Stata now fits linearized DSGE models, which are time-series models used in economics and finance. These models are an alternative to traditional forecasting models. Both attempt to explain aggregate economic phenomena, but DSGE models do this on the basis of models derived from microeconomic theory.
Being based on microeconomic theory means lots of equations. The key feature of these equations is that expectations of future variables affect variables today. This is one feature that distinguishes DSGEs from a vector autoregression or a state-space model. The other feature is that, being derived from theory, the parameters can usually be interpreted in terms of that theory.
After fitting a DSGE model,
estat policy and
estat transition can report the policy and transition matrices. You can produce forecasts using Stata’s existing forecast command, and you can graph impulse-response functions using Stata’s existing