Mind your ps and qs! Improving ARMA forecasts with RBC priors
AbstractWe utilise prior information from a simple RBC model to improve ARMA forecasts of post-war US GDP. We develop three alternative ARMA forecasting processes that use varying degrees of information from the Campbell (1994) flexible labour model. Directly calibrating the model produces poor forecasting performance whereas a model that uses a Bayesian framework to take the model to the data, yields forecasting performance comparable to a purely statistical ARMA process. A final model that uses theory only to restrict the order of the ARMA process (the ps and qs), but that estimates the ARMA parameters using maximum likelihood, yields improved forecasting performance.
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Bibliographic InfoArticle provided by Elsevier in its journal Economics Letters.
Volume (Year): 96 (2007)
Issue (Month): 2 (August)
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Web page: http://www.elsevier.com/locate/ecolet
Other versions of this item:
- Kirdan Lees & Troy Matheson, 2005. "Mind your Ps and Qs! Improving ARMA forecasts with RBC priors," Reserve Bank of New Zealand Discussion Paper Series DP2005/02, Reserve Bank of New Zealand.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
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