A robust method for microforecasting and estimation of random effects

Arbeitspapier

We propose a method for forecasting individual outcomes and estimating random effects in linear panel data models and value-added models when the panel has a short time dimension. The method is robust, trivial to implement and requires minimal assumptions. The idea is to take a weighted average of time series- and pooled forecasts/estimators, with individual weights that are based on time series information. We show the forecast optimality of individual weights, both in terms of minimax-regret and of mean squared forecast error. We then provide feasible weights that ensure good performance under weaker assumptions than those required by existing approaches. Unlike existing shrinkage methods, our approach borrows the strength - but avoids the tyranny - of the majority, by targeting individual (instead of group) accuracy and letting the data decide how much strength each individual should borrow. Unlike existing empirical Bayesian methods, our frequentist approach requires no distributional assumptions, and, in fact, it is particularly advantageous in the presence of features such as heavy tails that would make a fully nonparametric procedure problematic.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. WP 2023-26

Classification and Topics

• Classification: Wirtschaft ; Econometric and Statistical Methods and Methodology: General ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models ; Forecasting Models; Simulation Methods
• Subject: Minimax-Regret ; Shrinkage ; Forecast Combination ; Robustness

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Giacomini, Raffaella ; Lee, Sokbae ; Sarpietro, Silvia
• Event: Veröffentlichung
• (who): Federal Reserve Bank of Chicago
• (where): Chicago, IL
• (when): 2023

Further information

• DOI: doi:10.21033/wp-2023-26
• Handle: http://hdl.handle.net/10419/284067
• Last update: 10.03.2025, 11:42 AM CET