Reproducing Business Cycle Features: Are Nonlinear Dynamics a Proxy for Multivariate Information?
AbstractWe consider the extent to which different time-series models can generate simulated data with the same business cycle features that are evident in U.S. real GDP. We focus our analysis on whether multivariate linear models can improve on the previously documented failure of univariate linear models to replicate certain key business cycle features. We find that a particular nonlinear Markov-switching specification with an explicit “bounceback” effect continues to outperform linear models, even when the models incorporate variables such as the unemployment rate, inflation, interest rates, and the components of GDP. These results are robust to simulated data generated either using Normal disturbances or bootstrapped disturbances, as well as to allowing for a one-time structural break in the variance of shocks to real GDP growth.
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Bibliographic InfoPaper provided by School of Economics, The University of New South Wales in its series Discussion Papers with number 2012-23.
Length: 37 pages
Date of creation: Mar 2012
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More information through EDIRC
Business cycle features; nonlinear dynamics; multivariate models.;
Find related papers by JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-05-02 (All new papers)
- NEP-BEC-2012-05-02 (Business Economics)
- NEP-ETS-2012-05-02 (Econometric Time Series)
- NEP-MAC-2012-05-02 (Macroeconomics)
- NEP-ORE-2012-05-02 (Operations Research)
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