Dynamic decision making with predictive panels

This paper studies the dynamics of realized accuracy obtained with predictive panel models. A decision maker is affected by a loss of accuracy from an estimated model with respect to out-of-sample data. We investigate the link between this loss of accuracy and changes in the distribution of the underlying data from the estimation phase (in-sample) to the out-of-sample tests. We then model the norms of distributional changes with positive autoregressive processes in order to predict the loss of accuracy. Based on two different financial datasets, our empirical results show that our indicators have a strong explanatory power over realized portfolio returns.