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Asymmetric forecast densities for U.S. macroeconomic variables from a Gaussian Copula Model of cross-Sectional and serial dependence
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Smith, Michael S. and Vahey, Shaun P. (2016) Asymmetric forecast densities for U.S. macroeconomic variables from a Gaussian Copula Model of cross-Sectional and serial dependence. Journal of Business & Economic Statistics , 34 (3). pp. 416-434. doi:10.1080/07350015.2015.1044533 ISSN 0735-0015.
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Official URL: http://dx.doi.org/10.1080/07350015.2015.1044533
Abstract
Most existing reduced-form macroeconomic multivariate time series models employ elliptical disturbances, so that the forecast densities produced are symmetric. In this article, we use a copula model with asymmetric margins to produce forecast densities with the scope for severe departures from symmetry. Empirical and skew t distributions are employed for the margins, and a high-dimensional Gaussian copula is used to jointly capture cross-sectional and (multivariate) serial dependence. The copula parameter matrix is given by the correlation matrix of a latent stationary and Markov vector autoregression (VAR). We show that the likelihood can be evaluated efficiently using the unique partial correlations, and estimate the copula using Bayesian methods. We examine the forecasting performance of the model for four U.S. macroeconomic variables between 1975:Q1 and 2011:Q2 using quarterly real-time data. We find that the point and density forecasts from the copula model are competitive with those from a Bayesian VAR. During the recent recession the forecast densities exhibit substantial asymmetry, avoiding some of the pitfalls of the symmetric forecast densities from the Bayesian VAR. We show that the asymmetries in the predictive distributions of GDP growth and inflation are similar to those found in the probabilistic forecasts from the Survey of Professional Forecasters. Last, we find that unlike the linear VAR model, our fitted Gaussian copula models exhibit nonlinear dependencies between some macroeconomic variables. This article has online supplementary material.
| Item Type: | Journal Article | ||||||||
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| Divisions: | Faculty of Social Sciences > Warwick Business School > Global Energy Faculty of Social Sciences > Warwick Business School |
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| Journal or Publication Title: | Journal of Business & Economic Statistics | ||||||||
| Publisher: | Routledge | ||||||||
| ISSN: | 0735-0015 | ||||||||
| Official Date: | 2016 | ||||||||
| Dates: |
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| Volume: | 34 | ||||||||
| Number: | 3 | ||||||||
| Page Range: | pp. 416-434 | ||||||||
| DOI: | 10.1080/07350015.2015.1044533 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access |
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