model_info.m: fix display of block decomposition
Johannes Pfeifer
parent
1866286fb8
commit
45450651a2
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@ -3428,20 +3428,18 @@ Getting information about the model
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.. command:: model_info ;
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model_info (OPTIONS...);
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|br| This command provides information about the model.
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When used outside the context of the ``block`` option of the ``model`` block,
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|br| This command provides information about the model. By default,
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it will provide a list of predetermined state variables, forward-looking variables,
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and purely static variables.
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When used in conjunction with the ``block`` option of the ``model`` block,
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it displays:
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The command also allows to display information on the dynamic and static
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versions of the block decomposition of the model:
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* The normalization of the model: an endogenous variable is
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attributed to each equation of the model;
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attributed to each equation of the model (the dependent variable);
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* The block structure of the model: for each block ``model_info``
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indicates its type, the equations number and endogenous
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variables belonging to this block.
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indicates its type, size as well as the equation number(s) or name tags and
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endogenous variables belonging to this block.
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There are five different types of blocks depending on the
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@ -3450,37 +3448,37 @@ Getting information about the model
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* ``EVALUATE FORWARD``
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In this case the block contains only equations where the
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endogenous variable attributed to the equation appears
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at current period on the left hand side and where no forward looking
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dependent variable :math:`j` attributed to the equation appears
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contemporaneously on the left hand side and where no forward looking
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endogenous variables appear. The block has the form:
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:math:`y_{j,t} = f_j(y_t, y_{t-1}, \ldots, y_{t-k})`.
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* ``EVALUATE BACKWARD``
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The block contains only equations where the endogenous variable
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attributed to the equation appears at current period on the left hand
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side and where no backward looking endogenous variables
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The block contains only equations where the dependent variable :math:`j`
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attributed to the equation appears contemporaneously on the left hand side
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and where no backward looking endogenous variables
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appear. The block has the form: :math:`y_{j,t} = f_j(y_t,
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y_{t+1}, \ldots, y_{t+k})`.
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* ``SOLVE BACKWARD x``
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The block contains only equations where the endogenous variable
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attributed to the equation does not appear at current period on the
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The block contains only equations where the dependent variable :math:`j`
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attributed to the equation does not appear contemporaneously on the
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left hand side and where no forward looking endogenous
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variables appear. The block has the form: :math:`g_j(y_{j,t},
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y_t, y_{t-1}, \ldots, y_{t-k})=0`. ``x`` is equal to ``SIMPLE``
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if the block has only one equation. If several equations
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y_t, y_{t-1}, \ldots, y_{t-k})=0`. Here, ``x`` denotes the subtype of the block.
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``x`` is equal to ``SIMPLE`` if the block has only one equation. If several equations
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appear in the block, ``x`` is equal to ``COMPLETE``.
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* ``SOLVE FORWARD x``
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The block contains only equations where the endogenous variable
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attributed to the equation does not appear at current period on the
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The block contains only equations where the dependent variable :math:`j`
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attributed to the equation does not appear contemporaneously on the
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left hand side and where no backward looking endogenous
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variables appear. The block has the form: :math:`g_j(y_{j,t},
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y_t, y_{t+1}, \ldots, y_{t+k})=0`. ``x`` is equal to ``SIMPLE``
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if the block has only one equation. If several equations
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y_t, y_{t+1}, \ldots, y_{t+k})=0`. Here, ``x`` denotes the subtype of the block.
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``x`` is equal to ``SIMPLE`` if the block has only one equation. If several equations
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appear in the block, ``x`` is equal to ``COMPLETE``.
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* ``SOLVE TWO BOUNDARIES x``
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@ -3488,22 +3486,24 @@ Getting information about the model
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The block contains equations depending on both forward and
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backward variables. The block looks like: :math:`g_j(y_{j,t},
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y_t, y_{t-1}, \ldots, y_{t-k} ,y_t, y_{t+1}, \ldots,
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y_{t+k})=0`. ``x`` is equal to ``SIMPLE`` if the block has only
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one equation. If several equations appear in the block, ``x`` is
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equal to ``COMPLETE``.
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y_{t+k})=0`. Here, ``x`` denotes the subtype of the block.
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``x`` is equal to ``SIMPLE`` if the block has only one equation. If several equations
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appear in the block, ``x`` is equal to ``COMPLETE``.
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*Options*
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.. option:: static
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.. option:: block_static
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Prints out the block decomposition of the static
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model. Without the ``static`` option, ``model_info`` displays the block
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decomposition of the dynamic model.
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Prints out the block decomposition of the static model.
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.. option:: block_dynamic
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Prints out the block decomposition of the dynamic model.
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.. option:: incidence
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Displays the gross incidence matrix and the reordered incidence
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matrix of the block decomposed model.
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matrix of the block decomposed model for the ``block_dynamic`` or ``block_static`` options.
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.. command:: print_bytecode_dynamic_model ;
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@ -20,29 +20,42 @@ function model_info(options_model_info_)
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global M_;
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static_ = isfield(options_model_info_, 'static') && options_model_info_.static;
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dynamic_ = isfield(options_model_info_, 'block_dynamic') && options_model_info_.block_dynamic;
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static_ = isfield(options_model_info_, 'block_static') && options_model_info_.block_static;
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incidence = isfield(options_model_info_, 'incidence') && options_model_info_.incidence;
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if static_
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fprintf(' Information about %s (static model)\n',M_.fname);
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block_structre_str = 'block_structure_stat';
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nb_leadlag = 1;
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temp_string=sprintf('\nInformation about %s (static model)\n',M_.fname);
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fprintf(temp_string);
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block_structure_str = 'block_structure_stat';
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if ~isfield(M_,'block_structure_stat')
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fprintf('\nmodel_info: block information not present; skipping display.\n')
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return;
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else
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nb_leadlag = 1;
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end
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else
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fprintf(' Information about %s (dynamic model)\n',M_.fname);
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block_structre_str = 'block_structure';
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nb_leadlag = 3;
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temp_string=sprintf('\nInformation about %s (dynamic model)\n',M_.fname);
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fprintf(temp_string);
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block_structure_str = 'block_structure';
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if dynamic_ && isfield(M_,'block_structure')
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fprintf('\nmodel_info: block information not present; skipping display.\n')
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return;
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elseif dynamic_
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nb_leadlag = length([M_.(block_structure_str).incidence.lead_lag]);
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end
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end
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if isfield(M_,block_structre_str)
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if dynamic_ || static_ || incidence %block information requested
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if static_
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block_structure = M_.block_structure_stat;
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else
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block_structure = M_.block_structure;
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end
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fprintf(strcat(' ===================',char(ones(1,length(M_.fname))*'='),'\n\n'));
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fprintf([char(ones(1,length(temp_string))*'='),'\n']);
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nb_blocks=length(block_structure.block);
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fprintf('The model has %d equations and is decomposed in %d blocks as follow:\n',M_.endo_nbr,nb_blocks);
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fprintf('The model has %d equations and is decomposed into %d blocks as follows:\n',M_.endo_nbr,nb_blocks);
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fprintf('================================================================================================================================\n');
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fprintf('| %10s | %10s | %30s | %31s | %31s |\n','Block no','Size','Block Type','Equation','Dependent variable');
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fprintf('|============|============|================================|=================================|=================================|\n');
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@ -62,7 +75,7 @@ if isfield(M_,block_structre_str)
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fprintf('================================================================================================================================\n');
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fprintf('\n');
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if static_
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fprintf('%-30s %s','the variable','is used in the following equations contemporaneously');
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fprintf('%-30s %s','The variable','is used contemporaneously in the following equations:');
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if(size(block_structure.incidence.sparse_IM,1)>0)
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IM=sortrows(block_structure.incidence.sparse_IM,2);
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else
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@ -78,14 +91,14 @@ if isfield(M_,block_structre_str)
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last=IM(i,2);
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end
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fprintf('\n\n');
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else
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else %dynamic model
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for k=1:M_.maximum_endo_lag+M_.maximum_endo_lead+1
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if(k==M_.maximum_endo_lag+1)
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fprintf('%-30s %s','the variable','is used in the following equations Contemporaneously');
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fprintf('%-30s %s','The variable','is used in the following equations contemporaneously');
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elseif(k<M_.maximum_endo_lag+1)
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fprintf('%-30s %s %d','the variable','is used in the following equations with lag ',M_.maximum_endo_lag+1-k);
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fprintf('%-30s %s %d','The variable','is used in the following equations with lag ',M_.maximum_endo_lag+1-k);
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else
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fprintf('%-30s %s %d','the variable','is used in equations with lead ',k-(M_.maximum_endo_lag+1));
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fprintf('%-30s %s %d','The variable','is used in equations with lead ',k-(M_.maximum_endo_lag+1));
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end
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if(size(block_structure.incidence(k).sparse_IM,1)>0)
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IM=sortrows(block_structure.incidence(k).sparse_IM,2);
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@ -104,34 +117,36 @@ if isfield(M_,block_structre_str)
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fprintf('\n\n');
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end
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end
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%printing the gross incidence matrix
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IM_star = char([kron(ones(M_.endo_nbr, M_.endo_nbr-1), double(blanks(3))) double(blanks(M_.endo_nbr)')]);
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for i = 1:nb_leadlag
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n = size(block_structure.incidence(i).sparse_IM,1);
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for j = 1:n
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if ismember(block_structure.incidence(i).sparse_IM(j,2), M_.state_var)
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IM_star(block_structure.incidence(i).sparse_IM(j,1), 3 * (block_structure.incidence(i).sparse_IM(j,2) - 1) + 1) = 'X';
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else
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IM_star(block_structure.incidence(i).sparse_IM(j,1), 3 * (block_structure.incidence(i).sparse_IM(j,2) - 1) + 1) = '1';
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if incidence
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%printing the gross incidence matrix
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IM_star = char([kron(ones(M_.endo_nbr, M_.endo_nbr-1), double(blanks(3))) double(blanks(M_.endo_nbr)')]);
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for i = 1:nb_leadlag
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n = size(block_structure.incidence(i).sparse_IM,1);
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for j = 1:n
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if ismember(block_structure.incidence(i).sparse_IM(j,2), M_.state_var)
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IM_star(block_structure.incidence(i).sparse_IM(j,1), 3 * (block_structure.incidence(i).sparse_IM(j,2) - 1) + 1) = 'X';
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else
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IM_star(block_structure.incidence(i).sparse_IM(j,1), 3 * (block_structure.incidence(i).sparse_IM(j,2) - 1) + 1) = '1';
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end
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end
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end
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end
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seq = 1: M_.endo_nbr;
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blank = [ blanks(cellofchararraymaxlength(M_.endo_names)); blanks(cellofchararraymaxlength(M_.endo_names))];
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for i = 1:M_.endo_nbr
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if i == 1
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var_names = char(blank, M_.endo_names{i});
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else
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var_names = char(var_names, blank, M_.endo_names{i});
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seq = 1: M_.endo_nbr;
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blank = [ blanks(cellofchararraymaxlength(M_.endo_names)); blanks(cellofchararraymaxlength(M_.endo_names))];
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for i = 1:M_.endo_nbr
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if i == 1
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var_names = char(blank, M_.endo_names{i});
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else
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var_names = char(var_names, blank, M_.endo_names{i});
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end
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end
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end
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if incidence
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topp = [char(kron(double(blanks(ceil(log10(M_.endo_nbr)))),ones(cellofchararraymaxlength(M_.endo_names),1))) var_names' ];
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bott = [int2str(seq') blanks(M_.endo_nbr)' blanks(M_.endo_nbr)' IM_star];
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fprintf('\n Gross incidence matrix\n');
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fprintf(' =======================\n');
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disp([topp; bott]);
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fprintf('\nGross incidence matrix\n');
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fprintf('=======================\n');
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disp(topp);
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skipline;
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disp(bott);
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%printing the reordered incidence matrix
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IM_star_reordered = char([kron(ones(M_.endo_nbr, M_.endo_nbr-1), double(blanks(3))) double(blanks(M_.endo_nbr)')]);
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@ -145,12 +160,12 @@ if isfield(M_,block_structre_str)
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cur_block = cur_block + 1;
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end
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if i == 1
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var_names = [blank; M_.endo_names{block_structure.variable_reordered(i)}];
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var_names = char(blank, M_.endo_names{block_structure.variable_reordered(i)});
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else
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if past_block ~= cur_block
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var_names = [var_names; barre_blank; M_.endo_names{block_structure.variable_reordered(i)}];
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var_names = char(var_names, barre_blank, M_.endo_names{block_structure.variable_reordered(i)});
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else
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var_names = [var_names; blank; M_.endo_names{block_structure.variable_reordered(i)}];
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var_names = char(var_names, blank, M_.endo_names{block_structure.variable_reordered(i)});
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end
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end
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end
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@ -174,7 +189,7 @@ if isfield(M_,block_structre_str)
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end
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end
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end
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fprintf('1: non nul element, X: non nul element related to a state variable\n');
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fprintf('\n1: non-null element, X: non-null element related to a state variable\n');
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cur_block = 1;
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i_last = 0;
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@ -203,12 +218,14 @@ if isfield(M_,block_structre_str)
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end
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bott = [int2str(block_structure.equation_reordered') blanks(M_.endo_nbr)' blanks(M_.endo_nbr)' IM_star_reordered];
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fprintf('\n Reordered incidence matrix\n');
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fprintf(' ==========================\n');
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disp([topp; bott]);
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fprintf('1: non nul element, X: non nul element related to a state variable\n');
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fprintf('\nReordered incidence matrix\n');
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fprintf('==========================\n');
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disp(topp);
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skipline;
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disp(bott);
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fprintf('\n1: non-null element, X: non-null element related to a state variable\n');
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end
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else
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else %non-block information
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% print states
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if M_.maximum_endo_lag~=0
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lag_index=find(M_.lead_lag_incidence(1,:));
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@ -263,7 +280,7 @@ function print_line(names,var_index,lead_lag,M_)
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else
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aux_index=find([M_.aux_vars(:).endo_index]==var_index);
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aux_type=M_.aux_vars(aux_index).type;
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if isempty(M_.aux_vars(aux_index).orig_lead_lag)
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if ~isfield(M_.aux_vars(aux_index),'orig_lead_lag') || isempty(M_.aux_vars(aux_index).orig_lead_lag)
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if ismember(aux_type,[1,3])
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str = subst_auxvar(var_index, -1, M_);
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elseif ismember(aux_type,[0,2])
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@ -1 +1 @@
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Subproject commit 21fcfa7758b83216fa5a823c3ed0eb6a66bdf0a0
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Subproject commit 2fd068115155fb33129178829e655f0e5a0f492c
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@ -86,6 +86,8 @@ MODFILES = \
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estimation/univariate/nls/staticmodel.mod \
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estimation/univariate/nls/dynamicmodel1.mod \
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estimation/univariate/nls/dynamicmodel2.mod \
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model_info/NKM_3_back.mod \
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model_info/NKM_3_forward.mod \
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moments/example1_var_decomp.mod \
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moments/example1_bp_test.mod \
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moments/test_AR1_spectral_density.mod \
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@ -0,0 +1,41 @@
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parameters betta sigma phipi phiy omega kappa r_ss;
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var c r ui;
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varexo pai;
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betta = 0.99; % discount rate patient
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sigma = 1.5; % risk aversion param
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omega = 0.75; % calvo param
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phipi = 1.5;
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phiy = 0.5;
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r_ss = 0;
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kappa = (1-omega)*(1-omega*betta)/omega;
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model(linear);
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[name='Euler']
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c = c(+1) - (1/sigma)*(r - r_ss - pai(-2));
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[name='NKPC']
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pai = betta*pai(+2) + kappa*c;
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[name='Taylor rule']
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r = phipi*pai + phiy*c + ui;
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end;
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initval;
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ui = 0;
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c = 0;
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r = 0;
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end;
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steady;
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shocks;
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var pai;
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periods 1,2,3,4,5,6,7,8,9,10;
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values 0.006, 0.005854795, 0.00550196, 0.0052, 0.0048, 0.0043, 0.0037, 0.0028, 0.0022, 0.0015;
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end;
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model_info;
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model_info(block_static,incidence);
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model_info(block_dynamic,incidence);
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@ -0,0 +1,41 @@
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parameters betta sigma phipi phiy omega kappa r_ss;
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var c r ui;
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varexo pai;
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betta = 0.99; % discount rate patient
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sigma = 1.5; % risk aversion param
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omega = 0.75; % calvo param
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phipi = 1.5;
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phiy = 0.5;
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r_ss = 0;
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kappa = (1-omega)*(1-omega*betta)/omega;
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model(linear);
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[name='Euler']
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c = c(+1) - (1/sigma)*(r - r_ss - pai(+1));
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[name='NKPC']
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pai = betta*pai(+1) + kappa*c;
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[name='Taylor rule']
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r = phipi*pai + phiy*c + ui;
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end;
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initval;
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ui = 0;
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c = 0;
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r = 0;
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end;
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steady;
|
||||
|
||||
shocks;
|
||||
var pai;
|
||||
periods 1,2,3,4,5,6,7,8,9,10;
|
||||
values 0.006, 0.005854795, 0.00550196, 0.0052, 0.0048, 0.0043, 0.0037, 0.0028, 0.0022, 0.0015;
|
||||
end;
|
||||
model_info;
|
||||
model_info(block_static,incidence);
|
||||
|
||||
model_info(block_dynamic,incidence);
|
||||
Loading…
Reference in New Issue