arXiv 9 Sep 2023 · Econometrics · publishedJournal of Economic Dynamics and Control (2023) · 2 citations (OpenAlex)
arXiv:2309.04821 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces non-linear dimension reduction in factor-augmented vector autoregressions to analyze the effects of different economic shocks. I argue that controlling for non-linearities between a large-dimensional dataset and the latent factors is particularly useful during turbulent times of the business cycle. In simulations, I show that non-linear dimension reduction techniques yield good forecasting performance, especially when data is highly volatile. In an empirical application, I identify a monetary policy as well as an uncertainty shock excluding and including observations of the COVID-19 pandemic. Those two applications suggest that the non-linear FAVAR approaches are capable of dealing with the large outliers caused by the COVID-19 pandemic and yield reliable results in both scenarios.
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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 | Bernanke et al (2005) Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach | 1.000 | 8 | 4 | 100% |
| 2 | Jurado et al (2015) Measuring uncertainty | 1.000 | 5 | 3 | 100% |
| 3 | McCracken and Ng (2020) FRED-QD: A quarterly database for macroeconomic research | 0.874 | 6 | 3 | 67% |
| 4 | Boivin et al (2009) Sticky prices and monetary policy: Evidence from disaggregated US data | 0.843 | 3 | 3 | 100% |
| 5 | Andreini et al (2020) Deep dynamic factor models | 0.811 | 4 | 2 | 100% |
| 6 | Dixon and Polson (2019) Deep Fundamental Factor Models | 0.811 | 4 | 2 | 100% |
| 7 | Roweis and Saul (2000) Nonlinear dimensionality reduction by locally linear embedding | 0.811 | 4 | 2 | 100% |
| 8 | Goodfellow et al (2016) Deep Learning\/ | 0.737 | 3 | 2 | 100% |
| 9 | Feng et al (2018) Deep Learning for Predicting Asset Returns | 0.737 | 3 | 2 | 100% |
| 10 | Bloom (2009) The impact of uncertainty shocks | 0.737 | 3 | 2 | 100% |
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