Dynamic decision making with predictive panels
33 Pages Posted: 3 Jun 2021 Last revised: 6 Apr 2022
Date Written: March 12, 2022
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.
Keywords: Predictive Regressions, Panel Models, Error Decomposition, Out-of-Sample Accuracy, Distribution Shifts
JEL Classification: C33, C58, G17
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