EXAMPLE
global GROCERDIR; small=create_model(GROCERDIR+'\data\smallmod.txt') |  |  |
create a model from a a text file
model=create_model(filein)
* filein = the text file of the model
* model = a model tlist, with:
- model('namemod') = a string, the name of the model
- model('name endo') = a string vector, the names of the endogenous variables
- model('name exo') = a string vector, the names of the exogenous variables
- model('name resid') = a string vector, the names of the residuals
- model('name coeff') = a string vector, the names of the coefficients
- model('name param') = a string vector, the names of the parameters
- model('name eq') = a string vector, the names of the equations
- model('equations') = a string vector, the texts of the equations
- model('coeffs') = a tlist, whose type is 'coeffs' with:
model('coeffs')(i) = name of the i-th coeff
- model('params') = a tlist, the equivalent of model('coeffs') for parameters
- model('eq coeffs') = a (ncoeffs x neqs) sparse matrix, with
the (i,j) non zero value corresponding to the coefficient i being present in the equation j
- model('eq endos') = a (nendo x neqs) sparse matrix, with
the (i,j) non zero value corresponding to the endogenous i being present in the equation j
- model('eq exos') = a (nexo x neqs) sparse matrix, with
the (i,j) non zero value corresponding to the exogenous i being present in the equation j
- model('eq resids') = a (nresid x neqs) sparse matrix, with
the (i,j) non zero value corresponding to the residual i being present in the equation j
- model('eq params') = a (nparam x neqs) sparse matrix, with
the (i,j) non zero value corresponding to the parameter i being present in the equation j
- model('lags endos') = a list of vectors, each vector collecting the various lags the corresponding
endogenous variable appears in the model
- model('lags exos') = a list of vectors, each vector collecting the various lags the corresponding
exogenous variable appears in the model
- model('non empty lagged endos') = a vector of integers, collecting the endogenous that is lagged at least in an equation
- model('maxlag') = the lag maximum in the model
- model('names for regressions') = a list of (2 x n_i) matrix that for each equation collects on column 1 the names of the coefficients
in this equation (equivalent to the names of the exogenous variables in an ols estimation)
and in column 2 the derivative of the equation with respect to each coefficient
- model('linearity') = a (neq x 1) vector of booleans, set to %f if the equation is non linear with respect
to its coefficients, %t if it is (or has no coefficients)
- model('transf') = a boolean, set to %t if the model has been transformed for simulation, %f if not
- model('prolog string2run') = a (k x 1) string vector, collecting the equations that will be incorporated in the text
of the function called by function simulate to run the prologue
- model('prolog func txts') = a (k x 1) string vector, collecting the texts of the functions that are to be
solved for equations in the prologue that cannot be solved directly as 'endogenous=rhs'
- model('prolog Jac txts') = a (k x 1) string vector, collecting the texts of the corresponding Jacobians
- model('prolog endo') = a (k x 1) vector of integers, collecting the indexes of the endogenous variables that are calculated by the prologue
- model('prolog eq') = a (k x 1) vector of integers, collecting the indexes of the equations that are solved by the prologue
- model('prolog string2run') = a (m x 1) string vector, collecting the equations that will be incorporated in the text
of the function called by function simulate to run the epilogue
- model('prolog func txts') = a (m x 1) string vector, collecting the texts of the functions that are to be
solved for equations in the epilogue that cannot be solved directly as 'endogenous=rhs'
- model('prolog Jac txts') = a (m x 1) string vector, collecting the texts of the corresponding Jacobians
- model('prolog endo') = a (m x 1) vector of integers, collecting the indexes of the endogenous variables that are calculated by the epilogue
- model('prolog eq') = a (m x 1) vector of integers, collecting the indexes of the equations that are solved by the epilogue
- model('heart func txt') = a (n x 1) string vector,collecting the equations that will be incorporated in
the text of the function called by function simulate to run the heart of the model
- model('heart Jac txt') = a (p x 1) string vector, collecting the equations of the non zero Jacobian matrix values
- model('heart Jac trhs') = a (p x 1) string vector, collecting only the rhs of the equations of the non zero Jacobian matrix values
- model('heart Jac indexes') = a (p x 2) matrix of integers, collecting the corresponding coordinates in the Jacabian matrix
- model('heart endogenous') = a (n x 1) vector of integers, collecting the indexes of the endogenous variables that are calculated by the heart
- model('heart equations') = a (n x 1) vector of integers, collecting the indexes of the equations that are solved by the heart
- model('gs string2run') = a (q x 1) string vector, collecting the equations that will be calculated by the Gauss-Seidel method (if option 'GS' has been entered)
- model('gs func txts') = a (q x 1) string vector, collecting the texts of the functions that are to be solved at each Gsuss-Seidel step
- model('gs Jac txts') = a (q x 1) string vector, collecting the texts of the corresponding Jacobians
global GROCERDIR; small=create_model(GROCERDIR+'\data\smallmod.txt') | | |