Working Paper: CEPR ID: DP7176
Authors: Agostino Consolo; Carlo A. Favero; Alessia Paccagnini
Abstract: Dynamic Stochastic General Equilibrium (DSGE) models are now considered attractive by the profession not only from the theoretical perspective but also from an empirical standpoint. As a consequence of this development, methods for diagnosing the fit of these models are being proposed and implemented. In this article we illustrate how the concept of statistical identification, that was introduced and used by Spanos(1990) to criticize traditional evaluation methods of Cowles Commission models, could be relevant for DSGE models. We conclude that the recently proposed model evaluation method, based on the DSGE-VAR(ë), might not satisfy the condition for statistical identification. However, our application also shows that the adoption of a FAVAR as a statistically identified benchmark leaves unaltered the support of the data for the DSGE model and that a DSGE-FAVAR can be an optimal forecasting model.
Keywords: Bayesian analysis; Dynamic stochastic general equilibrium model; Factor-augmented vector autoregression; Model evaluation; Statistical identification
JEL Codes: C11; C52
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Traditional evaluation methods for Cowles Commission models (C52) | Misinterpretations of the structural models (C20) |
| DSGE-VAR evaluation methods may not satisfy statistical identification conditions (C32) | FAVAR could serve as a better benchmark (C52) |
| Adopting a FAVAR as a statistically identified benchmark does not alter the support of the data for the DSGE model (C51) | Statistical identification improves the robustness of empirical analyses (C14) |
| Use of a FAVAR allows for a more comprehensive evaluation of the DSGE model's performance (E17) | Enhances forecasting accuracy (C53) |
| FAVAR improves identification (C52) | Does not eliminate all concerns regarding model specification (C51) |