Learning from Forecast Errors: A New Approach to Forecast Combinations

Abstract

Forecasters often use common information and hence make common mistakes. We propose a new approach, Factor Graphical Model (FGM), to forecast combinations that separates idiosyncratic forecast errors from the common errors. FGM exploits the factor structure of forecast errors and the sparsity of the precision matrix of the idiosyncratic errors. We prove the consistency of forecast combination weights and mean squared forecast error estimated using FGM, supporting the results with extensive simulations. Empirical applications to forecasting macroeconomic series shows that forecast combination using FGM outperforms combined forecasts using equal weights and graphical models without incorporating factor structure of forecast errors.

arXiv comment41 pages, 14 figures, 1 table

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• Extended Bayesian Information Criteria for Gaussian Graphical Models
• The huge Package for High-dimensional Undirected Graph Estimation in R
• Nonlinear shrinkage estimation of large-dimensional covariance matrices
• High-dimensional covariance matrix estimation in approximate factor models
• Sparse Principal Components Analysis
• De-biased graphical Lasso for high-frequency data