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On a calibrated model, forecasting is done using the forecast
command. On an estimated model, use the forecast
option of
estimation
command.
It is also possible to compute forecasts on a calibrated or estimated
model for a given constrained path of the future endogenous
variables. This is done, from the reduced form representation of the
DSGE model, by finding the structural shocks that are needed to match
the restricted paths. Use conditional_forecast
,
conditional_forecast_paths
and plot_conditional_forecast
for that purpose.
Finally, it is possible to do forecasting with a Bayesian VAR using
the bvar_forecast
command.
Description
This command computes a simulation of a stochastic model from an arbitrary initial point.
When the model also contains deterministic exogenous shocks, the simulation is computed conditionaly to the agents knowing the future values of the deterministic exogenous variables.
forecast
must be called after stoch_simul
.
forecast
plots the trajectory of endogenous variables. When a
list of variable names follows the command, only those variables are
plotted. A 90% confidence interval is plotted around the mean
trajectory. Use option conf_sig
to change the level of the
confidence interval.
Options
periods = INTEGER
Number of periods of the forecast. Default: 40
conf_sig = DOUBLE
Level of significance for confidence
interval. Default: 0.90
nograph
See nograph.
nodisplay
See nodisplay.
graph_format = FORMAT
graph_format = ( FORMAT, FORMAT… )
See graph_format.
Initial Values
forecast
computes the forecast taking as initial values the values specified in histval
(see section histval). When no histval
block is present, the initial values are the one stated in initval
. When initval
is followed by command steady
, the initial values are the steady state (see section steady).
Output
The results are stored in oo_.forecast
, which is described below.
Example
varexo_det tau; varexo e; … shocks; var e; stderr 0.01; var tau; periods 1:9; values -0.15; end; stoch_simul(irf=0); forecast; |
Variable set by the forecast
command, or by the
estimation
command if used with the forecast
option and
if no Metropolis-Hastings has been computed (in that case, the
forecast is computed for the posterior mode). Fields are of the form:
|
where FORECAST_MOMENT is one of the following:
HPDinf
Lower bound of a 90% HPD interval(5) of forecast due to parameter uncertainty
HPDsup
Lower bound of a 90% HPD interval due to parameter uncertainty
HPDTotalinf
Lower bound of a 90% HPD interval of forecast due to parameter
uncertainty and future shocks (only with the estimation
command)
HPDTotalsup
Lower bound of a 90% HPD interval due to parameter uncertainty and
future shocks (only with the estimation
command)
Mean
Mean of the posterior distribution of forecasts
Median
Median of the posterior distribution of forecasts
Std
Standard deviation of the posterior distribution of forecasts
Set by the estimation
command, if it is used with the
forecast
option and if either mh_replic > 0
or
load_mh_file
option is used.
Contains the distribution of forecasts taking into account the uncertainty about both parameters and shocks.
Fields are of the form:
|
Set by the estimation
command, if it is used with the
forecast
option and if either mh_replic > 0
or
load_mh_file
option is used.
Contains the distribution of forecasts where the uncertainty about shocks is averaged out. The distribution of forecasts therefore only represents the uncertainty about parameters.
Fields are of the form:
|
Description
This command computes forecasts on an estimated model for a given constrained path of some future endogenous variables. This is done, from the reduced form representation of the DSGE model, by finding the structural shocks that are needed to match the restricted paths. This command has to be called after estimation.
Use conditional_forecast_paths
block to give the list of
constrained endogenous, and their constrained future path. Option
controlled_varexo
is used to specify the structural shocks
which will be matched to generate the constrained path.
Use plot_conditional_forecast
to graph the results.
Options
parameter_set = calibration
| prior_mode
| prior_mean
| posterior_mode
| posterior_mean
| posterior_median
Specify the parameter set to use for the forecasting. No default value, mandatory option.
controlled_varexo = (VARIABLE_NAME…)
Specify the exogenous variables to use as control variables. No default value, mandatory option.
periods = INTEGER
Number of periods of the forecast. Default: 40
. periods
cannot be less than the number of constrained periods.
replic = INTEGER
Number of simulations. Default: 5000
.
conf_sig = DOUBLE
Level of significance for confidence interval. Default: 0.80
Example
var y a varexo e u; … estimation(…); conditional_forecast_paths; var y; periods 1:3, 4:5; values 2, 5; var a; periods 1:5; values 3; end; conditional_forecast(parameter_set = calibration, controlled_varexo = (e, u), replic = 3000); plot_conditional_forecast(periods = 10) a y; |
Describes the path of constrained endogenous, before calling
conditional_forecast
. The syntax is similar to deterministic
shocks in shocks
, see conditional_forecast
for an
example.
The syntax of the block is the same than the deterministic shocks in
the shocks
blocks (see section Shocks on exogenous variables).
Description
Plots the conditional (plain lines) and unconditional (dashed lines) forecasts.
To be used after conditional_forecast
.
Options
periods = INTEGER
Number of periods to be plotted. Default: equal to periods
in
conditional_forecast
. The number of periods declared in
plot_conditional_forecast
cannot be greater than the one
declared in conditional_forecast
.
This command computes (out-of-sample) forecasts for an estimated BVAR model, using Minnesota priors.
See ‘bvar-a-la-sims.pdf’, which comes with Dynare distribution, for more information on this command.
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