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

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 (c) 2012 John Wiley & Sons, Ltd.