Varlam Kutateladze
arXiv 1 Mar 2021 · Econometrics
arXiv:2103.01266 · PDF · Extracted main text
Factor modeling is a powerful statistical technique that permits to capture the common dynamics in a large panel of data with a few latent variables, or factors, thus alleviating the curse of dimensionality. Despite its popularity and widespread use for various applications ranging from genomics to finance, this methodology has predominantly remained linear. This study estimates factors nonlinearly through the kernel method, which allows flexible nonlinearities while still avoiding the curse of dimensionality. We focus on factor-augmented forecasting of a single time series in a high-dimensional setting, known as diffusion index forecasting in macroeconomics literature. Our main contribution is twofold. First, we show that the proposed estimator is consistent and it nests linear PCA estimator as well as some nonlinear estimators introduced in the literature as specific examples. Second, our empirical application to a classical macroeconomic dataset demonstrates that this approach can offer substantial advantages over mainstream methods.
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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 | Bai, J. and Ng, S (2008) Forecasting economic time series using targeted predictors | 1.000 | 7 | 4 | 100% |
| 2 | Bai, J. and Ng, S (2002) Determining the number of factors in approximate factor models | 0.843 | 4 | 3 | 75% |
| 3 | Stock, J. H. and Watson, M. W (2002) Forecasting using principal components from a large number of predictors | 0.811 | 4 | 2 | 100% |
| 4 | Exterkate, P., Groenen, P. J., Heij, C., and van Dijk, D (2016) Nonlinear forecasting with many predictors using kernel ridge regression | 0.644 | 2 | 2 | 100% |
| 5 | Forni, M., Reichlin, L., Hallin, M., and Lippi, M (2000) The generalized dynamic-factor model: Identification and estimation | 0.644 | 2 | 2 | 100% |
| 6 | Hofmann, T., Schölkopf, B., and Smola, A. J (2008) Kernel methods in machine learning | 0.644 | 2 | 2 | 100% |
| 7 | Coulombe, P. G., Stevanovic, D., and Surprenant, S (2019) How is machine learning useful for macroeconomic forecasting? | 0.644 | 2 | 2 | 100% |
| 8 | Blanchard, G., Bousquet, O., and Zwald, L (2006) Statistical properties of kernel principal component analysis | 0.585 | 3 | 1 | 100% |
| 9 | Koltchinskii, V. and Giné, E (2000) Random matrix approximation of spectra of integral operators | 0.511 | 2 | 2 | 50% |
| 10 | Bai, J (2003) Inferential theory for factor models of large dimensions | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 56 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Kernel Three Pass Regression Filter | 0.811 | 4 | 2 |
| 2 | LLM-Generated Counterfactual Stress Scenarios for Portfolio Risk Simulation via Hybrid Prompt-RAG Pipeline | 0.405 | 1 | 1 |