João B. Assunção, Pedro Afonso Fernandes
arXiv 9 Sep 2024 · Econometrics
arXiv:2409.05713 · PDF · DOI · OpenAlex · Extracted main text
Partial least squares (PLS) is a simple factorisation method that works well with high dimensional problems in which the number of observations is limited given the number of independent variables. In this article, we show that PLS can perform better than ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO) and ridge regression in forecasting quarterly gross domestic product (GDP) growth, covering the period from 2000 to 2023. In fact, through dimension reduction, PLS proved to be effective in lowering the out-of-sample forecasting error, specially since 2020. For the period 2000-2019, the four methods produce similar results, suggesting that PLS is a valid regularisation technique like LASSO or ridge.
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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 | H. R. Varian (2014) Big Data: New Tricks for Econometrics | 0.928 | 4 | 3 | 100% |
| 2 | M. Taddy (2019) Business Data Science: Combining Machine Learning and Economics to Optimize, Automate and Accelerate Business Decisions | 0.874 | 5 | 2 | 100% |
| 3 | J.-F. Dauphin, K. Dybczak, M. Maneely, M. T. Sanjani, N. Suphaphipha… (2022) Nowcasting GDP: A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies | 0.811 | 4 | 2 | 100% |
| 4 | F. Petropoulos, D. Apiletti, V. Assimakopoulos, M. Z. Babai, D. K. B… (2021) Forecasting: theory and practice | 0.644 | 2 | 2 | 100% |
| 5 | A. Belloni, V. Chernozhukov, and C. Hansen (2014) High-Dimensional Methods and Inference on Structural and Treatment Effects | 0.585 | 3 | 1 | 100% |
| 6 | H. Abdi (2003) Partial Least Squares (PLS) Regression | 0.405 | 1 | 1 | 100% |
| 7 | J. B. Assuncão and P. A. Fernandes (2022) Nowcasting GDP: An Application to Portugal self | 0.405 | 1 | 1 | 100% |
| 8 | C. Bergmeir, R. J. Hyndman, and B. Koo (2017) A note on the validity of cross-validation for evaluating autoregressive time series prediction | 0.405 | 1 | 1 | 100% |
| 9 | B. Bok, D. Caratelli, D. Giannone, A. M. Sbordone, and A. Tambalotti (2018) Macroeconomic Nowcasting and Forecasting with Big Data | 0.405 | 1 | 1 | 100% |
| 10 | J. H. Friedman, T. Hastie, and R. Tibshirani (2000) Additive Logistic Regression; A Statistical View of Boosting | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 22 scored citations.