Panel Forecasting with Asymmetric Grouping

This paper proposes an asymmetric grouping estimator for panel data forecasting. The estimator relies on the observation that the biasvariance trade-off in potentially heterogeneous panel data may be different across individuals. Hence, the group of individuals used for parameter estimation that is optimal in terms of forecast accuracy, may be different for each individual. For a specific individual, the estimator uses cross-validation to estimate the bias-variance of all individual groupings, and uses the parameter estimates of the optimal grouping to produce the individual-specific forecast. Integer programming and screening methods deal with the combinatorial problem of a large number of individuals. A simulation study and an application to market leverage forecasts of U.S. firms demonstrate the promising performance of our new estimators.