On the Predictive Content of Autoregression Residuals: A Semiparametric, Copula-Based Approach to Time Series Prediction

Authors

  • Helmut Herwartz

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    • Institute for Statistics and Econometrics, Christian-Albrechts-University Kiel, Olshausenstrasse 40-60, D-24118 Kiel, Germany
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Correspondence to: Lutz Kilian, Institute for Statistics and Econometrics, Christian-Albrechts-University Kiel, Olshausenstrasse 40-60, D-24118 Kiel, Germany. E-mail: H.Herwartz@stat-econ.uni-kiel.de

ABSTRACT

This paper proposes an adjustment of linear autoregressive conditional mean forecasts that exploits the predictive content of uncorrelated model residuals. The adjustment is motivated by non-Gaussian characteristics of model residuals, and implemented in a semiparametric fashion by means of conditional moments of simulated bivariate distributions. A pseudo ex ante forecasting comparison is conducted for a set of 494 macroeconomic time series recently collected by Dees et al. (Journal of Applied Econometrics 2007; 22: 1–38). In total, 10,374 time series realizations are contrasted against competing short-, medium- and longer-term purely autoregressive and adjusted predictors. With regard to all forecast horizons, the adjusted predictions consistently outperform conditionally Gaussian forecasts according to cross-sectional mean group evaluation of absolute forecast errors and directional accuracy. Copyright © 2012 John Wiley & Sons, Ltd.

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