v4 matlab:
* added preprocessor support for inverse gamma of type 2 * added support for this distribution in prior_bounds.m and rndprior.m * other cosmetic changes git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1983 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
sebastien
parent
a5b3c829d8
commit
b61c9e6a69
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@ -44,7 +44,8 @@ p4 = bayestopt_.p4;
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truncprior = 10^(-3);
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if pshape(indx) == 1 %/* BETA Prior */
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switch pshape(indx)
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case 1 % Beta prior
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density = inline('((bb-x).^(b-1)).*(x-aa).^(a-1)./(beta(a,b)*(bb-aa)^(a+b-1))','x','a','b','aa','bb');
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mu = (p1(indx)-p3(indx))/(p4(indx)-p3(indx));
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stdd = p2(indx)/(p4(indx)-p3(indx));
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@ -57,7 +58,7 @@ if pshape(indx) == 1 %/* BETA Prior */
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stepsize = (supbound-infbound)/200;
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abscissa = infbound:stepsize:supbound;
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dens = density(abscissa,a,b,aa,bb);
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elseif pshape(indx) == 2 %/* GAMMA PRIOR */
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case 2 % Generalized Gamma prior
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mu = p1(indx)-p3(indx);
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b = p2(indx)^2/mu;
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a = mu/b;
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@ -67,7 +68,7 @@ elseif pshape(indx) == 2 %/* GAMMA PRIOR */
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abscissa = infbound:stepsize:supbound;
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dens = exp(lpdfgam(abscissa,a,b));
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abscissa = abscissa + p3(indx);
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elseif pshape(indx) == 3 %/* GAUSSIAN PRIOR */
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case 3 % Gaussian prior
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density = inline('inv(sqrt(2*pi)*b)*exp(-0.5*((x-a)/b).^2)','x','a','b');
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a = p1(indx);
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b = p2(indx);
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@ -76,7 +77,7 @@ elseif pshape(indx) == 3 %/* GAUSSIAN PRIOR */
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stepsize = (supbound-infbound)/200;
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abscissa = infbound:stepsize:supbound;
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dens = density(abscissa,a,b);
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elseif pshape(indx) == 4 %/* INVGAMMA PRIOR type 1 */
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case 4 % Inverse-gamma of type 1 prior
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density = inline('2*inv(gamma(nu/2))*(x.^(-nu-1))*((s/2)^(nu/2)).*exp(-s./(2*x.^2))','x','s','nu');
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nu = p2(indx);
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s = p1(indx);
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@ -87,7 +88,7 @@ elseif pshape(indx) == 4 %/* INVGAMMA PRIOR type 1 */
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stepsize = (supbound-infbound)/200;
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abscissa = infbound:stepsize:supbound;
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dens = density(abscissa,s,nu);
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elseif pshape(indx) == 5 %/* UNIFORM PRIOR */
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case 5 % Uniform prior
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density = inline('(x.^0)/(b-a)','x','a','b');
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a = p1(indx);
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b = p2(indx);
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@ -96,7 +97,7 @@ elseif pshape(indx) == 5 %/* UNIFORM PRIOR */
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stepsize = (supbound-infbound)/200;
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abscissa = infbound:stepsize:supbound;
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dens = density(abscissa,a,b);
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elseif pshape(indx) == 6 %/* INVGAMMA PRIOR type 2 */
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case 6 % Inverse-gamma of type 2 prior
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density = inline('inv(gamma(nu/2))*(x.^(-.5*(nu+2)))*((s/2)^(nu/2)).*exp(-s./(2*x))','x','s','nu');
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nu = p2(indx);
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s = p1(indx);
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@ -107,6 +108,8 @@ elseif pshape(indx) == 6 %/* INVGAMMA PRIOR type 2 */
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stepsize = (supbound-infbound)/200;
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abscissa = infbound:stepsize:supbound;
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dens = density(abscissa,s,nu);
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otherwise
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error(sprintf('draw_prior_density: unknown distribution shape (index %d, type %d)', indx, pshape(indx)));
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end
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k = [1:length(dens)];
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@ -67,8 +67,10 @@ for i=1:n
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case 5
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bounds(i,1) = p1(i);
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bounds(i,2) = p2(i);
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case 6
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bounds(i,1) = 1/gaminv(1-options_.prior_trunc, p2(i)/2, 2/p1(i));
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bounds(i,2) = 1/gaminv(options_.prior_trunc, p2(i)/2, 2/p1(i));
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otherwise
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bounds(i,1) = -Inf;
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bounds(i,2) = Inf;
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error(sprintf('prior_bounds: unknown distribution shape (index %d, type %d)', i, pshape(i)));
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end
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end
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@ -82,9 +82,7 @@ if init
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% 5: Uniform prior
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% p3(i) and p4(i) are used.
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otherwise
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disp('prior_draw :: Error!')
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disp('Unknown prior shape.')
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return
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error(sprintf('prior_draw: unknown distribution shape (index %d, type %d)', i, pshape(i)));
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end
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pdraw = zeros(npar,1);
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end
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@ -139,6 +137,6 @@ for i = 1:npar
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end
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end
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otherwise
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% Nothing to do here.
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error(sprintf('prior_draw: unknown distribution shape (index %d, type %d)', i, pshape(i)));
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end
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end
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@ -39,7 +39,7 @@ for i=1:length(pmean),
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switch pshape(i)
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case 1 %'beta'
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case 1 % Beta
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mu = (pmean(i)-p3(i))/(p4(i)-p3(i));
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stdd = p2(i)/(p4(i)-p3(i));
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A = (1-mu)*mu^2/stdd^2 - mu;
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@ -47,25 +47,32 @@ for i=1:length(pmean),
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y(1,i) = betarnd(A, B);
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y(1,i) = y(1,i) * (p4(i)-p3(i)) + p3(i);
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case 2 %'gamma'
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case 2 % Generalized gamma
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mu = pmean(i)-p3(i);
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B = p2(i)^2/mu;
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A = mu/B;
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y(1,i) = gamrnd(A, B) + p3(i);
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case 3 %'normal'
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case 3 % Gaussian
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MU = pmean(i);
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SIGMA = p2(i);
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y(1,i) = randn*SIGMA+ MU;
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case 4 %'invgamma'
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case 4 % Inverse-gamma type 1
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nu = p2(i);
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s = p1(i);
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y(1,i) = 1/sqrt(gamrnd(nu/2, 2/s));
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case 5 %'uniform'
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case 5 % Uniform
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y(1,i) = rand*(p2(i)-p1(i)) + p1(i);
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case 6 % Inverse-gamma type 2
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nu = p2(i);
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s = p1(i);
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y(1,i) = 1/gamrnd(nu/2, 2/s);
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otherwise
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error(sprintf('rndprior: unknown distribution shape (index %d, type %d)', i, pshape(i)));
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end
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end
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@ -94,7 +94,7 @@ class ParsingDriver;
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%token HISTVAL HP_FILTER HP_NGRID
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%token INITVAL INITVAL_FILE
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%token <string_val> INT_NUMBER
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%token INV_GAMMA_PDF IRF
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%token INV_GAMMA1_PDF INV_GAMMA2_PDF IRF
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%token KALMAN_ALGO KALMAN_TOL
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%token LAPLACE LCC_COMPILER LIK_ALGO LIK_INIT LINEAR LOAD_MH_FILE LOGLINEAR LU MARKOWITZ MAX
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%token METHOD MH_DROP MH_INIT_SCALE MH_JSCALE MH_MODE MH_NBLOCKS MH_REPLIC MH_RECOVER MIN
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@ -897,10 +897,12 @@ prior : BETA_PDF
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{ $$ = new string("2"); }
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| NORMAL_PDF
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{ $$ = new string("3"); }
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| INV_GAMMA_PDF
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| INV_GAMMA1_PDF
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{ $$ = new string("4"); }
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| UNIFORM_PDF
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{ $$ = new string("5"); }
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| INV_GAMMA2_PDF
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{ $$ = new string("6"); }
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;
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value : { $$ = new string("NaN"); }
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@ -241,9 +241,9 @@ int sigma_e = 0;
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<DYNARE_BLOCK>gamma_pdf {return token::GAMMA_PDF;}
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<DYNARE_BLOCK>beta_pdf {return token::BETA_PDF;}
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<DYNARE_BLOCK>normal_pdf {return token::NORMAL_PDF;}
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<DYNARE_BLOCK>inv_gamma_pdf {return token::INV_GAMMA_PDF;}
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<DYNARE_BLOCK>inv_gamma1_pdf {return token::INV_GAMMA_PDF;}
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<DYNARE_BLOCK>inv_gamma2_pdf {return token::INV_GAMMA_PDF;}
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<DYNARE_BLOCK>inv_gamma_pdf {return token::INV_GAMMA1_PDF;}
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<DYNARE_BLOCK>inv_gamma1_pdf {return token::INV_GAMMA1_PDF;}
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<DYNARE_BLOCK>inv_gamma2_pdf {return token::INV_GAMMA2_PDF;}
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<DYNARE_BLOCK>uniform_pdf {return token::UNIFORM_PDF;}
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<DYNARE_BLOCK>; {return Dynare::parser::token_type (yytext[0]);}
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