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Provided that you have observations on some endogenous variables, it is possible to use Dynare to estimate some or all parameters. Both maximum likelihood (as in Ireland (2004)) and Bayesian techniques (as in Rabanal and RubioRamirez (2003), Schorfheide (2000) or Smets and Wouters (2003)) are available. Using Bayesian methods, it is possible to estimate DSGE models, VAR models, or a combination of the two techniques called DSGEVAR.
Note that in order to avoid stochastic singularity, you must have at least as many shocks or measurement errors in your model as you have observed variables.
The estimation using a first order approximation can benefit from the block decomposition of the model (see block).
Description
This command lists the name of observed endogenous variables for the estimation procedure. These variables must be available in the data file (see estimation_cmd).
Alternatively, this command is also used in conjunction with the
partial_information
option of stoch_simul
, for declaring
the set of observed variables when solving the model under partial
information.
Only one instance of varobs
is allowed in a model file. If one
needs to declare observed variables in a loop, the macroprocessor can
be used as shown in the second example below.
Simple example
varobs C y rr; 
Example with a loop
varobs @#for co in countries GDP_@{co} @#endfor ; 
Description
This block specifies linear trends for observed variables as
functions of model parameters. In case the loglinear
option is used,
this corresponds to a linear trend in the logged observables, i.e. an exponential
trend in the level of the observables.
Each line inside of the block should be of the form:
VARIABLE_NAME(EXPRESSION); 
In most cases, variables shouldn’t be centered when
observation_trends
is used.
Example
observation_trends; Y (eta); P (mu/eta); end; 
Description
This block lists all parameters to be estimated and specifies bounds and priors as necessary.
Each line corresponds to an estimated parameter.
In a maximum likelihood estimation, each line follows this syntax:
stderr VARIABLE_NAME  corr VARIABLE_NAME_1, VARIABLE_NAME_2  PARAMETER_NAME , INITIAL_VALUE [, LOWER_BOUND, UPPER_BOUND ]; 
In a Bayesian estimation, each line follows this syntax:
stderr VARIABLE_NAME  corr VARIABLE_NAME_1, VARIABLE_NAME_2  PARAMETER_NAME  DSGE_PRIOR_WEIGHT [, INITIAL_VALUE [, LOWER_BOUND, UPPER_BOUND]], PRIOR_SHAPE, PRIOR_MEAN, PRIOR_STANDARD_ERROR [, PRIOR_3RD_PARAMETER [, PRIOR_4TH_PARAMETER [, SCALE_PARAMETER ] ] ]; 
The first part of the line consists of one of the three following alternatives:
stderr VARIABLE_NAME
Indicates that the standard error of the exogenous variable VARIABLE_NAME, or of the observation error/measurement errors associated with endogenous observed variable VARIABLE_NAME, is to be estimated
corr VARIABLE_NAME1, VARIABLE_NAME2
Indicates that the correlation between the exogenous variables
VARIABLE_NAME1 and VARIABLE_NAME2, or the correlation of
the observation errors/measurement errors associated with endogenous observed variables
VARIABLE_NAME1 and VARIABLE_NAME2, is to be estimated. Note that correlations set by previous shocks
blocks or estimation
commands are kept at their value set prior to estimation if they are not estimated again subsequently. Thus, the treatment is the same as in the case of deep parameters set during model calibration and not estimated.
PARAMETER_NAME
The name of a model parameter to be estimated
DSGE_PRIOR_WEIGHT
…
The rest of the line consists of the following fields, some of them being optional:
INITIAL_VALUE
Specifies a starting value for the posterior mode optimizer or the maximum likelihood estimation. If unset, defaults to the prior mean.
LOWER_BOUND
Specifies a lower bound for the parameter value in maximum likelihood estimation. In a Bayesian estimation context, sets a lower bound only effective while maximizing the posterior kernel. This lower bound does not modify the shape of the prior density, and is only aimed at helping the optimizer in identifying the posterior mode (no consequences for the MCMC). For some prior densities (namely inverse gamma, gamma, uniform, beta or weibull) it is possible to shift the support of the prior distributions to the left or the right using prior_3rd_parameter. In this case the prior density is effectively modified (note that the truncated Gaussian density is not implemented in Dynare). If unset, defaults to minus infinity (ML) or the natural lower bound of the prior (Bayesian estimation).
UPPER_BOUND
Same as lower_bound, but specifying an upper bound instead.
PRIOR_SHAPE
A keyword specifying the shape of the prior density.
The possible values are: beta_pdf
,
gamma_pdf
, normal_pdf
,
uniform_pdf
, inv_gamma_pdf
,
inv_gamma1_pdf
, inv_gamma2_pdf
and weibull_pdf
. Note
that inv_gamma_pdf
is equivalent to
inv_gamma1_pdf
PRIOR_MEAN
PRIOR_STANDARD_ERROR
PRIOR_3RD_PARAMETER
A third parameter of the prior used for generalized beta distribution,
generalized gamma, generalized weibull and for the uniform distribution. Default: 0
PRIOR_4TH_PARAMETER
A fourth parameter of the prior used for generalized beta distribution
and for the uniform distribution. Default: 1
SCALE_PARAMETER
A parameter specific scale parameter for the jumping distribution’s covariance matrix of the MetropolisHasting algorithm
Note that 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.
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.
Example
The following line:
corr eps_1, eps_2, 0.5, , , beta_pdf, 0, 0.3, 1, 1; 
sets a generalized beta prior for the correlation between eps_1
and
eps_2
with mean 0 and variance 0.3. By setting
PRIOR_3RD_PARAMETER to 1 and PRIOR_4TH_PARAMETER to 1 the
standard beta distribution with support [0,1] is changed to a
generalized beta with support [1,1]. Note that LOWER_BOUND and
UPPER_BOUND are left empty and thus default to 1 and 1,
respectively. The initial value is set to 0.5.
Similarly, the following line:
corr eps_1, eps_2, 0.5, 0.5, 1, beta_pdf, 0, 0.3, 1, 1; 
sets the same generalized beta distribution as before, but now truncates this distribution to [0.5,1] through the use of LOWER_BOUND and UPPER_BOUND. Hence, the prior does not integrate to 1 anymore.
Parameter transformation
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 section Model declaration).
Example
parameters bet; model; # sig = 1/bet; c = sig*c(+1)*mpk; end; estimated_params; bet, normal_pdf, 1, 0.05; end; 
This block declares numerical initial values for the optimizer when
these ones are different from the prior mean. It should be specified after the estimated_params
block as otherwise the specified starting values are overwritten by the latter.
Each line has the following syntax:
stderr VARIABLE_NAME  corr VARIABLE_NAME_1, VARIABLE_NAME_2  PARAMETER_NAME , INITIAL_VALUE; 
Options
use_calibration
For not specifically initialized parameters, use the deep parameters and the elements of the covariance matrix specified in the shocks
block from calibration as starting values for estimation. For components of the shocks
block that were not explicitly specified during calibration or which violate the prior, the prior mean is used.
See estimated_params, for the meaning and syntax of the various components.
This block declares lower and upper bounds for parameters in maximum likelihood estimation.
Each line has the following syntax:
stderr VARIABLE_NAME  corr VARIABLE_NAME_1, VARIABLE_NAME_2  PARAMETER_NAME , LOWER_BOUND, UPPER_BOUND; 
See estimated_params, for the meaning and syntax of the various components.
Description
This command runs Bayesian or maximum likelihood estimation.
The following information will be displayed by the command:
Note that the posterior moments, smoothed variables, kstep ahead
filtered variables and forecasts (when requested) will only be
computed on the variables listed after the estimation
command.
Alternatively, one can choose to compute these quantities on all
endogenous or on all observed variables (see
consider_all_endogenous
and consider_only_observed
options below). If no variable is listed after the estimation
command, then Dynare will interactively ask which variable set to use.
Also, during the MCMC (Bayesian estimation with mh_replic
>0) a
(graphical or text) waiting bar is displayed showing the progress of the
MonteCarlo and the current value of the acceptance ratio. Note that
if the load_mh_file
option is used (see below) the reported
acceptance ratio does not take into account the draws from the previous
MCMC. In the literature there is a general agreement for saying that the
acceptance ratio should be close to one third or one quarter. If this
not the case, you can stop the MCMC (CtrlC
) and change the value
of option mh_jscale
(see below).
Note that by default Dynare generates random numbers using the algorithm
mt199937ar
(ie Mersenne Twister method) with a seed set equal
to 0
. Consequently the MCMCs in Dynare are deterministic: one
will get exactly the same results across different Dynare runs
(ceteris paribus). For instance, the posterior moments or posterior
densities will be exactly the same. This behaviour allows to easily
identify the consequences of a change on the model, the priors or the
estimation options. But one may also want to check that across multiple
runs, with different sequences of proposals, the returned results are
almost identical. This should be true if the number of iterations
(ie the value of mh_replic
) is important enough to ensure the
convergence of the MCMC to its ergodic distribution. In this case the
default behaviour of the random number generators in not wanted, and the
user should set the seed according to the system clock before the
estimation command using the following command:
set_dynare_seed('clock'); 
so that the sequence of proposals will be different across different runs.
Algorithms
The Monte Carlo Markov Chain (MCMC) diagnostics are generated by the estimation command if mh_replic is larger than 2000 and if option nodiagnostic is not used. If mh_nblocks is equal to one, the convergence diagnostics of Geweke (1992,1999) is computed. It uses a chi square test to compare the means of the first and last draws specified by geweke_interval after discarding the burnin of mh_drop. The test is computed using variance estimates under the assumption of no serial correlation as well as using tapering windows specified in taper_steps. If mh_nblocks is larger than 1, the convergence diagnostics of Brooks and Gelman (1998) are used instead. As described in section 3 of Brooks and Gelman (1998) the univariate convergence diagnostics are based on comparing pooled and within MCMC moments (Dynare displays the second and third order moments, and the length of the Highest Probability Density interval covering 80% of the posterior distribution). Due to computational reasons, the multivariate convergence diagnostic does not follow Brooks and Gelman (1998) strictly, but rather applies their idea for univariate convergence diagnostics to the range of the posterior likelihood function instead of the individual parameters. The posterior kernel is used to aggregate the parameters into a scalar statistic whose convergence is then checked using the Brooks and Gelman (1998) univariate convergence diagnostic.
The inefficiency factors are computed as in Giordano et al. (2011) based on Parzen windows as in e.g. Andrews (1991).
Options
datafile = FILENAME
The datafile: a ‘.m’ file, a ‘.mat’ file, a
‘.csv’ file, or a ‘.xls’/‘.xlsx’ file (under Octave, the
io package from OctaveForge is
required for the ‘.csv’ and ‘.xlsx’ formats and the ‘.xls’ file
extension is not supported). Note that the base name (i.e. without
extension) of the datafile has to be different from the base name of the model
file.
If there are several files named FILENAME
, but with different file endings,
the file name must be included in quoted strings and provide the file ending like

dirname = FILENAME
Directory in which to store estimation
output. To pass a
subdirectory of a directory, you must quote the argument. Default:
<mod_file>
xls_sheet = NAME
xls_range = RANGE
The range with the data in an Excel file. For example, xls_range=B2:D200
nobs = INTEGER
The number of observations following first_obs to be used. Default: all observations in
the file after first_obs
nobs = [INTEGER1:INTEGER2]
Runs a recursive estimation and forecast for samples of size ranging
of INTEGER1 to INTEGER2. Option forecast
must
also be specified. The forecasts are stored in the
RecursiveForecast
field of the results structure (see RecursiveForecast).
The respective results structures oo_
are saved in oo_recursive_
(see oo_recursive_)
and are indexed with the respective sample length.
first_obs = INTEGER
The number of the first observation to be used. In case of estimating a DSGEVAR,
first_obs
needs to be larger than the number of lags. Default: 1
first_obs = [INTEGER1:INTEGER2]
Runs a rolling window estimation and forecast for samples of fixed size nobs
starting with the
first observation ranging from INTEGER1 to INTEGER2. Option forecast
must also be specified. This option is incompatible with requesting recursive forecasts using an
expanding window (see nobs). The respective results structures oo_
are saved in oo_recursive_
(see oo_recursive_) and are indexed with the respective
first observation of the rolling window.
prefilter = INTEGER
A value of 1
means that the estimation procedure will
demean each data series by its empirical mean. If the loglinear option
without the logdata option is requested, the data will first be logged
and then demeaned. Default: 0
, i.e. no prefiltering
presample = INTEGER
The number of observations after first_obs to be skipped before evaluating the
likelihood. These presample observations do not enter the likelihood, but are used as a
training sample for starting the Kalman filter iterations. This option is incompatible with
estimating a DSGEVAR. Default: 0
loglinear
Computes a loglinear approximation of the model instead of a linear approximation. As always in the context of estimation, the data must correspond to the definition of the variables used in the model (see Pfeifer (2013) for more details on how to correctly specify observation equations linking model variables and the data). If you specify the loglinear option, Dynare will take the logarithm of both your model variables and of your data as it assumes the data to correspond to the original nonlogged model variables. The displayed posterior results like impulse responses, smoothed variables, and moments will be for the logged variables, not the original unlogged ones. Default: computes a linear approximation
logdata
Dynare applies the transformation to the provided data if a loglinearization of the model is requested (loglinear) unless logdata
option is used. This option is necessary if the user provides data already in logs, otherwise the transformation will be applied twice (this may result in complex data).
plot_priors = INTEGER
Control the plotting of priors: 1
0
No prior plot
1
Prior density for each estimated parameter is plotted. It is important to check that the actual shape of prior densities matches what you have in mind. Illchosen values for the prior standard density can result in absurd prior densities.
Default value is 1
.
nograph
See nograph.
posterior_nograph
Suppresses the generation of graphs associated with Bayesian IRFs (bayesian_irf), posterior smoothed objects (smoother), and posterior forecasts (forecast).
posterior_graph
Reenables the generation of graphs previously shut off with posterior_nograph.
nodisplay
See nodisplay.
graph_format = FORMAT
graph_format = ( FORMAT, FORMAT… )
See graph_format.
lik_init = INTEGER
Type of initialization of Kalman filter:
1
For stationary models, the initial matrix of variance of the error of forecast is set equal to the unconditional variance of the state variables
2
For nonstationary models: a wide prior is used with an initial matrix of variance of the error of forecast diagonal with 10 on the diagonal (follows the suggestion of Harvey and Phillips(1979))
3
For nonstationary models: use a diffuse filter (use rather the diffuse_filter
option)
4
The filter is initialized with the fixed point of the Riccati equation
5
Use i) option 2 for the nonstationary elements by setting their initial variance in the forecast error matrix to 10 on the diagonal and all covariances to 0 and ii) option 1 for the stationary elements.
Default value is 1
. For advanced use only.
lik_algo = INTEGER
For internal use and testing only.
conf_sig = DOUBLE
Confidence interval used for classical forecasting after estimation. See conf_sig.
mh_conf_sig = DOUBLE
Confidence/HPD interval used for the computation of prior and posterior statistics like: parameter distributions, prior/posterior moments, conditional variance decomposition, impulse response functions, Bayesian forecasting. Default: 0.9
mh_replic = INTEGER
Number of replications for MetropolisHastings
algorithm. For the time being, mh_replic
should be larger than
1200
. Default: 20000
sub_draws = INTEGER
number of draws from the MCMC that are used to
compute posterior distribution of various objects (smoothed variable,
smoothed shocks, forecast, moments, IRF). The draws used to compute
these posterior moments are sampled uniformly in the estimated empirical
posterior distribution (ie draws of the MCMC). sub_draws
should be smaller than the total number of MCMC draws available.
Default: min(posterior_max_subsample_draws,(Total number of
draws)*(number of chains))
posterior_max_subsample_draws = INTEGER
maximum number of draws from the
MCMC used to compute posterior distribution of various objects (smoothed
variable, smoothed shocks, forecast, moments, IRF), if not overriden by
option sub_draws. Default: 1200
mh_nblocks = INTEGER
Number of parallel chains for MetropolisHastings algorithm. Default:
2
mh_drop = DOUBLE
The fraction of initially generated parameter vectors to be dropped as a burnin before using posterior simulations. Default: 0.5
mh_jscale = DOUBLE
The scale parameter of the jumping distribution’s covariance matrix (MetropolisHastings or TaRBalgorithm). The default value is rarely satisfactory. This option must be tuned to obtain, ideally, an acceptance ratio of 25%33%. Basically, the idea is to increase the variance of the jumping distribution if the acceptance ratio is too high, and decrease the same variance if the acceptance ratio is too low. In some situations it may help to consider parameterspecific values for this scale parameter. This can be done in the estimated_params block.
Note that mode_compute=6
will tune the scale parameter to achieve an acceptance rate of
AcceptanceRateTarget. The resulting scale parameter will be saved into a file
named ‘MODEL_FILENAME_mh_scale.mat’. This file can be loaded in subsequent runs
via the posterior_sampler_options
option scale_file. Both mode_compute=6
and scale_file
will overwrite any value specified in estimated_params
with the tuned value.
Default: 0.2
mh_init_scale = DOUBLE
The scale to be used for drawing the initial value of the
MetropolisHastings chain. Generally, the starting points should be overdispersed
for the Brooks and Gelman (1998)convergence diagnostics to be meaningful. Default: 2*mh_jscale
.
It is important to keep in mind that mh_init_scale
is set at the beginning of
Dynare execution, i.e. the default will not take into account potential changes in
mh_jscale introduced by either mode_compute=6
or the
posterior_sampler_options
option scale_file.
If mh_init_scale
is too wide during initalization of the posterior sampler so that 100 tested draws
are inadmissible (e.g. BlanchardKahn conditions are always violated), Dynare will request user input
of a new mh_init_scale
value with which the next 100 draws will be drawn and tested.
If the nointeractiveoption has been invoked, the program will instead automatically decrease
mh_init_scale
by 10 percent after 100 futile draws and try another 100 draws. This iterative
procedure will take place at most 10 times, at which point Dynare will abort with an error message.
mh_recover
Attempts to recover a MetropolisHastings
simulation that crashed prematurely, starting with the last available saved
mh
file. Shouldn’t be used together with
load_mh_file
or a different mh_replic
than in the crashed run. Since Dynare 4.5
the proposal density from the previous run will automatically be loaded. In older versions,
to assure a neat continuation of the chain with the same proposal density, you should
provide the mode_file
used in the previous
run or the same userdefined mcmc_jumping_covariance
when using this option. Note that
under Octave, a neat continuation of the crashed chain with the respective last random number
generator state is currently not supported.
mh_mode = INTEGER
…
mode_file = FILENAME
Name of the file containing previous value for the mode. When
computing the mode, Dynare stores the mode (xparam1
) and the
hessian (hh
, only if cova_compute=1
) in a file called
‘MODEL_FILENAME_mode.mat’. After a successful run of the estimation
command, the mode_file
will be disabled to prevent other function calls
from implicitly using an updated modefile. Thus, if the modfile contains subsequent
estimation
commands, the mode_file
option, if desired, needs to be
specified again.
mode_compute = INTEGER  FUNCTION_NAME
Specifies the optimizer for the mode computation:
0
The mode isn’t computed. When mode_file
option is specified, the
mode is simply read from that file.
When mode_file
option is not
specified, Dynare reports the value of the log posterior (log likelihood)
evaluated at the initial value of the parameters.
When mode_file
option is not specified and there is no estimated_params
block,
but the smoother
option is used, it is a roundabout way to
compute the smoothed value of the variables of a model with calibrated parameters.
1
Uses fmincon
optimization routine (available under MATLAB if
the Optimization Toolbox is installed; not available under Octave)
2
Uses the continuous simulated annealing global optimization algorithm described in Corana et al. (1987) and Goffe et al. (1994).
3
Uses fminunc
optimization routine (available under MATLAB if
the optimization toolbox is installed; available under Octave if the
optim package from
OctaveForge is installed)
4
Uses Chris Sims’s csminwel
5
Uses Marco Ratto’s newrat
. This value is not compatible with non
linear filters or DSGEVAR models.
This is a slice optimizer: most iterations are a sequence of univariate optimization step, one for each estimated parameter or shock.
Uses csminwel
for line search in each step.
6
Uses a MonteCarlo based optimization routine (see Dynare wiki for more details)
7
Uses fminsearch
, a simplex based optimization routine (available
under MATLAB if the optimization toolbox is installed; available under
Octave if the optim
package from OctaveForge is installed)
8
Uses Dynare implementation of the NelderMead simplex based optimization
routine (generally more efficient than the MATLAB or Octave implementation
available with mode_compute=7
)
9
Uses the CMAES (Covariance Matrix Adaptation Evolution Strategy) algorithm of Hansen and Kern (2004), an evolutionary algorithm for difficult nonlinear nonconvex optimization
10
Uses the simpsa algorithm, based on the combination of the nonlinear simplex and simulated annealing algorithms and proposed by Cardoso, Salcedo and Feyo de Azevedo (1996).
11
This is not strictly speaking an optimization algorithm. The (estimated) parameters are treated as state variables and estimated jointly with the original state variables of the model using a nonlinear filter. The algorithm implemented in Dynare is described in Liu and West (2001).
12
Uses particleswarm
optimization routine (available under MATLAB if
the Global Optimization Toolbox is installed; not available under Octave).
101
Uses the SolveOpt algorithm for local nonlinear optimization problems proposed by Kuntsevich and Kappel (1997).
102
Uses simulannealbnd
optimization routine (available under MATLAB if
the Global Optimization Toolbox is installed; not available under Octave)
FUNCTION_NAME
It is also possible to give a FUNCTION_NAME to this option, instead of an INTEGER. In that case, Dynare takes the return value of that function as the posterior mode.
Default value is 4
.
silent_optimizer
Instructs Dynare to run mode computing/optimization silently without displaying results or saving files in between. Useful when running loops.
mcmc_jumping_covariance = hessianprior_varianceidentity_matrixFILENAME
Tells Dynare which covariance to use for the proposal density of the MCMC sampler. mcmc_jumping_covariance
can be one of the following:
hessian
Uses the Hessian matrix computed at the mode.
prior_variance
Uses the prior variances. No infinite prior variances are allowed in this case.
identity_matrix
Uses an identity matrix.
FILENAME
Loads an arbitrary userspecified covariance matrix from FILENAME.mat
. The covariance matrix must be saved in a variable named jumping_covariance
, must be square, positive definite, and have the same dimension as the number of estimated parameters.
Note that the covariance matrices are still scaled with mh_jscale. Default value is hessian
.
mode_check
Tells Dynare to plot the posterior density for values around the
computed mode for each estimated parameter in turn. This is helpful to
diagnose problems with the optimizer. Note that for order
>1, the
likelihood function resulting from the particle filter is not differentiable
anymore due to random chatter introduced by selecting different particles for
different parameter values. For this reason, the mode_check
plot may look wiggly.
mode_check_neighbourhood_size = DOUBLE
Used in conjunction with option mode_check
, gives the width of
the window around the posterior mode to be displayed on the diagnostic
plots. This width is expressed in percentage deviation. The Inf
value is allowed, and will trigger a plot over the entire domain
(see also mode_check_symmetric_plots
).
Default: 0.5
.
mode_check_symmetric_plots = INTEGER
Used in conjunction with option mode_check
, if set to 1
,
tells Dynare to ensure that the check plots are symmetric around the
posterior mode. A value of 0
allows to have asymmetric plots,
which can be useful if the posterior mode is close to a domain
boundary, or in conjunction with mode_check_neighbourhood_size =
Inf
when the domain in not the entire real line. Default: 1
.
mode_check_number_of_points = INTEGER
Number of points around the posterior mode where the posterior kernel is evaluated (for each parameter). Default is 20
prior_trunc = DOUBLE
Probability of extreme values of the prior
density that is ignored when computing bounds for the
parameters. Default: 1e32
huge_number = DOUBLE
Value for replacing infinite values in the definition of (prior) bounds
when finite values are required for computational reasons. Default: 1e7
load_mh_file
Tells Dynare to add to previous
MetropolisHastings simulations instead of starting from
scratch. Since Dynare 4.5
the proposal density from the previous run will automatically be loaded. In older versions,
to assure a neat continuation of the chain with the same proposal density, you should
provide the mode_file
used in the previous
run or the same userdefined mcmc_jumping_covariance
when using this option.
Shouldn’t be used together with mh_recover
. Note that under Octave, a neat
continuation of the chain with the last random number
generator state of the already present draws is currently not supported.
load_results_after_load_mh
This option is available when loading a previous MCMC run without
adding additional draws, i.e. when load_mh_file
is specified with mh_replic=0
. It tells Dynare
to load the previously computed convergence diagnostics, marginal data density, and posterior statistics from an
existing _results
file instead of recomputing them.
optim = (NAME, VALUE, ...)
A list of NAME and VALUE pairs. Can be used to set options for the optimization routines. The set of available options depends on the selected optimization routine (ie on the value of option mode_compute):
1, 3, 7, 12
Available options are given in the documentation of the MATLAB Optimization Toolbox or in Octave’s documentation.
2
Available options are:
'initial_step_length'
Initial step length. Default: 1
'initial_temperature'
Initial temperature. Default: 15
'MaxIter'
Maximum number of function evaluations. Default: 100000
'neps'
Number of final function values used to decide upon termination. Default: 10
'ns'
Number of cycles. Default: 10
'nt'
Number of iterations before temperature reduction. Default: 10
'step_length_c'
Step length adjustment. Default: 0.1
'TolFun'
Stopping criteria. Default: 1e8
'rt'
Temperature reduction factor. Default: 0.1
'verbosity'
Controls verbosity of display during optimization, ranging from 0 (silent) to 3
(each function evaluation). Default: 1
4
Available options are:
'InitialInverseHessian'
Initial approximation for the inverse of the Hessian matrix of the posterior kernel (or likelihood). Obviously this approximation has to be a square, positive definite and symmetric matrix. Default: '1e4*eye(nx)'
, where nx
is the number of parameters to be estimated.
'MaxIter'
Maximum number of iterations. Default: 1000
'NumgradAlgorithm'
Possible values are 2
, 3
and 5
respectively corresponding to the two, three and five points formula used to compute the gradient of the objective function (see Abramowitz and Stegun (1964)). Values 13
and 15
are more experimental. If perturbations on the right and the left increase the value of the objective function (we minimize this function) then we force the corresponding element of the gradient to be zero. The idea is to temporarily reduce the size of the optimization problem. Default: 2
.
'NumgradEpsilon'
Size of the perturbation used to compute numerically the gradient of the objective function. Default: 1e6
'TolFun'
Stopping criteria. Default: 1e7
'verbosity'
Controls verbosity of display during optimization. Set to 0 to set to silent. Default: 1
'SaveFiles'
Controls saving of intermediate results during optimization. Set to 0 to shut off saving. Default: 1
5
Available options are:
'Hessian'
Triggers three types of Hessian computations. 0
: outer product gradient; 1
default DYNARE Hessian routine; 2
’mixed’ outer product gradient, where diagonal elements are obtained using second order derivation formula and outer product is used for correlation structure.
Both {0} and {2} options require univariate filters, to ensure using maximum number of individual densities and a positive definite Hessian.
Both {0} and {2} are quicker than default DYNARE numeric Hessian, but provide decent starting values for Metropolis for large models (option {2} being more accurate than {0}).
Default: 1
.
'MaxIter'
Maximum number of iterations. Default: 1000
'TolFun'
Stopping criteria. Default: 1e5
for numerical derivatives 1e7
for analytic derivatives.
'verbosity'
Controls verbosity of display during optimization. Set to 0 to set to silent. Default: 1
'SaveFiles'
Controls saving of intermediate results during optimization. Set to 0 to shut off saving. Default: 1
6
Available options are:
'AcceptanceRateTarget'
A real number between zero and one. The scale parameter of the jumping distribution is adjusted so that the effective acceptance rate matches the value of option 'AcceptanceRateTarget'
. Default: 1.0/3.0
'InitialCovarianceMatrix'
Initial covariance matrix of the jumping distribution. Default is 'previous'
if option mode_file
is used, 'prior'
otherwise.
'nclimb'
Number of iterations in the last MCMC (climbing mode).
'ncovmh'
Number of iterations used for updating the covariance matrix of the jumping distribution. Default: 20000
'nscalemh'
Maximum number of iterations used for adjusting the scale parameter of the jumping distribution. 200000
'NumberOfMh'
Number of MCMC run sequentially. Default: 3
8
Available options are:
'InitialSimplexSize'
Initial size of the simplex, expressed as percentage deviation from the provided initial guess in each direction. Default: .05
'MaxIter'
Maximum number of iterations. Default: 5000
'MaxFunEvals'
Maximum number of objective function evaluations. No default.
'MaxFunvEvalFactor'
Set MaxFunvEvals
equal to MaxFunvEvalFactor
times the number of estimated parameters. Default: 500
.
'TolFun'
Tolerance parameter (w.r.t the objective function). Default: 1e4
'TolX'
Tolerance parameter (w.r.t the instruments). Default: 1e4
'verbosity'
Controls verbosity of display during optimization. Set to 0 to set to silent. Default: 1
9
Available options are:
'CMAESResume'
Resume previous run. Requires the variablescmaes.mat
from the last run.
Set to 1 to enable. Default: 0
'MaxIter'
Maximum number of iterations.
'MaxFunEvals'
Maximum number of objective function evaluations. Default: Inf
.
'TolFun'
Tolerance parameter (w.r.t the objective function). Default: 1e7
'TolX'
Tolerance parameter (w.r.t the instruments). Default: 1e7
'verbosity'
Controls verbosity of display during optimization. Set to 0 to set to silent. Default: 1
'SaveFiles'
Controls saving of intermediate results during optimization. Set to 0 to shut off saving. Default: 1
10
Available options are:
'EndTemperature'
Terminal condition w.r.t the temperature. When the temperature reaches EndTemperature
, the temperature is set to zero and the algorithm falls back into a standard simplex algorithm. Default: .1
'MaxIter'
Maximum number of iterations. Default: 5000
'MaxFunvEvals'
Maximum number of objective function evaluations. No default.
'TolFun'
Tolerance parameter (w.r.t the objective function). Default: 1e4
'TolX'
Tolerance parameter (w.r.t the instruments). Default: 1e4
'verbosity'
Controls verbosity of display during optimization. Set to 0 to set to silent. Default: 1
101
Available options are:
'LBGradientStep'
Lower bound for the stepsize used for the difference approximation of gradients. Default: 1e11
'MaxIter'
Maximum number of iterations. Default: 15000
'SpaceDilation'
Coefficient of space dilation. Default: 2.5
'TolFun'
Tolerance parameter (w.r.t the objective function). Default: 1e6
'TolX'
Tolerance parameter (w.r.t the instruments). Default: 1e6
'verbosity'
Controls verbosity of display during optimization. Set to 0 to set to silent. Default: 1
102
Available options are given in the documentation of the MATLAB Global Optimization Toolbox.
Example 1
To change the defaults of csminwel (mode_compute=4
):
estimation(..., mode_compute=4, optim=('NumgradAlgorithm',3,'TolFun',1e5), ...);
nodiagnostic
Does not compute the convergence diagnostics for MetropolisHastings. Default: diagnostics are computed and displayed
bayesian_irf
Triggers the computation of the posterior
distribution of IRFs. The length of the IRFs are controlled by the
irf
option. Results are stored in oo_.PosteriorIRF.dsge
(see below for a description of this variable)
relative_irf
See relative_irf.
dsge_var = DOUBLE
Triggers the estimation of a DSGEVAR model, where the
weight of the DSGE prior of the VAR model is calibrated to the value
passed (see Del Negro and Schorfheide (2004)). It represents ratio of dummy over actual observations.
To assure that the prior is proper, the value must be bigger than ,
where is the number of estimated parameters, is the number of observables, #
and is the number of observations. NB: The previous method
of declaring dsge_prior_weight
as a parameter and then
calibrating it is now deprecated and will be removed in a future release
of Dynare.
Some of objects arising during estimation are stored with their values at the mode in
oo_.dsge_var.posterior_mode.
dsge_var
Triggers the estimation of a DSGEVAR model, where the weight of the
DSGE prior of the VAR model will be estimated (as in Adjemian et alii
(2008)). The prior on the weight of the DSGE prior,
dsge_prior_weight
, must be defined in the estimated_params
section. NB: The previous method of declaring dsge_prior_weight
as a parameter and then placing it in estimated_params
is now
deprecated and will be removed in a future release of Dynare.
dsge_varlag = INTEGER
The number of lags used to estimate a DSGEVAR
model. Default: 4
.
posterior_sampling_method=NAME
Selects the sampler used to sample from the posterior distribution during Bayesian estimation. Default: ’random_walk_metropolis_hastings’
'random_walk_metropolis_hastings'
Instructs Dynare to use the RandomWalk MetropolisHastings. In this algorithm, the proposal density is recentered to the previous draw in every step.
'tailored_random_block_metropolis_hastings'
Instructs Dynare to use the Tailored randomized block (TaRB) MetropolisHastings algorithm proposed by Chib and Ramamurthy (2010) instead of the standard RandomWalk MetropolisHastings. In this algorithm, at each iteration the estimated parameters are randomly assigned to different blocks. For each of these blocks a modefinding step is conducted. The inverse Hessian at this mode is then used as the covariance of the proposal density for a RandomWalk MetropolisHastings step. If the numerical Hessian is not positive definite, the generalized Cholesky decomposition of Schnabel and Eskow (1990) is used, but without pivoting. The TaRBMH algorithm massively reduces the autocorrelation in the MH draws and thus reduces the number of draws required to representatively sample from the posterior. However, this comes at a computational costs as the algorithm takes more time to run.
'independent_metropolis_hastings'
Use the Independent MetropolisHastings algorithm where the proposal distribution  in contrast to the Random Walk MetropolisHastings algorithm  does not depend on the state of the chain.
'slice'
Instructs Dynare to use the Slice sampler of Planas, Ratto, and Rossi (2015).
Note that 'slice'
is incompatible with
prior_trunc=0
.
posterior_sampler_options = (NAME, VALUE, ...)
A list of NAME and VALUE pairs. Can be used to set options for the posterior sampling methods. The set of available options depends on the selected posterior sampling routine (i.e. on the value of option posterior_sampling_method):
'random_walk_metropolis_hastings'
Available options are:
'proposal_distribution'
Specifies the statistical distribution used for the proposal density.
'rand_multivariate_normal'
Use a multivariate normal distribution. This is the default.
'rand_multivariate_student'
Use a multivariate student distribution
'student_degrees_of_freedom'
Specifies the degrees of freedom to be used with the multivariate student distribution. Default: 3
'use_mh_covariance_matrix'
Indicates to use the covariance matrix of the draws from a previous MCMC run to define the covariance of the proposal distribution. Requires the load_mh_fileoption to be specified. Default: 0
'scale_file'
Provides the name of a ‘_mh_scale.mat’file storing the tuned scale factor from a
previous run of mode_compute=6
'save_tmp_file'
Save the MCMC draws into a _mh_tmp_blck
file at the refresh rate of the status bar instead of just saving the draws
when the current _mh*_blck
file is full. Default: 0
'independent_metropolis_hastings'
Takes the same options as in the case of random_walk_metropolis_hastings
'slice'
'rotated'
Triggers rotated slice iterations using a covariance matrix from initial burnin iterations.
Requires either use_mh_covariance_matrix
or slice_initialize_with_mode
. Default: 0
'mode_files'
For multimodal posteriors, provide the name of a file containing a nparam by nmodes
variable called
xparams
storing the different modes. This array must have one column vector per mode and the estimated
parameters along the row dimension. With this info,
the code will automatically trigger the rotated
and mode
options. Default: []
.
'slice_initialize_with_mode'
The default for slice is to set mode_compute = 0
and start the chain(s) from a random
location in the prior space. This option first runs the modefinder and then starts the
chain from the mode. Together with rotated
, it will use the inverse Hessian from the
mode to perform rotated slice iterations. Default: 0
'initial_step_size'
Sets the initial size of the interval in the steppingout procedure as fraction of the prior support
i.e. the size will be initial_step_size*(UBLB). initial_step_size
must be a real number in the interval [0, 1].
Default: 0.8
'use_mh_covariance_matrix'
See use_mh_covariance_matrix. Must be used with 'rotated'
. Default: 0
'save_tmp_file'
See save_tmp_file. Default: 1.
'tailored_random_block_metropolis_hastings'
new_block_probability = DOUBLE
Specifies the probability of the next parameter belonging to a new block when the random blocking in the TaRB
MetropolisHastings algorithm is conducted. The higher this number, the smaller is the average block size and the
more random blocks are formed during each parameter sweep. Default: 0.25
.
mode_compute = INTEGER
Specifies the modefinder run in every iteration for every block of the
TaRB MetropolisHastings algorithm. See mode_compute. Default: 4
.
optim = (NAME, VALUE, ...)
Specifies the options for the modefinder used in the TaRB MetropolisHastings algorithm. See optim.
'scale_file'
See scale_file.
'save_tmp_file'
See save_tmp_file. Default: 1.
moments_varendo
Triggers the computation of the posterior
distribution of the theoretical moments of the endogenous
variables. Results are stored in
oo_.PosteriorTheoreticalMoments
(see oo_.PosteriorTheoreticalMoments). The number of lags in the autocorrelation function is
controlled by the ar
option.
contemporaneous_correlation
See contemporaneous_correlation. Results are stored in oo_.PosteriorTheoreticalMoments
.
Note that the nocorr
option has no effect.
no_posterior_kernel_density
Shuts off the computation of the kernel density estimator for the posterior objects (see densityfield).
conditional_variance_decomposition = INTEGER
See below.
conditional_variance_decomposition = [INTEGER1:INTEGER2]
See below.
conditional_variance_decomposition = [INTEGER1 INTEGER2 …]
Computes the posterior distribution of the conditional variance
decomposition for the specified period(s). The periods must be strictly
positive. Conditional variances are given by
. For
period 1, the conditional variance decomposition provides the
decomposition of the effects of shocks upon impact. The results are
stored in
oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition
,
but currently there is no displayed output. Note that this option requires the
option moments_varendo
to be specified.
filtered_vars
Triggers the computation of the posterior
distribution of filtered endogenous variables/onestep ahead forecasts, i.e.
. Results are
stored in oo_.FilteredVariables
(see below for a description of
this variable)
smoother
Triggers the computation of the posterior distribution
of smoothed endogenous variables and shocks, i.e. the expected value of variables and shocks given the information available in all observations up to the final date (). Results are stored in
oo_.SmoothedVariables
, oo_.SmoothedShocks
and
oo_.SmoothedMeasurementErrors
. Also triggers the computation of
oo_.UpdatedVariables
, which contains the estimation of the expected value of variables given the information available at the current date (). See below for a description of all these
variables.
forecast = INTEGER
Computes the posterior distribution of a forecast on
INTEGER periods after the end of the sample used in
estimation. If no MetropolisHastings is computed, the result is
stored in variable oo_.forecast
and corresponds to the forecast
at the posterior mode. If a MetropolisHastings is computed, the
distribution of forecasts is stored in variables
oo_.PointForecast
and
oo_.MeanForecast
. See section Forecasting, for a description of
these variables.
tex
see tex.
kalman_algo = INTEGER
0
Automatically use the Multivariate Kalman Filter for stationary models and the Multivariate Diffuse Kalman Filter for nonstationary models
1
Use the Multivariate Kalman Filter
2
Use the Univariate Kalman Filter
3
Use the Multivariate Diffuse Kalman Filter
4
Use the Univariate Diffuse Kalman Filter
Default value is 0
. In case of missing observations of single or all series, Dynare treats those missing values as unobserved states and uses the Kalman filter to infer their value (see e.g. Durbin and Koopman (2012), Ch. 4.10)
This procedure has the advantage of being capable of dealing with observations where the forecast error variance matrix becomes singular for some variable(s).
If this happens, the respective observation enters with a weight of zero in the loglikelihood, i.e. this observation for the respective variable(s) is dropped
from the likelihood computations (for details see Durbin and Koopman (2012), Ch. 6.4 and 7.2.5 and Koopman and Durbin (2000)). If the use of a multivariate Kalman filter is specified and a
singularity is encountered, Dynare by default automatically switches to the univariate Kalman filter for this parameter draw. This behavior can be changed via the
use_univariate_filters_if_singularity_is_detected option.
fast_kalman_filter
Select the fast Kalman filter using Chandrasekhar
recursions as described by Herbst, 2015. This setting is only used with
kalman_algo=1
or kalman_algo=3
. In case of using the diffuse Kalman
filter (kalman_algo=3/lik_init=3
), the observables must be stationary. This option
is not yet compatible with analytic_derivation.
kalman_tol = DOUBLE
Numerical tolerance for determining the singularity of the covariance matrix of the prediction errors during the Kalman filter (minimum allowed reciprocal of the matrix condition number). Default value is 1e10
diffuse_kalman_tol = DOUBLE
Numerical tolerance for determining the singularity of the covariance matrix of the prediction errors () and the rank of the covariance matrix of the nonstationary state variables () during the Diffuse Kalman filter. Default value is 1e6
filter_covariance
Saves the series of one step ahead error of
forecast covariance matrices. With Metropolis, they are saved in oo_.FilterCovariance,
otherwise in oo_.Smoother.Variance. Saves also kstep ahead error of
forecast covariance matrices if filter_step_ahead
is set.
filter_step_ahead = [INTEGER1:INTEGER2]
See below.
filter_step_ahead = [INTEGER1 INTEGER2 …]
Triggers the computation kstep ahead filtered values, i.e.
. Stores results in
oo_.FilteredVariablesKStepAhead
. Also stores 1step ahead values in oo_.FilteredVariables
.
oo_.FilteredVariablesKStepAheadVariances
is stored if filter_covariance
.
filter_decomposition
Triggers the computation of the shock
decomposition of the above kstep ahead filtered values. Stores results in oo_.FilteredVariablesShockDecomposition
.
smoothed_state_uncertainty
Triggers the computation of the variance of smoothed estimates, i.e.
Var_T(y_t)
. Stores results in oo_.Smoother.State_uncertainty
.
diffuse_filter
Uses the diffuse Kalman filter (as described in Durbin and Koopman (2012) and Koopman and Durbin (2003) for the multivariate and Koopman and Durbin (2000) for the univariate filter) to estimate models with nonstationary observed variables.
When diffuse_filter
is used the lik_init
option of
estimation
has no effect.
When there are nonstationary exogenous variables in a model, there is no unique deterministic steady state. For instance, if productivity is a pure random walk:
any value of of is a deterministic steady state for productivity. Consequently, the model admits an infinity of steady states. In this situation, the user must help Dynare in selecting one steady state, except if zero is a trivial model’s steady state, which happens when the linear
option is used in the model declaration. The user can either provide the steady state to Dynare using a steady_state_model
block (or writing a steady state file) if a closed form solution is available, see steady_state_model, or specify some constraints on the steady state, see equation_tag_for_conditional_steady_state, so that Dynare computes the steady state conditionally on some predefined levels for the non stationary variables. In both cases, the idea is to use dummy values for the steady state level of the exogenous non stationary variables.
Note that the nonstationary variables in the model must be integrated processes (their first difference or kdifference must be stationary).
selected_variables_only
Only run the classical smoother on the variables listed just after the
estimation
command. This option is incompatible with requesting classical
frequentist forecasts and will be overridden in this case. When using Bayesian estimation,
the smoother is by default only run on the declared endogenous variables.
Default: run the smoother on all the
declared endogenous variables.
cova_compute = INTEGER
When 0
, the covariance matrix of estimated parameters is not
computed after the computation of posterior mode (or maximum
likelihood). This increases speed of computation in large models
during development, when this information is not always necessary. Of
course, it will break all successive computations that would require
this covariance matrix. Otherwise, if this option is equal to
1
, the covariance matrix is computed and stored in variable
hh
of ‘MODEL_FILENAME_mode.mat’. Default is 1
.
solve_algo = INTEGER
See solve_algo.
order = INTEGER
Order of approximation, either 1
or 2
. When equal to
2
, the likelihood is evaluated with a particle filter based on
a second order approximation of the model (see
FernandezVillaverde and RubioRamirez (2005)). Default is
1
, ie the likelihood of the linearized model is evaluated
using a standard Kalman filter.
irf = INTEGER
See irf. Only used if bayesian_irf is passed.
irf_shocks = ( VARIABLE_NAME [[,] VARIABLE_NAME …] )
See irf_shocks. Only used if bayesian_irf is passed.
irf_plot_threshold = DOUBLE
See irf_plot_threshold. Only used if bayesian_irf is passed.
aim_solver
See aim_solver.
sylvester = OPTION
See sylvester.
sylvester_fixed_point_tol = DOUBLE
lyapunov = OPTION
Determines the algorithm used to solve the Lyapunov equation to initialized the variancecovariance matrix of the Kalman filter using the steadystate value of state variables. Possible values for OPTION
are:
default
Uses the default solver for Lyapunov equations based on BartelsStewart algorithm.
fixed_point
Uses a fixed point algorithm to solve the Lyapunov equation. This method is faster than the default
one for large scale models, but it could require a large amount of iterations.
doubling
Uses a doubling algorithm to solve the Lyapunov equation (disclyap_fast
). This method is faster than the two previous one for large scale models.
square_root_solver
Uses a squareroot solver for Lyapunov equations
(dlyapchol
). This method is fast for large scale models
(available under MATLAB if the control system toolbox is installed;
available under Octave if the
control package from
OctaveForge is installed)
Default value is default
lyapunov_fixed_point_tol = DOUBLE
This is the convergence criterion used in the fixed point Lyapunov solver. Its default value is 1e10.
lyapunov_doubling_tol = DOUBLE
This is the convergence criterion used in the doubling algorithm to solve the Lyapunov equation. Its default value is 1e16.
use_penalized_objective_for_hessian
Use the penalized objective instead of the objective function to compute numerically the hessian matrix at the mode. The penalties decrease the value of the posterior density (or likelihood) when, for some perturbations, Dynare is not able to solve the model (issues with steady state existence, Blanchard and Kahn conditions, ...). In pratice, the penalized and original objectives will only differ if the posterior mode is found to be near a region where the model is illbehaved. By default the original objective function is used.
analytic_derivation
Triggers estimation with analytic gradient. The final hessian is also
computed analytically. Only works for stationary models without
missing observations, i.e. for kalman_algo<3
.
ar = INTEGER
See ar. Only useful in conjunction with option moments_varendo
.
endogenous_prior
Use endogenous priors as in Christiano, Trabandt and Walentin
(2011).
The procedure is motivated by sequential Bayesian learning. Starting from independent initial priors on the parameters,
specified in the estimated_params
block, the standard deviations observed in a "presample",
taken to be the actual sample, are used to update the initial priors. Thus, the product of the initial
priors and the presample likelihood of the standard deviations of the observables is used as the new prior
(for more information, see the technical appendix of Christiano, Trabandt and Walentin (2011)).
This procedure helps in cases where the regular posterior estimates, which minimize insample forecast
errors, result in a large overprediction
of model variable variances (a statistic that is not explicitly targeted, but often of particular interest to researchers).
use_univariate_filters_if_singularity_is_detected = INTEGER
Decide whether Dynare should automatically switch to univariate filter
if a singularity is encountered in the likelihood computation (this is
the behaviour if the option is equal to 1
). Alternatively, if
the option is equal to 0
, Dynare will not automatically change
the filter, but rather use a penalty value for the likelihood when
such a singularity is encountered. Default: 1
.
keep_kalman_algo_if_singularity_is_detected
With the default use_univariate_filters_if_singularity_is_detected=1, Dynare will switch
to the univariate Kalman filter when it encounters a singular forecast error variance
matrix during Kalman filtering. Upon encountering such a singularity for the first time, all subsequent
parameter draws and computations will automatically rely on univariate filter, i.e. Dynare will never try
the multivariate filter again. Use the keep_kalman_algo_if_singularity_is_detected
option to have the
use_univariate_filters_if_singularity_is_detected
only affect the behavior for the current draw/computation.
rescale_prediction_error_covariance
Rescales the prediction error covariance in the Kalman filter to avoid badly scaled matrix and reduce the probability of a switch to univariate Kalman filters (which are slower). By default no rescaling is done.
qz_zero_threshold = DOUBLE
See qz_zero_threshold.
taper_steps = [INTEGER1 INTEGER2 …]
Percent tapering used for the spectral window in the Geweke (1992,1999)
convergence diagnostics (requires mh_nblocks=1). The tapering is used to
take the serial correlation of the posterior draws into account. Default: [4 8 15]
.
geweke_interval = [DOUBLE DOUBLE]
Percentage of MCMC draws at the beginning and end of the MCMC chain taken
to compute the Geweke (1992,1999) convergence diagnostics (requires mh_nblocks=1)
after discarding the first mh_drop percent of draws as a burnin. Default: [0.2 0.5]
.
raftery_lewis_diagnostics
Triggers the computation of the Raftery and Lewis (1992) convergence diagnostics. The goal is deliver the number of draws
required to estimate a particular quantile of the CDF q
with precision r
with a probability s
. Typically, one wants to estimate
the q=0.025
percentile (corresponding to a 95 percent HPDI) with a precision of 0.5 percent (r=0.005
) with 95 percent
certainty (s=0.95
). The defaults can be changed via raftery_lewis_qrs. Based on the
theory of first order Markov Chains, the diagnostics will provide a required burnin (M
), the number of draws after the burnin (N
)
as well as a thinning factor that would deliver a first order chain (k
). The last line of the table will also deliver the maximum over
all parameters for the respective values.
raftery_lewis_qrs = [DOUBLE DOUBLE DOUBLE]
Sets the quantile of the CDF q
that is estimated with precision r
with a probability s
in the
Raftery and Lewis (1992) convergence diagnostics. Default: [0.025 0.005 0.95]
.
consider_all_endogenous
Compute the posterior moments, smoothed variables, kstep ahead
filtered variables and forecasts (when requested) on all the
endogenous variables. This is equivalent to manually listing all the
endogenous variables after the estimation
command.
consider_only_observed
Compute the posterior moments, smoothed variables, kstep ahead
filtered variables and forecasts (when requested) on all the observed
variables. This is equivalent to manually listing all the observed
variables after the estimation
command.
number_of_particles = INTEGER
Number of particles used when evaluating the likelihood of a non linear state space model. Default: 1000
.
resampling = OPTION
Determines if resampling of the particles is done. Possible values for OPTION are:
none
No resampling.
systematic
Resampling at each iteration, this is the default value.
generic
Resampling if and only if the effective sample size is below a certain level defined by resampling_threshold*number_of_particles.
resampling_threshold = DOUBLE
A real number between zero and one. The resampling step is triggered as soon as the effective number of particles is less than this number times the total number of particles (as set by number_of_particles). This option is effective if and only if option resampling has value generic
.
resampling_method = OPTION
Sets the resampling method. Possible values for OPTION are: kitagawa
, stratified
and smooth
.
filter_algorithm = OPTION
Sets the particle filter algorithm. Possible values for OPTION are:
sis
Sequential importance sampling algorithm, this is the default value.
apf
Auxiliary particle filter.
gf
Gaussian filter.
gmf
Gaussian mixture filter.
cpf
Conditional particle filter.
nlkf
Use a standard (linear) Kalman filter algorithm with the nonlinear measurement and state equations.
proposal_approximation = OPTION
Sets the method for approximating the proposal distribution. Possible values for OPTION are: cubature
, montecarlo
and unscented
. Default value is unscented
.
distribution_approximation = OPTION
Sets the method for approximating the particle distribution. Possible values for OPTION are: cubature
, montecarlo
and unscented
. Default value is unscented
.
cpf_weights = OPTION
Controls the method used to update the weights in conditional particle filter, possible values are amisanotristani
(Amisano et al (2010)) or murrayjonesparslow
(Murray et al. (2013)). Default value is amisanotristani
.
nonlinear_filter_initialization = INTEGER
Sets the initial condition of the
nonlinear filters. By default the nonlinear filters are initialized with the
unconditional covariance matrix of the state variables, computed with the
reduced form solution of the first order approximation of the model. If
nonlinear_filter_initialization=2
, the nonlinear filter is instead
initialized with a covariance matrix estimated with a stochastic simulation of
the reduced form solution of the second order approximation of the model. Both
these initializations assume that the model is stationary, and cannot be used
if the model has unit roots (which can be seen with the check command
prior to estimation). If the model has stochastic trends, user must use
nonlinear_filter_initialization=3
, the filters are then initialized with
an identity matrix for the covariance matrix of the state variables. Default
value is nonlinear_filter_initialization=1
(initialization based on the
first order approximation of the model).
Note
If no mh_jscale
parameter is used for a parameter in estimated_params
,
the procedure uses mh_jscale
for all parameters. If
mh_jscale
option isn’t set, the procedure uses 0.2
for
all parameters. Note that if mode_compute=6
is used or the posterior_sampler_option
called scale_file
is specified, the values set in estimated_params
will be overwritten.
“Endogenous” prior restrictions
It is also possible to impose implicit “endogenous” priors about IRFs and moments on the model during
estimation. For example, one can specify that all valid parameter draws for the model must generate fiscal multipliers that are
bigger than 1 by specifying how the IRF to a government spending shock must look like. The prior restrictions can be imposed
via irf_calibration
and moment_calibration
blocks (see section IRF/Moment calibration). The way it works internally is that
any parameter draw that is inconsistent with the “calibration” provided in these blocks is discarded, i.e. assigned a prior density of 0.
When specifying these blocks, it is important to keep in mind that one won’t be able to easily do model_comparison
in this case,
because the prior density will not integrate to 1.
Output
After running estimation
, the parameters M_.params
and
the variance matrix M_.Sigma_e
of the shocks are set to the
mode for maximum likelihood estimation or posterior mode computation
without Metropolis iterations.
After estimation
with Metropolis iterations (option
mh_replic
> 0 or option load_mh_file
set) the parameters
M_.params
and the variance matrix M_.Sigma_e
of the
shocks are set to the posterior mean.
Depending on the options, estimation
stores results in various
fields of the oo_
structure, described below.
In the following variables, we will adopt the following shortcuts for specific field names:
This field can take the following values:
HPDinf
Lower bound of a 90% HPD interval(3)
HPDsup
Upper bound of a 90% HPD interval
HPDinf_ME
Lower bound of a 90% HPD interval(4) for observables when taking measurement error into account (see e.g. Christoffel et al. (2010), p.17).
HPDsup_ME
Upper bound of a 90% HPD interval for observables when taking measurement error into account
Mean
Mean of the posterior distribution
Median
Median of the posterior distribution
Std
Standard deviation of the posterior distribution
Variance
Variance of the posterior distribution
deciles
Deciles of the distribution.
density
Non parametric estimate of the posterior density following the approach outlined in Skoeld and Roberts (2003). First and second columns are respectively abscissa and ordinate coordinates.
This field can take the following values:
measurement_errors_corr
Correlation between two measurement errors
measurement_errors_std
Standard deviation of measurement errors
parameters
Parameters
shocks_corr
Correlation between two structural shocks
shocks_std
Standard deviation of structural shocks
Variable set by the estimation
command. Stores the marginal data density
based on the Laplace Approximation.
Variable set by the estimation
command, if it is used with
mh_replic > 0
or load_mh_file
option. Stores the marginal data density
based on Geweke (1999) Modified Harmonic Mean estimator.
Variable set by the estimation
command if modefinding is used. Stores the results at the mode.
Fields are of the form

where OBJECT is one of the following:
mode
Parameter vector at the mode
Variance
Inverse Hessian matrix at the mode or MCMC jumping covariance matrix when used with the MCMC_jumping_covariance option
log_density
Log likelihood (ML)/log posterior density (Bayesian) at the mode when used with mode_compute>0
Variable set by the estimation
command if mh_replic>0
is used.
Fields are of the form

where OBJECT is one of the following:
mean
Mean parameter vector from the MCMC
Variance
Covariance matrix of the parameter draws in the MCMC
Variable set by the estimation
command, if it is used with the
filtered_vars
option.
After an estimation without Metropolis, fields are of the form:

After an estimation with Metropolis, fields are of the form:

Variable set by the estimation
command, if it is used with the
filter_step_ahead
option. The ksteps are stored along the rows while the columns
indicate the respective variables. The third dimension of the array provides the
observation for which the forecast has been made. For example, if filter_step_ahead=[1 2 4]
and nobs=200
, the element (3,5,204) stores the four period ahead filtered
value of variable 5 computed at time t=200 for time t=204. The periods at the beginning
and end of the sample for which no forecasts can be made, e.g. entries (1,5,1) and
(1,5,204) in the example, are set to zero. Note that in case of Bayesian estimation
the variables will be ordered in the order of declaration after the estimation
command (or in general declaration order if no variables are specified here). In case
of running the classical smoother, the variables will always be ordered in general
declaration order. If the selected_variables_only option is specified with the classical smoother,
nonrequested variables will be simply left out in this order.
Variable set by the estimation
command, if it is used with the
filter_step_ahead
option. It is a 4 dimensional array where the ksteps
are stored along the first dimension, while the fourth dimension of the array
provides the observation for which the forecast has been made. The second and third
dimension provide the respective variables.
For example, if filter_step_ahead=[1 2 4]
and nobs=200
, the element (3,4,5,204)
stores the four period ahead forecast error covariance between variable 4 and variable 5,
computed at time t=200 for time t=204. Padding with zeros and variable ordering is analogous to oo_.FilteredVariablesKStepAhead
.
Variable set by the estimation
command, if it is used with the filter_step_ahead
option in the context of Bayesian estimation. Fields are of the form:

The nth entry stores the kstep ahead filtered variable computed at time n for time n+k.
Variable set by the estimation
command, if it is used with the
filter_step_ahead
option. The ksteps are stored along the rows while the columns
indicate the respective variables. The third dimension corresponds to the shocks in declaration order.
The fourth dimension of the array provides the
observation for which the forecast has been made. For example, if filter_step_ahead=[1 2 4]
and nobs=200
, the element (3,5,2,204) stores the contribution of the second shock to the
four period ahead filtered value of variable 5 (in deviations from the mean) computed at time t=200 for time t=204. The periods at the beginning
and end of the sample for which no forecasts can be made, e.g. entries (1,5,1) and
(1,5,204) in the example, are set to zero. Padding with zeros and variable ordering is analogous to
oo_.FilteredVariablesKStepAhead
.
Variable set by the estimation
command, if it is used with the
bayesian_irf
option. Fields are of the form:

Variable set by the estimation
command, if it is used with the
smoother
option. Fields are of the form:

Variable set by the estimation
command (if used with the
smoother
option), or by the calib_smoother
command.
After an estimation without Metropolis, or if computed by
calib_smoother
, fields are of the form:

After an estimation with Metropolis, fields are of the form:

Variable set by the estimation
command (if used with the
smoother
option), or by the calib_smoother
command.
After an estimation without Metropolis, or if computed by
calib_smoother
, fields are of the form:

After an estimation with Metropolis, fields are of the form:

Variable set by the estimation
command (if used with the
smoother
option), or by the calib_smoother
command.
Contains the estimation of the expected value of variables given the
information available at the current date.
After an estimation without Metropolis, or if computed by
calib_smoother
, fields are of the form:

After an estimation with Metropolis, fields are of the form:

Threedimensional array set by the estimation
command if used with the
smoother
and Metropolis, if the filter_covariance
option
has been requested.
Contains the series of onestep ahead forecast error covariance matrices
from the Kalman smoother. The M_.endo_nbr
times M_.endo_nbr
times
T+1
array contains the variables in declaration order along the first
two dimensions. The third dimension of the array provides the
observation for which the forecast has been made.
Fields are of the form:

Note that density estimation is not supported.
Threedimensional array set by the estimation
command (if used with the
smoother
) without Metropolis,
or by the calib_smoother
command, if the filter_covariance
option
has been requested.
Contains the series of onestep ahead forecast error covariance matrices
from the Kalman smoother. The M_.endo_nbr
times M_.endo_nbr
times
T+1
array contains the variables in declaration order along the first
two dimensions. The third dimension of the array provides the
observation for which the forecast has been made.
Threedimensional array set by the estimation
command (if used with the
smoother
) without Metropolis,
or by the calib_smoother
command, if the o_smoothed_state_uncertainty
option
has been requested.
Contains the series of covariance matrices for the state estimate given the full data
from the Kalman smoother. The M_.endo_nbr
times M_.endo_nbr
times
T
array contains the variables in declaration order along the first
two dimensions. The third dimension of the array provides the
observation for which the smoothed estimate has been made.
Variable set by the estimation
command (if used with the
smoother
) without Metropolis,
or by the calib_smoother
command.
Contains the steady state component of the endogenous variables used in the
smoother in order of variable declaration.
Variable set by the estimation
command (if used with the
smoother
) without Metropolis,
or by the calib_smoother
command.
Contains the trend coefficients of the observed variables used in the
smoother in order of declaration of the observed variables.
Variable set by the estimation
command (if used with the
smoother
option), or by the calib_smoother
command.
Contains the trend component of the variables used in the
smoother.
Fields are of the form:

Variable set by the estimation
command (if used with the
smoother
option), or by the calib_smoother
command.
Contains the constant part of the endogenous variables used in the
smoother, accounting e.g. for the data mean when using the prefilter
option.
Fields are of the form:

Indicator keeping track of whether the smoother was run with the loglinear option and thus whether stored smoothed objects are in logs.
Variable set by the estimation
command, if it is used with the
moments_varendo
option. Fields are of the form:

where THEORETICAL_MOMENT is one of the following:
covariance
Variancecovariance of endogenous variables
contemporaneous_correlation
Contemporaneous correlation of endogenous variables when the contemporaneous_correlation option is specified.
correlation
Auto and crosscorrelation of endogenous variables. Fields are vectors with correlations from 1 up to order options_.ar
VarianceDecomposition
Decomposition of variance (unconditional variance, i.e. at horizon infinity)(5)
ConditionalVarianceDecomposition
Only if the conditional_variance_decomposition
option has been
specified
Variable set by the estimation
command, if it is used with
mh_replic > 0
or load_mh_file
option. Fields are of the form:

Variable set by the estimation
command, if it is used with
mh_replic > 0
or load_mh_file
option. Fields are of the form:

Variable set by the estimation
command, if it is used with
mh_replic > 0
or load_mh_file
option. Fields are of the form:

Variable set by the estimation
command, if it is used with
mh_replic > 0
or load_mh_file
option. Fields are of the form:

Variable set by the estimation
command during modefinding. Fields are
of the form:

Variable set by the estimation
command during modefinding. It is based on the
inverse Hessian at oo_.posterior_mode
. Fields are
of the form:

Variable set by the estimation
command, if it is used with
mh_replic > 0
or load_mh_file
option. Fields are of the form:

Variable set by the estimation
command, if it is used with
mh_replic > 0
or load_mh_file
option. Fields are of the form:

Variable set by the estimation
command, if it is used with
mh_replic > 0
or load_mh_file
option. Fields are of the form:

Here are some examples of generated variables:
oo_.posterior_mode.parameters.alp oo_.posterior_mean.shocks_std.ex oo_.posterior_hpdsup.measurement_errors_corr.gdp_conso 
Structure set by the dsge_var
option of the estimation
command after mode_compute
.
The following fields are saved:
PHI_tilde
Stacked posterior DSGEBVAR autoregressive matrices at the mode (equation (28) of Del Negro and Schorfheide (2004)).
SIGMA_u_tilde
Posterior covariance matrix of the DSGEBVAR at the mode (equation (29) of Del Negro and Schorfheide (2004)).
iXX
Posterior population moments in the DSGEBVAR at the mode ( ).
prior
Structure storing the DSGEBVAR prior.
PHI_star
Stacked prior DSGEBVAR autoregressive matrices at the mode (equation (22) of Del Negro and Schorfheide (2004)).
SIGMA_star
Prior covariance matrix of the DSGEBVAR at the mode (equation (23) of Del Negro and Schorfheide (2004)).
ArtificialSampleSize
Size of the artifical prior sample ( ).
DF
Prior degrees of freedom ( ).
iGXX_star
Inverse of the theoretical prior “covariance” between X and X ( in Del Negro and Schorfheide (2004)).
Variable set by the forecast
option of the estimation
command when used with the nobs = [INTEGER1:INTEGER2] option (see nobs).
Fields are of the form:

where FORECAST_OBJECT is one of the following(6):
Mean
Mean of the posterior forecast distribution
HPDinf/HPDsup
Upper/lower bound of the 90% HPD interval taking into account only parameter uncertainty (corresponding to oo_.MeanForecast)
HPDTotalinf/HPDTotalsup
Upper/lower bound of the 90% HPD interval taking into account both parameter and future shock uncertainty (corresponding to oo_.PointForecast)
VARIABLE_NAME contains a matrix of the following size: number of time periods for which forecasts are requested using the nobs = [INTEGER1:INTEGER2] option times the number of forecast horizons requested by the forecast
option. i.e., the row indicates the period at which the forecast is performed and the column the respective kstep ahead forecast. The starting periods are sorted in ascending order, not in declaration order.
Variable set by the convergence diagnostics of the estimation
command when used with mh_nblocks=1 option (see mh_nblocks).
Fields are of the form:

where DIAGNOSTIC_OBJECT is one of the following:
posteriormean
Mean of the posterior parameter distribution
posteriorstd
Standard deviation of the posterior parameter distribution
nse_iid
Numerical standard error (NSE) under the assumption of iid draws
rne_iid
Relative numerical efficiency (RNE) under the assumption of iid draws
nse_x
Numerical standard error (NSE) when using an x% taper
rne_x
Relative numerical efficiency (RNE) when using an x% taper
pooled_mean
Mean of the parameter when pooling the beginning and end parts of the chain specified in geweke_interval and weighting them with their relative precision. It is a vector containing the results under the iid assumption followed by the ones using the taper_steps (see taper_steps).
pooled_nse
NSE of the parameter when pooling the beginning and end parts of the chain and weighting them with their relative precision. See pooled_mean
prob_chi2_test
pvalue of a chi squared test for equality of means in the beginning and the end
of the MCMC chain. See pooled_mean
. A value above 0.05 indicates that
the null hypothesis of equal means and thus convergence cannot be rejected
at the 5 percent level. Differing values along the taper_steps signal
the presence of significant autocorrelation in draws. In this case, the
estimates using a higher tapering are usually more reliable.
This command is deprecated. Use estimation
option diffuse_filter
instead for estimating a model with nonstationary observed variables or steady
option nocheck
to prevent steady
to check the steady state returned by your steady state file.
Dynare also has the ability to estimate Bayesian VARs:
Computes the marginal density of an estimated BVAR model, using Minnesota priors.
See ‘bvaralasims.pdf’, which comes with Dynare distribution, for more information on this command.
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