Combining Forecasts under Structural Breaks Using Graphical LASSO

Tae Hwy Lee and Ekaterina Seregina ()
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Ekaterina Seregina: Colby College

No 202213, Working Papers from University of California at Riverside, Department of Economics

Abstract: In this paper we develop a novel method of combining many forecasts based on a machine learning algorithm called Graphical LASSO. We visualize forecast errors from different forecasters as a network of interacting entities and generalize network inference in the presence of common factor structure and structural breaks. First, we note that forecasters often use common information and hence make common mistakes, which makes the forecast errors exhibit common factor structures. We propose the Factor Graphical LASSO (Factor GLASSO), which separates common forecast errors from the idiosyncratic errors and exploits sparsity of the precision matrix of the latter. Second, since the network of experts changes over time as a response to unstable environments such as recessions, it is unreasonable to assume constant forecast combination weights. Hence, we propose Regime-Dependent Factor Graphical LASSO (RD-Factor GLASSO) and develop its scalable implementation using the Alternating Direction Method of Multipliers (ADMM) to estimate regime-dependent forecast combination weights. The empirical application to forecasting macroeconomic series using the data of the European Central Bank’s Survey of Professional Forecasters (ECB SPF) demonstrates superior performance of a combined forecast using Factor GLASSO and RD-Factor GLASSO.

Keywords: Common Forecast Errors; Regime Dependent Forecast Combination; Sparse Precision Matrix of Idiosyncratic Errors; Structural Breaks. (search for similar items in EconPapers)
JEL-codes: C13 C38 C55 (search for similar items in EconPapers)
Pages: 43 Pages
Date: 2022-09
New Economics Papers: this item is included in nep-big, nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (1)

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https://economics.ucr.edu/repec/ucr/wpaper/202213.pdf First version, 2022 (application/pdf)

Related works:
Working Paper: Combining Forecasts under Structural Breaks Using Graphical LASSO (2023)
Working Paper: Combining Forecasts under Structural Breaks Using Graphical LASSO (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:202213

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