arXiv 1 Sep 2020 · Econometrics
arXiv:2009.00401 · PDF · Extracted main text
Time-varying parameters (TVPs) models are frequently used in economics to capture structural change. I highlight a rather underutilized fact -- that these are actually ridge regressions. Instantly, this makes computations, tuning, and implementation much easier than in the state-space paradigm. Among other things, solving the equivalent dual ridge problem is computationally very fast even in high dimensions, and the crucial "amount of time variation" is tuned by cross-validation. Evolving volatility is dealt with using a two-step ridge regression. I consider extensions that incorporate sparsity (the algorithm selects which parameters vary and which do not) and reduced-rank restrictions (variation is tied to a factor model). To demonstrate the usefulness of the approach, I use it to study the evolution of monetary policy in Canada using large time-varying local projections. The application requires the estimation of about 4600 TVPs, a task well within the reach of the new method.
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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 | Cadonna, A., Frühwirth-Schnatter, S., and Knaus, P (2020) Triple the gamma—a unifying shrinkage prior for variance and variable selection in sparse state space and tvp models | 1.000 | 6 | 4 | 100% |
| 2 | Primiceri, G. E (2005) Time varying structural vector autoregressions and monetary policy | 1.000 | 5 | 3 | 100% |
| 3 | Goulet Coulombe, P (2024) The macroeconomy as a random forest | 0.928 | 4 | 3 | 100% |
| 4 | Champagne, J. and Sekkel, R (2018) Changes in monetary regimes and the identification of monetary policy shocks: Narrative evidence from canada | 0.874 | 11 | 2 | 100% |
| 5 | Stevanovic, D (2016) Common time variation of parameters in reduced-form macroeconomic models | 0.843 | 4 | 3 | 75% |
| 6 | Goulet Coulombe, P (2022) A neural phillips curve and a deep output gap | 0.843 | 3 | 3 | 100% |
| 7 | Amir-Ahmadi, P., Matthes, C., and Wang, M.-C (2018) Choosing prior hyperparameters: with applications to time-varying parameter models | 0.811 | 4 | 2 | 100% |
| 8 | Goulet Coulombe, P., Leroux, M., Stevanovic, D., and Surprenant, S (2022) How is machine learning useful for macroeconomic forecasting? | 0.737 | 3 | 2 | 100% |
| 9 | Bitto, A. and Frühwirth-Schnatter, S (2018) Achieving shrinkage in a time-varying parameter model framework | 0.737 | 3 | 2 | 100% |
| 10 | Newton, M. A., Polson, N. G., and Xu, J (2021) Weighted bayesian bootstrap for scalable posterior distributions | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 94 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Theory coherent shrinkage of Time-Varying Parameters in VARs | 0.405 | 1 | 1 |
| 2 | Overparametrized models with posterior drift | 0.405 | 1 | 1 |