estimated_params — specifies the estimated parameters and their prior
estimated_params ;
{
stderr VARIABLE_NAME
|
corr VARIABLE_NAME_1, VARIABLE_NAME_2
|
PARAMETER_NAME
}
,
INITIAL_VALUE
[
, LOWER_BOUND
, UPPER_BOUND
]
;
... end ;
estimated_params ;
{
stderr VARIABLE_NAME
|
corr VARIABLE_NAME_1, VARIABLE_NAME_2
|
PARAMETER_NAME
}
[
, INITIAL_VALUE
[
, LOWER_BOUND
, UPPER_BOUND
]
]
, (1) PRIOR_SHAPE
, PRIOR_MEAN
, PRIOR_STANDARD_ERROR
[
, PRIOR_3RD_PARAMETER
[
, PRIOR_4TH_PARAMETER
[
, SCALE_PARAMETER
]
]
]
;
... end ; (1) beta_pdf | gamma_pdf | normal_pdf | uniform_pdf | inv_gamma_pdf | inv_gamma1_pdf | inv_gamma2_pdf
The estimated_params block lists all parameters to be estimated and specifies bounds and priors as necessary.
Each line corresponds to an estimated parameter and follows this syntax:
stderr VARIABLE_NAMEIndicates that the standard error of the exogenous variable VARIABLE_NAME, or of the observation error associated with endogenous observed variable VARIABLE_NAME, is to be estimated
corr VARIABLE_NAME_1, VARIABLE_NAME_2Indicates that the correlation between the exogenous variables VARIABLE_NAME_1 and VARIABLE_NAME_2, or the correlation of the observation errors associated with endogenous observed variables VARIABLE_NAME_1 and VARIABLE_NAME_2, is to be estimated
PARAMETER_NAMEThe name of a model parameter to be estimated
INITIAL_VALUESpecifies a starting value for maximum likelihood estimation
LOWER_BOUNDSpecifies a lower bound for the parameter value in maximum likelihood estimation
UPPER_BOUNDSpecifies an upper bound for the parameter value in maximum likelihood estimation
PRIOR_SHAPEA keyword specifying the shape of the prior density. See the list of possible values. Note that inv_gamma_pdf is equivalent to inv_gamma1_pdf
PRIOR_MEANThe mean of the prior distribution
PRIOR_STANDARD_ERRORThe standard error of the prior distribution
PRIOR_3RD_PARAMETERA third parameter of the prior used for generalized beta distribution, generalized gamma and for the uniform distribution. Default: 0
PRIOR_4TH_PARAMETERA fourth parameter of the prior used for generalized beta distribution, generalized gamma and for the uniform distribution. Default: 1
SCALE_PARAMETERThe scale parameter to be used for the jump distribution of the Metropolis-Hasting algorithm
INITIAL_VALUE, LOWER_BOUND, UPPER_BOUND, PRIOR_MEAN, PRIOR_STANDARD_ERROR, PRIOR_3RD_PARAMETER, PRIOR_4TH_PARAMETER and SCALE_PARAMETER can be any valid EXPRESSION. Some of them can be empty, in which Dynare will select a default value depending on the context and the prior shape.
At minimum, one must specify the name of the parameter and an initial guess. That will trigger unconstrained maximum likelihood estimation.
As one uses options more towards the end of the list, all previous options must be filled: for example, if you want to specify SCALE_PARAMETER, you must specify PRIOR_3RD_PARAMETER and PRIOR_4TH_PARAMETER. Use empty values, if these parameters don't apply.
Sometimes, it is desirable to estimate a transformation of a parameter appearing in the model, rather than the parameter itself. It is of course possible to replace the original parameter by a function of the estimated parameter everywhere is the model, but it is often unpractical.
In such a case, it is possible to declare the parameter to be estimated in the parameters statement and to define the transformation, using a pound sign (#) expression (see model).