Hello everyone! Please could I ask a question about how Dynare handles exogenous regressors in a model? I have been looking through the manual, source code and forums, and I am still a bit lost on what Dynare is doing ‘under the hood’.

The general idea is to add some additional exogenous time series into an otherwise standard 3 equation NK model and weight this channel using the model’s structure. A silly model example (that I hope to elaborate on) and data is attached where consumer confidence is imposed into the NKPC such that (1+\rho)^{-1} E_t(\pi_{t+1}) = betaEST*confidence_t + \pi_t - \phi (y_t - z^s_t), where betaEST is the estimator of interest.

I followed the advice of jpfeifer on how to introduce exogenous regressors in Dynare (How to estimate DSGE when R is exogenous and observed? - #4 by jpfeifer) and the estimation works, I’m just not too sure how Dynare is processing the series.

My question is: does Dynare introduce this confidence time series as an exogenous regressor in the state space representation? (a la Hamilton (1994):

). Or is it more like the agent’s know this series at date zero and they take it as given? Pg. 40 of the manual says:

Blockquote

“It is possible to mix deterministic and stochastic shocks to build models where agents know from the start of the simulation about future exogenous changes. In that case stoch_simul will compute the rational expectation solution adding future information to the state space (nothing is shown in the output of stoch_simul) and forecast will compute a simulation conditional on initial conditions and future information.”

Is this true when it comes to estimation as well?

I am sorry if this is a trivial question or has been answered elsewhere.

Thank you so much, I really appreciate it.

Erica

exog_var_est.mod (4.0 KB)

matNKdata3.mat (7.4 KB)