Tae-Hwy Lee, Ekaterina Seregina
arXiv 4 Nov 2020 · Econometrics
arXiv:2011.02077 · PDF · DOI · OpenAlex · Extracted main text
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.
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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 | Callot, L., Caner, M., Önder, A. O., and Ulaşan, E (2019) A nodewise regression approach to estimating large portfolios | 1.000 | 5 | 3 | 100% |
| 2 | Meinshausen, N. and Bühlmann, P (2006) High-dimensional graphs and variable selection with the Lasso | 0.928 | 4 | 3 | 100% |
| 3 | Friedman, J., Hastie, T., and Tibshirani, R (2008) Sparse inverse covariance estimation with the Graphical Lasso | 0.874 | 5 | 2 | 100% |
| 4 | Diebold, F. and Shin, M (2019) Machine learning for regularized survey forecast combination: Partially-egalitarian lasso and its derivatives | 0.737 | 3 | 2 | 100% |
| 5 | Janková, J. and van de Geer, S (2018) Inference in high-dimensional graphical models | 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 | Koike, Y (2020) De-biased graphical lasso for high-frequency data | 0.737 | 3 | 2 | 100% |
| 8 | Bai, J. and Ng, S (2002) Determining the number of factors in approximate factor models | 0.644 | 2 | 2 | 100% |
| 9 | Barigozzi, M., Brownlees, C., and Lugosi, G (2018) Power-law partial correlation network models | 0.644 | 2 | 2 | 100% |
| 10 | Brownlees, C., Nualart, E., and Sun, Y (2018) Realized networks | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 34 scored citations.