Working Paper: CEPR ID: DP17123
Authors: M Hashem Pesaran; Andreas Pick; Allan Timmermann
Abstract: We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with existing approaches. We conduct a systematic comparison of their predictive accuracy in settings with different cross-sectional (N) and time (T) dimensions and varying degrees of parameter heterogeneity. We investigate conditions under which panel forecasting methods can perform better than forecasts based on individual estimates and demonstrate how gains in predictive accuracy depend on the degree of parameter heterogeneity, whether heterogeneity is correlated with the regressors, the goodness of fit of the model, and, particularly, the time dimension of the data set. We propose optimal combination weights for forecasts based on pooled and individual estimates and develop a novel forecast poolability test that can be used as a pretesting tool. Through a set of Monte Carlo simulations and three empirical applications to house prices, CPI inflation, and stock returns, we show that no single forecasting approach dominates uniformly. However, forecast combination and shrinkage methods provide better overall forecasting performance and offer more attractive risk profiles compared to individual, pooled, and random effects methods.
Keywords: Panel Data; Heterogeneity; Forecast Evaluation; Forecast Combination; Shrinkage; Pooling
JEL Codes: C33; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| multiple factors (C39) | gains in predictive accuracy (C52) |
| pooled estimates (C13) | median MSFE value (C51) |
| combination forecasts (C53) | individual estimates performance (C13) |
| individual estimates (C13) | pooled estimates performance (C51) |
| pooled estimates (C13) | accuracy in forecasting stock returns (G17) |
| Bayesian and empirical Bayes shrinkage forecasts (C11) | average MSFE values for many stocks (G17) |
| degree of parameter heterogeneity (C46) | performance of forecasting methods (C53) |
| individual estimates and shrinkage forecasts (C51) | loss distributions (G22) |
| pooled and individual estimates (C13) | optimal combination weights for forecasts (C53) |