Forecasting out of sample


I have completed Bayesian estimation based on the entire data sample I have. For this case, I found out how to do k-step ahead forecast.

Now, 4 more quarters of data have been released. I do not want to re-run estimation with these additional data, but would like to forecast using the previously estimated model starting at the end of newly available data. Is this possible to accomplish in DYNARE?

Thank you.

You can run the calib_smoother based on the old parameter values on the new data. See

Thank you.

Could I ask if k-step ahead forecast is available in an array/vector form? _.FilteredVariablesKStepAhead is text with no definition of displayed columns, also not so straightforward to copy individual columns.

Also, what is the difference between filtered_vars and forecast?

What do you mean? oo_.FilteredVariablesKStepAhead is a three dimensional array

MATLAB/Octave variable: oo_.FilteredVariablesKStepAhead

Variable set by the estimation command, if it is used with the filter_step_ahead option. The k-steps 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, non-requested variables will be simply left out in this order.

  1. The forecast is for the end of the sample. The filtered variables provide forecasts at each point within the sample.

Thank you