Working Paper: CEPR ID: DP10140
Authors: Atsushi Inoue; Chunhung Kuo; Barbara Rossi
Abstract: In this paper we propose empirical methods for detecting and identifying misspecifications in DSGE models. We introduce wedges in a DSGE model and identify potential misspecification via forecast error variance decomposition (FEVD) and marginal likelihood analyses. Our simulation results based on a small-scale DSGE model demonstrate that our method can correctly identify the source of misspecification. Our empirical results show that the medium-scale New Keynesian DSGE model that incorporates features in the recent empirical macro literature is still very much misspecified; our analysis highlights that the asset and labor markets may be the source of the misspecification.
Keywords: DSGE models; empirical macroeconomics; model misspecification
JEL Codes: C32; E32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| proposed empirical methods (C59) | identify sources of model misspecification (C50) |
| introduction of wedges (Y20) | identify model misspecification (C50) |
| medium-scale New Keynesian DSGE model (E12) | misspecified (C50) |
| model misspecification (C52) | observed contributions of wedges (D33) |
| FEVD and marginal likelihood analyses (C52) | understanding of model structure (C20) |