arXiv 12 May 2024 · Econometrics
arXiv:2405.07292 · PDF · DOI · OpenAlex · Extracted main text
We forecast a single time series using a high-dimensional set of predictors. When these predictors share common underlying dynamics, an approximate latent factor model provides a powerful characterization of their co-movements Bai(2003). These latent factors succinctly summarize the data and can also be used for prediction, alleviating the curse of dimensionality in high-dimensional prediction exercises, see Stock & Watson (2002a). However, forecasting using these latent factors suffers from two potential drawbacks. First, not all pervasive factors among the set of predictors may be relevant, and using all of them can lead to inefficient forecasts. The second shortcoming is the assumption of linear dependence of predictors on the underlying factors. The first issue can be addressed by using some form of supervision, which leads to the omission of irrelevant information. One example is the three-pass regression filter proposed by Kelly & Pruitt (2015). We extend their framework to cases where the form of dependence might be nonlinear by developing a new estimator, which we refer to as the Kernel Three-Pass Regression Filter (K3PRF). This alleviates the aforementioned second shortcoming. The estimator is computationally efficient and performs well empirically. The short-term performance matches or exceeds that of established models, while the long-term performance shows significant improvement.
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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 | Kelly, Bryan and Pruitt, Seth (2015) The three-pass regression filter: A new approach to forecasting using many predictors | 0.916 | 39 | 6 | 77% |
| 2 | Kutateladze, Varlam (2022) The kernel trick for nonlinear factor modeling | 0.811 | 4 | 2 | 100% |
| 3 | Bai, Jushan and Ng, Serena (2008) Forecasting economic time series using targeted predictors | 0.811 | 4 | 2 | 100% |
| 4 | Bai, Jushan (2003) Inferential theory for factor models of large dimensions | 0.644 | 2 | 2 | 100% |
| 5 | Stock, James H and Watson, Mark W (2002) Macroeconomic forecasting using diffusion indexes | 0.644 | 2 | 2 | 100% |
| 6 | Ahn, Seung C and Horenstein, Alex R (2013) Eigenvalue ratio test for the number of factors | 0.511 | 2 | 1 | 100% |
| 7 | Fan, Jianqing and Xue, Lingzhou and Yao, Jiawei (2017) Sufficient forecasting using factor models | 0.511 | 2 | 1 | 100% |
| 8 | Goulet Coulombe, Philippe and Leroux, Maxime and Stevanovic, Dalibor… (2022) How is machine learning useful for macroeconomic forecasting? | 0.511 | 2 | 1 | 100% |
| 9 | Hinton, Geoffrey E and Salakhutdinov, Ruslan R (2006) Reducing the dimensionality of data with neural networks | 0.405 | 1 | 1 | 100% |
| 10 | Onatski, Alexei and Wang, Chen (2021) Spurious factor analysis | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 19 scored citations.