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Selecting structural innovations in DSGE models
Author(s) -
Ferroni Filippo,
Grassi Stefano,
LeónLedesma Miguel A.
Publication year - 2019
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2664
Subject(s) - dynamic stochastic general equilibrium , economics , econometrics , prior probability , general equilibrium theory , monetary policy , bayes estimator , macroeconomics , computer science , bayesian probability , artificial intelligence
Summary Dynamic stochastic general equilibrium (DSGE) models are typically estimated assuming the existence of certain structural shocks that drive macroeconomic fluctuations. We analyze the consequences of estimating shocks that are “nonexistent” and propose a method to select the economic shocks driving macroeconomic uncertainty. Forcing these nonexisting shocks in estimation produces a downward bias in the estimated internal persistence of the model. We show how these distortions can be reduced by using priors for standard deviations whose support includes zero. The method allows us to accurately select shocks and estimate model parameters with high precision. We revisit the empirical evidence on an industry standard medium‐scale DSGE model and find that government and price markup shocks are innovations that do not generate statistically significant dynamics.