Skip to main content
padlock icon - secure page this page is secure

Estimating dynamic macroeconomic models: how informative are the data?

Buy Article:

$43.00 plus tax (Refund Policy)

  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.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

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: 01 February 2018

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more