Fast Deterministic Simulation

This page documents the model and simul options used to speed-up deterministic simulation in middle and large scale models. Two groups of options are considered:

sparse model options and model_info command

sparse

Dynare performs a block-decomposition of the model and the preprocessor creates two m-files: ModFileName_static.m and ModFileName_dynamic.m containing the code for deterministic and stochastic simulation. 

model(sparse);
...
end;

sparse_dll

Dynare performs also a block-decomposition of the model and the preprocessor creates two C-files or a binary file containing the model code according to the "Compiler option" containing the code of the deterministic simulation. 

model(sparse_dll);
...
end;

/!\ in the current version of Dynare this option is not compatible with the check command.

Compiler options

These options have to be used in conjunction with sparse_dll option. Two values could be used GCC_compiler or NO_compiler (default value). 

model(sparse_dll,GCC_compiler);
...
end;

/!\ if you use GCC_compiler Gnumex has to be installed on your computer.

cutoff

To get ride of nearly zero elements in the jacobian a threshold is used to determine whether a jacobian element has to be considered as null or not. If the absolute value of a jacobian element is below the threshold then it is considered as null and is discarded form the sparse representation of the jacobian. Eliminating nearly zero elements improves the sparsity of the model and hence the simulation time. Note that if the cutoff value is too high then the model properties could be strongly modified and the simulation could failed.

The default value of the cutoff is 1e-15. 

model(sparse,cutoff=1e-13);
...
end;

/!\ Note that the jacobian is evaluated considering the initial value of the parameters provided in the mod file. If the parameters value are modified (the model is estimated for example) before the simul command, some components of the jacobian could be wrongly discarded. To prevent such a result you have to set the cutoff to 0.

markowitz

At each iteration of the deterministic simulation, if the linear approximation of the model is solve using a sparse gaussian elimination, two criterion could be used to select the variable to substitute:

Markowitz option

The pivot is selected according to the highest value among j=1,…,N (where N is the number of possible pivots) of the following criteria:

\[ \frac{\frac{\left | \lambda_j\right |}{Sup_{i} \left | \lambda_i\right |} }{\left ( \frac{ n_j }{ Sup_{i} n_i} \right )^\mu} \]

With:

As $\mu \rightarrow \infty $ the Markowitz criteria prevails. At the opposite if $\mu \rightarrow 0 $ the usual maximum pivot criteria prevails.

The default value is 0.5 

model(sparse_dll,markowitz=2);
...
end;

/!\ This option is only used in conjunction with sparse_dll option.

model_info command

The model_info command provides information about

There are five different types of blocks depending on the simulation method used: 

model(sparse);
...
end;

model_info;

simulate options

method

This option indicates which linear solver will be implemented at each step of the Newton simulation of the stacked systems formed by the simultaneous blocks containing leads and lags on the endogenous variables. There are three values for this option if the sparse option is used in model command:

When sparse_dll option is used only SGE (for Sparse Gaussian Elimination) is available. It is the unique and default value of method in this case.

datafile

If the variables of the model are not constant over time, their initial values, stored in a text file, could be loaded, using the datafile option, as initial values before a deteministic simulation. 

model;
...
end;

simul(periods=80,datafile=mark3);