1, no smirnov sensitivity analysis is done
//);
disp(' ');
disp('I ESTIMATE THE MODEL');
disp(' ');
disp('Press ENTER to continue'); pause(5);
// run this to generate posterior mode and Metropolis files if not yet done
estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=2,
prefilter=1,mh_jscale=0.5,mh_replic=5000, mode_compute=4, nograph, mh_drop=0.6,
bayesian_irf, filtered_vars, smoother) y_obs R_obs pie_obs dq de;
// run this to produce posterior samples of filtered, smoothed and irf variables, if not yet done
//estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=2,prefilter=1,mh_jscale=0.3,
// mh_replic=0, mode_file=ls2003_mode, mode_compute=0, nograph, load_mh_file, bayesian_irf,
// filtered_vars, smoother, mh_drop=0.6);
disp(' ');
disp('WE DO STABILITY MAPPING AGAIN, BUT FOR MULTIVARIATE SAMPLE AT THE POSTERIOR MODE (or ML) and Hessian (pprior=0 & ppost=0)');
disp('Typical for ML estimation, also feasible for posterior mode');
disp(' ');
disp('Press ENTER to continue'); pause(5);
dynare_sensitivity(pprior=0,Nsam=2048,alpha2_stab=0.4,
mode_file=ls2003_mode // specifies the mode file where the mode and Hessian are stored
);
disp(' ');
disp('RMSE ANALYSIS FOR MULTIVARIATE SAMPLE AT THE POSTERIOR MODE');
disp(' ');
disp('Press ENTER to continue'); pause(5);
dynare_sensitivity(mode_file=ls2003_mode,
datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
pprior=0,
stab=0,
rmse=1,
alpha2_rmse=1, // no correlation analysis
alpha_rmse=1 // no Smirnov sensitivity analysis
);
disp(' ');
disp('THE LAST TWO CALLS COULD BE DONE TOGETHER');
disp('BY USING THE COMBINED CALL');
disp(' ');
disp('dynare_sensitivity(pprior=0,Nsam=2048,alpha2_stab=0.4,mode_file=ls2003_mode,')
disp('datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,')
disp('rmse=1, alpha2_rmse=1, alpha_rmse=1);')
disp(' ');
disp('Press ENTER to continue'); pause(5);
//dynare_sensitivity(pprior=0,Nsam=2048,alpha2_stab=0.4,mode_file=ls2003_mode,
//datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
//rmse=1,
//alpha2_rmse=1, // no correlation analysis
//alpha_rmse=1 // no Smirnov sensitivity analysis
//);
/*
disp(' ');
disp('RMSE ANALYSIS FOR POSTERIOR MCMC sample (ppost=1)');
disp('Needs a call to dynare_estimation to load all MH environment');
disp('Press ENTER to continue'); pause(5);
estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=2, mode_file=ls2003_mode, load_mh_file,
prefilter=1,mh_jscale=0.5,mh_replic=0, mode_compute=0, nograph, mh_drop=0.6);
dynare_sensitivity(stab=0, // no need for stability analysis since the posterior sample is surely OK
rmse=1,ppost=1,alpha2_rmse=1,alpha_rmse=1);
*/
var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
varexo e_R e_q e_ys e_pies e_A;
parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
psi1 = 1.54;
psi2 = 0.25;
psi3 = 0.25;
rho_R = 0.5;
alpha = 0.3;
rr = 2.51;
k = 0.5;
tau = 0.5;
rho_q = 0.4;
rho_A = 0.2;
rho_ys = 0.9;
rho_pies = 0.7;
model(linear);
y = y(+1) - (tau +alpha*(2-alpha)*(1-tau))*(R-pie(+1))-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq(+1) + alpha*(2-alpha)*((1-tau)/tau)*(y_s-y_s(+1))-A(+1);
pie = exp(-rr/400)*pie(+1)+alpha*exp(-rr/400)*dq(+1)-alpha*dq+(k/(tau+alpha*(2-alpha)*(1-tau)))*y+k*alpha*(2-alpha)*(1-tau)/(tau*(tau+alpha*(2-alpha)*(1-tau)))*y_s;
pie = de+(1-alpha)*dq+pie_s;
R = rho_R*R(-1)+(1-rho_R)*(psi1*pie+psi2*(y+alpha*(2-alpha)*((1-tau)/tau)*y_s)+psi3*de)+e_R;
dq = rho_q*dq(-1)+e_q;
y_s = rho_ys*y_s(-1)+e_ys;
pie_s = rho_pies*pie_s(-1)+e_pies;
A = rho_A*A(-1)+e_A;
y_obs = y-y(-1)+A;
pie_obs = 4*pie;
R_obs = 4*R;
end;
shocks;
var e_R = 1.25^2;
var e_q = 2.5^2;
var e_A = 1.89;
var e_ys = 1.89;
var e_pies = 1.89;
end;
varobs y_obs R_obs pie_obs dq de;
estimated_params;
psi1 , gamma_pdf,1.5,0.5;
psi2 , gamma_pdf,0.25,0.125;
psi3 , gamma_pdf,0.25,0.125;
rho_R ,beta_pdf,0.5,0.2;
alpha ,beta_pdf,0.3,0.1;
rr ,gamma_pdf,2.5,1;
k , gamma_pdf,0.5,0.25;
tau ,gamma_pdf,0.5,0.2;
rho_q ,beta_pdf,0.4,0.2;
rho_A ,beta_pdf,0.5,0.2;
rho_ys ,beta_pdf,0.8,0.1;
rho_pies,beta_pdf,0.7,0.15;
stderr e_R,inv_gamma_pdf,1.2533,0.6551;
stderr e_q,inv_gamma_pdf,2.5066,1.3103;
stderr e_A,inv_gamma_pdf,1.2533,0.6551;
stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
stderr e_pies,inv_gamma_pdf,1.88,0.9827;
end;
disp(' ');
disp('NOW I DO STABILITY MAPPING and prepare sample for Reduced form Mapping');
disp(' ');
disp('Press ENTER to continue'); pause(5);
dynare_sensitivity(redform=1, //create sample of reduced form coefficients
alpha2_stab=0.4,
ksstat=0);
// NOTE: since namendo is emppty by default,
// this call does not perform the mapping of reduced form coefficient: just prepares the sample
/*
disp(' ');
disp('ANALYSIS OF REDUCED FORM COEFFICIENTS');
disp(' ');
disp('Press ENTER to continue'); pause(5);
dynare_sensitivity(load_stab=1, // loead previously generated sample analysed for stability
redform=1, // do the reduced form mapping
logtrans_redform=1, // estimate log-transformed reduced form coefficients (default=0)
namendo=(pie,R), // evaluate relationships for pie and R (namendo=(:) for all variables)
namexo=(e_R), // evaluate relationships with exogenous e_R (use namexo=(:) for all shocks)
namlagendo=(R), // evaluate relationships with lagged R (use namlagendo=(:) for all lagged endogenous)
stab=0 // don't repeat again the stability mapping
);
*/
disp(' ');
disp('THE PREVIOUS TWO CALLS COULD BE DONE TOGETHER');
disp('BY USING THE COMBINED CALL');
disp(' ');
disp('dynare_sensitivity(alpha2_stab=0.4, ksstat=0, redform=1,')
disp('logtrans_redform=1, namendo=(pie,R), namexo=(e_R), namlagendo=(R));')
disp(' ');
disp('Press ENTER to continue'); pause(5);
//dynare_sensitivity(
//alpha2_stab=0.4,
//ksstat=0,
//redform=1, //create sample of reduced form coefficients
//logtrans_redform=1, // estimate log-transformed reduced form coefficients (default=0)
//namendo=(pie,R), // evaluate relationships for pie and R (namendo=(:) for all variables)
//namexo=(e_R), // evaluate relationships with exogenous e_R (use namexo=(:) for all shocks)
//namlagendo=(R) // evaluate relationships with lagged R (use namlagendo=(:) for all lagged endogenous)
//);
disp(' ');
disp('MC FILTERING(rmse=1), TO MAP THE FIT FROM PRIORS');
disp('Press ENTER to continue'); pause(5);
dynare_sensitivity(datafile=data_ca1,first_obs=8,nobs=79,prefilter=1, // also presample=2,loglinear, are admissible
load_stab=1, // load prior sample
istart_rmse=2, //start computing rmse from second observation (i.e. rmse does not inlude initial big error)
stab=0, // don't plot again stability analysis results
rmse=1, // do rmse analysis
pfilt_rmse=0.1, // filtering criterion, i.e. filter the best 10 percent rmse's
alpha2_rmse=0.3, // critical value for correlations in the rmse filterting analysis:
// if ==1, means no corrleation analysis done
alpha_rmse=1 // critical value for smirnov statistics of filtered samples
// if ==1, no smornov analysis is done
);
disp(' ');
disp('THE PREVIOUS THREE CALLS COULD BE DONE TOGETHER');
disp('BY USING THE COMBINED CALL');
disp(' ');
disp('dynare_sensitivity(alpha2_stab=0.4, ksstat=0, redform=1,')
disp('logtrans_redform=1, namendo=(pie,R), namexo=(e_R), namlagendo=(R),')
disp('datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,')
disp('istart_rmse=2, rmse=1, pfilt_rmse=0.1, alpha2_rmse=0.3, alpha_rmse=1);')
disp(' ');
disp('Press ENTER to continue'); pause(5);
//dynare_sensitivity(
//alpha2_stab=0.4,
//ksstat=0,
//redform=1, //create sample of reduced form coefficients
//logtrans_redform=1, // estimate log-transformed reduced form coefficients (default=0)
//namendo=(pie,R), // evaluate relationships for pie and R (namendo=(:) for all variables)
//namexo=(e_R), // evaluate relationships with exogenous e_R (use namexo=(:) for all shocks)
//namlagendo=(R), // evaluate relationships with lagged R (use namlagendo=(:) for all lagged endogenous)
//datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
//istart_rmse=2, //start computing rmse from second observation (i.e. rmse does not inlude initial big error)
//rmse=1, // do rmse analysis
//pfilt_rmse=0.1, // filtering criterion, i.e. filter the best 10 percent rmse's
//alpha2_rmse=0.3, // critical value for correlations in the rmse filterting analysis:
// // if ==1, means no corrleation analysis done
//alpha_rmse=1 // critical value for smirnov statistics of filtered samples
// // if ==1, no smirnov sensitivity analysis is done
//);
disp(' ');
disp('I ESTIMATE THE MODEL');
disp(' ');
disp('Press ENTER to continue'); pause(5);
// run this to generate posterior mode and Metropolis files if not yet done
estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=2,
prefilter=1,mh_jscale=0.5,mh_replic=5000, mode_compute=4, nograph, mh_drop=0.6,
bayesian_irf, filtered_vars, smoother) y_obs R_obs pie_obs dq de;
// run this to produce posterior samples of filtered, smoothed and irf variables, if not yet done
//estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=2,prefilter=1,mh_jscale=0.3,
// mh_replic=0, mode_file=ls2003_mode, mode_compute=0, nograph, load_mh_file, bayesian_irf,
// filtered_vars, smoother, mh_drop=0.6);
disp(' ');
disp('WE DO STABILITY MAPPING AGAIN, BUT FOR MULTIVARIATE SAMPLE AT THE POSTERIOR MODE (or ML) and Hessian (pprior=0 & ppost=0)');
disp('Typical for ML estimation, also feasible for posterior mode');
disp(' ');
disp('Press ENTER to continue'); pause(5);
dynare_sensitivity(pprior=0,Nsam=2048,alpha2_stab=0.4,
mode_file=ls2003_mode // specifies the mode file where the mode and Hessian are stored
);
disp(' ');
disp('RMSE ANALYSIS FOR MULTIVARIATE SAMPLE AT THE POSTERIOR MODE');
disp(' ');
disp('Press ENTER to continue'); pause(5);
dynare_sensitivity(mode_file=ls2003_mode,
datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
pprior=0,
stab=0,
rmse=1,
alpha2_rmse=1, // no correlation analysis
alpha_rmse=1 // no Smirnov sensitivity analysis
);
disp(' ');
disp('THE LAST TWO CALLS COULD BE DONE TOGETHER');
disp('BY USING THE COMBINED CALL');
disp(' ');
disp('dynare_sensitivity(pprior=0,Nsam=2048,alpha2_stab=0.4,mode_file=ls2003_mode,')
disp('datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,')
disp('rmse=1, alpha2_rmse=1, alpha_rmse=1);')
disp(' ');
disp('Press ENTER to continue'); pause(5);
//dynare_sensitivity(pprior=0,Nsam=2048,alpha2_stab=0.4,mode_file=ls2003_mode,
//datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
//rmse=1,
//alpha2_rmse=1, // no correlation analysis
//alpha_rmse=1 // no Smirnov sensitivity analysis
//);
/*
disp(' ');
disp('RMSE ANALYSIS FOR POSTERIOR MCMC sample (ppost=1)');
disp('Needs a call to dynare_estimation to load all MH environment');
disp('Press ENTER to continue'); pause(5);
estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=2, mode_file=ls2003_mode, load_mh_file,
prefilter=1,mh_jscale=0.5,mh_replic=0, mode_compute=0, nograph, mh_drop=0.6);
dynare_sensitivity(stab=0, // no need for stability analysis since the posterior sample is surely OK
rmse=1,ppost=1,alpha2_rmse=1,alpha_rmse=1);
*/

176
tests/gsa/ls2003ide.mod
View File

@ -1,88 +1,88 @@
var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
varexo e_R e_q e_ys e_pies e_A;
parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
psi1 = 1.54;
psi2 = 0.25;
psi3 = 0.25;
rho_R = 0.5;
alpha = 0.3;
rr = 2.51;
k = 0.5;
tau = 0.5;
rho_q = 0.4;
rho_A = 0.2;
rho_ys = 0.9;
rho_pies = 0.7;
model(linear);
y = y(+1) - (tau +alpha*(2-alpha)*(1-tau))*(R-pie(+1))-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq(+1) + alpha*(2-alpha)*((1-tau)/tau)*(y_s-y_s(+1))-A(+1);
pie = exp(-rr/400)*pie(+1)+alpha*exp(-rr/400)*dq(+1)-alpha*dq+(k/(tau+alpha*(2-alpha)*(1-tau)))*y+k*alpha*(2-alpha)*(1-tau)/(tau*(tau+alpha*(2-alpha)*(1-tau)))*y_s;
pie = de+(1-alpha)*dq+pie_s;
R = rho_R*R(-1)+(1-rho_R)*(psi1*pie+psi2*(y+alpha*(2-alpha)*((1-tau)/tau)*y_s)+psi3*de)+e_R;
dq = rho_q*dq(-1)+e_q;
y_s = rho_ys*y_s(-1)+e_ys;
pie_s = rho_pies*pie_s(-1)+e_pies;
A = rho_A*A(-1)+e_A;
y_obs = y-y(-1)+A;
pie_obs = 4*pie;
R_obs = 4*R;
end;
shocks;
var e_R = 1.25^2;
var e_q = 2.5^2;
var e_A = 1.89;
var e_ys = 1.89;
var e_pies = 1.89;
end;
varobs y_obs R_obs pie_obs dq de;
estimated_params;
psi1 , gamma_pdf,1.5,0.5;
psi2 , gamma_pdf,0.25,0.125;
psi3 , gamma_pdf,0.25,0.125;
rho_R ,beta_pdf,0.5,0.2;
alpha ,beta_pdf,0.3,0.1;
rr ,gamma_pdf,2.5,1;
k , gamma_pdf,0.5,0.25;
tau ,gamma_pdf,0.5,0.2;
rho_q ,beta_pdf,0.4,0.2;
rho_A ,beta_pdf,0.5,0.2;
rho_ys ,beta_pdf,0.8,0.1;
rho_pies,beta_pdf,0.7,0.15;
stderr e_R,inv_gamma_pdf,1.2533,0.6551;
stderr e_q,inv_gamma_pdf,2.5066,1.3103;
stderr e_A,inv_gamma_pdf,1.2533,0.6551;
stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
stderr e_pies,inv_gamma_pdf,1.88,0.9827;
end;
disp('CREATE SCREENING SAMPLE, CHECK FOR STABILITY AND PERFORM A SCREENING FOR IDENTIFICATION ANALYSIS');
disp('TYPE II ERRORS')
disp(' ')
disp('PRESS ENTER TO CONTUNUE');
pause;
dynare_sensitivity(identification=1, morris_nliv=6, morris_ntra=50);
disp('CREATE MC SAMPLE, CHECK FOR STABILITY AND PERFORM IDENTIFICATION ANALYSIS');
disp('TYPE I (main effects)')
disp(' ')
disp('PRESS ENTER TO CONTUNUE');
pause;
dynare_sensitivity(identification=1, morris=0);
disp('USE PREVIOUS MC SAMPLE AND PERFORM IDENTIFICATION ANALYSIS');
disp('WIth analytic derivatives')
disp(' ')
disp('PRESS ENTER TO CONTUNUE');
pause;
dynare_sensitivity(identification=1, load_stab=1, stab=0, morris=2);
stoch_simul(order=1,irf=40);
var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
varexo e_R e_q e_ys e_pies e_A;
parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
psi1 = 1.54;
psi2 = 0.25;
psi3 = 0.25;
rho_R = 0.5;
alpha = 0.3;
rr = 2.51;
k = 0.5;
tau = 0.5;
rho_q = 0.4;
rho_A = 0.2;
rho_ys = 0.9;
rho_pies = 0.7;
model(linear);
y = y(+1) - (tau +alpha*(2-alpha)*(1-tau))*(R-pie(+1))-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq(+1) + alpha*(2-alpha)*((1-tau)/tau)*(y_s-y_s(+1))-A(+1);
pie = exp(-rr/400)*pie(+1)+alpha*exp(-rr/400)*dq(+1)-alpha*dq+(k/(tau+alpha*(2-alpha)*(1-tau)))*y+k*alpha*(2-alpha)*(1-tau)/(tau*(tau+alpha*(2-alpha)*(1-tau)))*y_s;
pie = de+(1-alpha)*dq+pie_s;
R = rho_R*R(-1)+(1-rho_R)*(psi1*pie+psi2*(y+alpha*(2-alpha)*((1-tau)/tau)*y_s)+psi3*de)+e_R;
dq = rho_q*dq(-1)+e_q;
y_s = rho_ys*y_s(-1)+e_ys;
pie_s = rho_pies*pie_s(-1)+e_pies;
A = rho_A*A(-1)+e_A;
y_obs = y-y(-1)+A;
pie_obs = 4*pie;
R_obs = 4*R;
end;
shocks;
var e_R = 1.25^2;
var e_q = 2.5^2;
var e_A = 1.89;
var e_ys = 1.89;
var e_pies = 1.89;
end;
varobs y_obs R_obs pie_obs dq de;
estimated_params;
psi1 , gamma_pdf,1.5,0.5;
psi2 , gamma_pdf,0.25,0.125;
psi3 , gamma_pdf,0.25,0.125;
rho_R ,beta_pdf,0.5,0.2;
alpha ,beta_pdf,0.3,0.1;
rr ,gamma_pdf,2.5,1;
k , gamma_pdf,0.5,0.25;
tau ,gamma_pdf,0.5,0.2;
rho_q ,beta_pdf,0.4,0.2;
rho_A ,beta_pdf,0.5,0.2;
rho_ys ,beta_pdf,0.8,0.1;
rho_pies,beta_pdf,0.7,0.15;
stderr e_R,inv_gamma_pdf,1.2533,0.6551;
stderr e_q,inv_gamma_pdf,2.5066,1.3103;
stderr e_A,inv_gamma_pdf,1.2533,0.6551;
stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
stderr e_pies,inv_gamma_pdf,1.88,0.9827;
end;
disp('CREATE SCREENING SAMPLE, CHECK FOR STABILITY AND PERFORM A SCREENING FOR IDENTIFICATION ANALYSIS');
disp('TYPE II ERRORS')
disp(' ')
disp('PRESS ENTER TO CONTUNUE');
pause;
dynare_sensitivity(identification=1, morris_nliv=6, morris_ntra=50);
disp('CREATE MC SAMPLE, CHECK FOR STABILITY AND PERFORM IDENTIFICATION ANALYSIS');
disp('TYPE I (main effects)')
disp(' ')
disp('PRESS ENTER TO CONTUNUE');
pause;
dynare_sensitivity(identification=1, morris=0);
disp('USE PREVIOUS MC SAMPLE AND PERFORM IDENTIFICATION ANALYSIS');
disp('WIth analytic derivatives')
disp(' ')
disp('PRESS ENTER TO CONTUNUE');
pause;
dynare_sensitivity(identification=1, load_stab=1, stab=0, morris=2);
stoch_simul(order=1,irf=40);

144
tests/gsa/ls2003scr.mod
View File

@ -1,72 +1,72 @@
var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
varexo e_R e_q e_ys e_pies e_A;
parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
psi1 = 1.54;
psi2 = 0.25;
psi3 = 0.25;
rho_R = 0.5;
alpha = 0.3;
rr = 2.51;
k = 0.5;
tau = 0.5;
rho_q = 0.4;
rho_A = 0.2;
rho_ys = 0.9;
rho_pies = 0.7;
model(linear);
y = y(+1) - (tau +alpha*(2-alpha)*(1-tau))*(R-pie(+1))-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq(+1) + alpha*(2-alpha)*((1-tau)/tau)*(y_s-y_s(+1))-A(+1);
pie = exp(-rr/400)*pie(+1)+alpha*exp(-rr/400)*dq(+1)-alpha*dq+(k/(tau+alpha*(2-alpha)*(1-tau)))*y+k*alpha*(2-alpha)*(1-tau)/(tau*(tau+alpha*(2-alpha)*(1-tau)))*y_s;
pie = de+(1-alpha)*dq+pie_s;
R = rho_R*R(-1)+(1-rho_R)*(psi1*pie+psi2*(y+alpha*(2-alpha)*((1-tau)/tau)*y_s)+psi3*de)+e_R;
dq = rho_q*dq(-1)+e_q;
y_s = rho_ys*y_s(-1)+e_ys;
pie_s = rho_pies*pie_s(-1)+e_pies;
A = rho_A*A(-1)+e_A;
y_obs = y-y(-1)+A;
pie_obs = 4*pie;
R_obs = 4*R;
end;
shocks;
var e_R = 1.25^2;
var e_q = 2.5^2;
var e_A = 1.89;
var e_ys = 1.89;
var e_pies = 1.89;
end;
varobs y_obs R_obs pie_obs dq de;
estimated_params;
psi1 , gamma_pdf,1.5,0.5;
psi2 , gamma_pdf,0.25,0.125;
psi3 , gamma_pdf,0.25,0.125;
rho_R ,beta_pdf,0.5,0.2;
alpha ,beta_pdf,0.3,0.1;
rr ,gamma_pdf,2.5,1;
k , gamma_pdf,0.5,0.25;
tau ,gamma_pdf,0.5,0.2;
rho_q ,beta_pdf,0.4,0.2;
rho_A ,beta_pdf,0.5,0.2;
rho_ys ,beta_pdf,0.8,0.1;
rho_pies,beta_pdf,0.7,0.15;
stderr e_R,inv_gamma_pdf,1.2533,0.6551;
stderr e_q,inv_gamma_pdf,2.5066,1.3103;
stderr e_A,inv_gamma_pdf,1.2533,0.6551;
stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
stderr e_pies,inv_gamma_pdf,1.88,0.9827;
end;
disp('CREATE SCREENING SAMPLE, CHECK FOR STABILITY AND PERFORM SENSITIVITY ANALYSIS');
disp('PRESS ENTER TO CONTUNUE');
pause;
dynare_sensitivity(morris=1, morris_nliv=6, morris_ntra=20, redform=1,
namendo=(:), namexo=(:), namlagendo=(:));
stoch_simul(order=1,irf=40);
var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
varexo e_R e_q e_ys e_pies e_A;
parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
psi1 = 1.54;
psi2 = 0.25;
psi3 = 0.25;
rho_R = 0.5;
alpha = 0.3;
rr = 2.51;
k = 0.5;
tau = 0.5;
rho_q = 0.4;
rho_A = 0.2;
rho_ys = 0.9;
rho_pies = 0.7;
model(linear);
y = y(+1) - (tau +alpha*(2-alpha)*(1-tau))*(R-pie(+1))-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq(+1) + alpha*(2-alpha)*((1-tau)/tau)*(y_s-y_s(+1))-A(+1);
pie = exp(-rr/400)*pie(+1)+alpha*exp(-rr/400)*dq(+1)-alpha*dq+(k/(tau+alpha*(2-alpha)*(1-tau)))*y+k*alpha*(2-alpha)*(1-tau)/(tau*(tau+alpha*(2-alpha)*(1-tau)))*y_s;
pie = de+(1-alpha)*dq+pie_s;
R = rho_R*R(-1)+(1-rho_R)*(psi1*pie+psi2*(y+alpha*(2-alpha)*((1-tau)/tau)*y_s)+psi3*de)+e_R;
dq = rho_q*dq(-1)+e_q;
y_s = rho_ys*y_s(-1)+e_ys;
pie_s = rho_pies*pie_s(-1)+e_pies;
A = rho_A*A(-1)+e_A;
y_obs = y-y(-1)+A;
pie_obs = 4*pie;
R_obs = 4*R;
end;
shocks;
var e_R = 1.25^2;
var e_q = 2.5^2;
var e_A = 1.89;
var e_ys = 1.89;
var e_pies = 1.89;
end;
varobs y_obs R_obs pie_obs dq de;
estimated_params;
psi1 , gamma_pdf,1.5,0.5;
psi2 , gamma_pdf,0.25,0.125;
psi3 , gamma_pdf,0.25,0.125;
rho_R ,beta_pdf,0.5,0.2;
alpha ,beta_pdf,0.3,0.1;
rr ,gamma_pdf,2.5,1;
k , gamma_pdf,0.5,0.25;
tau ,gamma_pdf,0.5,0.2;
rho_q ,beta_pdf,0.4,0.2;
rho_A ,beta_pdf,0.5,0.2;
rho_ys ,beta_pdf,0.8,0.1;
rho_pies,beta_pdf,0.7,0.15;
stderr e_R,inv_gamma_pdf,1.2533,0.6551;
stderr e_q,inv_gamma_pdf,2.5066,1.3103;
stderr e_A,inv_gamma_pdf,1.2533,0.6551;
stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
stderr e_pies,inv_gamma_pdf,1.88,0.9827;
end;
disp('CREATE SCREENING SAMPLE, CHECK FOR STABILITY AND PERFORM SENSITIVITY ANALYSIS');
disp('PRESS ENTER TO CONTUNUE');
pause;
dynare_sensitivity(morris=1, morris_nliv=6, morris_ntra=20, redform=1,
namendo=(:), namexo=(:), namlagendo=(:));
stoch_simul(order=1,irf=40);