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Estimating dynamic macroeconomic models: how informative are the data?

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  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.
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Keywords: Bayesian estimation; Econometric modelling; Euro area; Kalman filter; Likelihood; Local identification; Markov chain Monte Carlo sampling; Policy relevant parameters; Prior‐versus‐posterior comparison; Sensitivity analysis

Document Type: Research Article

Publication date: February 1, 2018

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