Working Paper: CEPR ID: DP6791
Authors: Fabio Canova
Abstract: This chapter highlights the problems that structural methods and SVAR approaches have when estimating DSGE models and examining their ability to capture important features of the data. We show that structural methods are subject to severe identification problems due, in large part, to the nature of DSGE models. The problems can be patched up in a number of ways, but solved only if DSGEs are completely reparametrized or respecified. The potential misspecification of the structural relationships give Bayesian methods an hedge over classical ones in structural estimation. SVAR approaches may face invertibility problems but simple diagnostics can help to detect and remedy these problems. A pragmatic empirical approach ought to use the flexibility of SVARs against potential misspecification of the structural relationships but must firmly tie SVARs to the class of DSGE models which could have have generated the data.
Keywords: DSGE models; identification; invertibility; SVAR models
JEL Codes: C10; C52; E32; E50
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Structural methods (C10) | Identification problems (C30) |
| Identification problems (C30) | Biased parameter estimates (C51) |
| Biased parameter estimates (C51) | Non-convergence to true DGP (E19) |
| Observational equivalence (C90) | Obscured identification of structural parameters (C51) |
| Limited information objective functions (L21) | Limited information identification problems (D82) |
| Small sample sizes (C83) | Complicated recovery of structural parameters (C51) |
| Bayesian methods (C11) | Incorporation of prior information (D83) |
| Bayesian methods (C11) | Mimicking of prior distribution (C59) |
| Mimicking of prior distribution (C59) | Resembling sophisticated calibration (C51) |