Tae-Hwy Lee, Ekaterina Seregina
arXiv 4 Sep 2022 · Econometrics · publishedInternational Journal of Forecasting (2025)
arXiv:2209.01697 · PDF · DOI · OpenAlex · Extracted main text
In this paper we develop a novel method of combining many forecasts based on a machine learning algorithm called Graphical LASSO (GL). 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 use the Factor Graphical LASSO (FGL, Lee and Seregina (2023)) to separate common forecast errors from the idiosyncratic errors and exploit 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-FGL) that allows factor loadings and idiosyncratic precision matrix to be regime-dependent. We 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 FGL and RD-FGL.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lee, T.-H. and Seregina, E (2023) Optimal Portfolio Using Factor Graphical Lasso self | 1.000 | 7 | 3 | 100% |
| 2 | Pesaran, M. H., Pettenuzzo, D., and Timmermann, A (2006) Forecasting time series subject to multiple structural breaks | 0.811 | 4 | 2 | 100% |
| 3 | Friedman, J., Hastie, T., and Tibshirani, R (2008) Sparse inverse covariance estimation with the Graphical Lasso | 0.737 | 4 | 3 | 50% |
| 4 | Ledoit, O. and Wolf, M (2004) A well-conditioned estimator for large-dimensional covariance matrices | 0.737 | 3 | 3 | 67% |
| 5 | Diebold, F. and Shin, M (2019) Machine learning for regularized survey forecast combination: Partially-egalitarian lasso and its derivatives | 0.737 | 3 | 2 | 100% |
| 6 | Stock, J. H. and Watson, M. W (2002) Forecasting using principal components from a large number of predictors | 0.737 | 3 | 2 | 100% |
| 7 | Su, L. and Wang, X (2017) On time-varying factor models: Estimation and testing | 0.693 | 5 | 1 | 100% |
| 8 | Bai, J. and Ng, S (2002) Determining the number of factors in approximate factor models | 0.644 | 2 | 2 | 100% |
| 9 | Bates, J. M. and Granger, C. W. J (1969) The combination of forecasts | 0.644 | 2 | 2 | 100% |
| 10 | Bai, J. and Perron, P (2003) Computation and analysis of multiple structural change models | 0.644 | 2 | 2 | 100% |
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