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Estimating dynamic macroeconomic models: how informative are the data?
Author(s) -
Beltran Daniel O.,
Draper David
Publication year - 2018
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12238
Subject(s) - econometrics , bayesian probability , computer science , identification (biology) , markov chain monte carlo , monte carlo method , dynamic stochastic general equilibrium , economics , statistics , monetary policy , mathematics , artificial intelligence , botany , biology , monetary economics
Summary Central banks have long used dynamic stochastic general equilibrium models, which are typically estimated by using Bayesian techniques, to inform key policy decisions. This paper offers an empirical strategy that quantifies the information content of the data relative to that of the prior distribution. Using an off‐the‐shelf dynamic stochastic general equilibrium model applied to quarterly euro area data from 1970, quarter 3, to 2009, quarter 4, we show how Monte Carlo simulations can reveal parameters for which the model's structure obscures identification. By integrating out components of the likelihood function and conducting a Bayesian sensitivity analysis, we uncover parameters that are weakly informed by the data. The weak identification of some key structural parameters in our comparatively simple model should raise a red flag to researchers trying to draw valid inferences from, and to base policy on, complex large‐scale models featuring many parameters.