Bootstrapping DSGE models

Angelini, Giovanni ; Cavaliere, Giuseppe ; Fanelli, Luca (2016) Bootstrapping DSGE models. Bologna, IT: Dipartimento di Scienze Statistiche "Paolo Fortunati", Alma Mater Studiorum Università di Bologna, p. 29. DOI 10.6092/unibo/amsacta/5412. In: Quaderni di Dipartimento. Serie Ricerche (3). ISSN 1973-9346.
Full text available as:
[thumbnail of Quaderni_2016_3_AngeliniCavaliereFanelli_Bootstrapping.pdf]
Preview
Text(pdf)
License: Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0 (CC BY-NC-ND 3.0)

Download (522kB) | Preview

Abstract

This paper explores the potential of bootstrap methods in the empirical evalu- ation of dynamic stochastic general equilibrium (DSGE) models and, more generally, in linear rational expectations models featuring unobservable (latent) components. We consider two dimensions. First, we provide mild regularity conditions that suffice for the bootstrap Quasi- Maximum Likelihood (QML) estimator of the structural parameters to mimic the asymptotic distribution of the QML estimator. Consistency of the bootstrap allows to keep the probability of false rejections of the cross-equation restrictions under control. Second, we show that the realizations of the bootstrap estimator of the structural parameters can be constructively used to build novel, computationally straightforward tests for model misspecification, including the case of weak identification. In particular, we show that under strong identification and boot- strap consistency, a test statistic based on a set of realizations of the bootstrap QML estimator approximates the Gaussian distribution. Instead, when the regularity conditions for inference do not hold as e.g. it happens when (part of) the structural parameters are weakly identified, the above result is no longer valid. Therefore, we can evaluate how close or distant is the esti- mated model from the case of strong identification. Our Monte Carlo experimentations suggest that the bootstrap plays an important role along both dimensions and represents a promising evaluation tool of the cross-equation restrictions and, under certain conditions, of the strength of identification. An empirical illustration based on a small-scale DSGE model estimated on U.S. quarterly observations shows the practical usefulness of our approach.

Abstract
Document type
Monograph (Working Paper)
Creators
CreatorsAffiliationORCID
Angelini, GiovanniUniversità di Bologna0000-0003-3000-9885
Cavaliere, GiuseppeUniversità di Bologna0000-0002-2856-0005
Fanelli, LucaUniversità di Bologna0000-0001-5351-2876
Keywords
Bootstrap, Cross-equation restrictions, DSGE, QLR test, State space model, Weak identification.
Subjects
ISSN
1973-9346
DOI
Deposit date
16 Sep 2016 10:07
Last modified
08 May 2017 13:25
Project name
MISURA - Multivariate statistical models for risk assessment
Funding program
MIUR - PRIN
URI

Other metadata

Downloads

Downloads

Staff only: View the document

^