Evidence on a Real Business Cycle Model with Neutral and Investment-Specific Technology Shocks using Bayesian Model Averaging
AbstractThe empirical support for a real business cycle model with two technology shocks is evaluated using a Bayesian model averaging procedure. This procedure makes use of a finite mixture of many models within the class ofvector autoregressive (VAR) processes. The linear VAR model is extendedto permit cointegration, a range of deterministic processes, equilibrium restrictions and restrictions on long-run responses to technology shocks. Wefind support for a number of the features implied by the real business cyclemodel. For example, restricting long run responses to identify technologyshocks has reasonable support and important implications for the short runresponses to these shocks. Further, there is evidence that savings and investment ratios form stable relationships, but technology shocks do not accountfor all stochastic trends in our system. There is uncertainty as to the mostappropriate model for our data, with thirteen models receiving similar support, and the model or model set used has signficant implications for theresults obtained.
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Bibliographic InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 10-050/4.
Date of creation: 17 May 2010
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Posterior probability; Real business cycle model; Cointegration; Model averaging; Stochastic trend; Impulse response; Vector autoregressive model;
Other versions of this item:
- Rodney W. Strachan & Herman K. van Dijk, 2010. "Evidence on a Real Business Cycle model with Neutral and Investment-Specific Technology Shocks using Bayesian Model Averaging," ANU Working Papers in Economics and Econometrics 2010-522, Australian National University, College of Business and Economics, School of Economics.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-02-26 (All new papers)
- NEP-BEC-2011-02-26 (Business Economics)
- NEP-DGE-2011-02-26 (Dynamic General Equilibrium)
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