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On the predictive content of autoregression residuals: a semiparametric, copula-based approach to time series prediction. (English) Zbl 1397.62309

Summary: 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 S. Dees et al. [“Exploring the international linkages of the euro area: a global VAR analysis”, J. Appl. Econom. 22, No. 1, 1–38 (2007; doi:10.1002/jae.932)]. 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.

MSC:

62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
91B84 Economic time series analysis
62P20 Applications of statistics to economics
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