arXiv 11 May 2020 · Econometrics
arXiv:2005.04897 · PDF · DOI · OpenAlex · Extracted main text
We combine high-dimensional factor models with fractional integration methods and derive models where nonstationary, potentially cointegrated data of different persistence is modelled as a function of common fractionally integrated factors. A two-stage estimator, that combines principal components and the Kalman filter, is proposed. The forecast performance is studied for a high-dimensional US macroeconomic data set, where we find that benefits from the fractional factor models can be substantial, as they outperform univariate autoregressions, principal components, and the factor-augmented error-correction model.
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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 | Stock \ Watson (2002) Macroeconomic forecasting using diffusion indexes, Journal of Business & Economic Statistics 20(2): 147–162 | 1.000 | 5 | 3 | 100% |
| 2 | Bai \ Ng (2002) Determining the number of factors in approximate factor models, Econometrica 70(1): 191–221 | 0.928 | 10 | 4 | 80% |
| 3 | Hartl \ Weigand (2019) Approximate state space modelling of unobserved fractional components, arXiv:1812.09142, arXiv.org | 0.899 | 11 | 3 | 73% |
| 4 | Barigozzi, Lippi \ Luciani (2016) Non-stationary dynamic factor models for large datasets, Working paper, Board of Governors of the Federal Reserve System | 0.874 | 5 | 2 | 100% |
| 5 | McCracken \ Ng (2016) FRED-MD: A monthly database for macroeconomic research, Journal of Business & Economic Statistics 34(4): 574–589 | 0.843 | 4 | 3 | 75% |
| 6 | Jungbacker \ Koopman (2015) Likelihood-based dynamic factor analysis for measurement and forecasting, Econometrics Journal 18: C1–C21 | 0.737 | 5 | 2 | 60% |
| 7 | Hartl \ Weigand (2019) Multivariate fractional components analysis, arXiv:1812.09149, arXiv.org | 0.737 | 3 | 2 | 100% |
| 8 | Bai (2004) Estimating cross-section common stochastic trends in nonstationary panel data, Journal of Econometrics 122(1): 137–183 | 0.644 | 4 | 2 | 50% |
| 9 | Banerjee, Marcellino \ Masten (2014) Forecasting with factor-augmented error correction models, International Journal of Forecasting 30(3): 589–612 | 0.644 | 2 | 2 | 100% |
| 10 | Forni, Hallin, Lippi \ Reichlin (2000) The generalized dynamic-factor model: Identification and estimation, The Review of Economics and Statistics 82(4): 540–554 | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 34 scored citations.