arXiv 20 Jun 2024 · Econometrics
arXiv:2406.14046 · PDF · DOI · OpenAlex · Extracted main text
We consider estimating nonparametric time-varying parameters in linear models using kernel regression. Our contributions are threefold. First, we consider a broad class of time-varying parameters including deterministic smooth functions, the rescaled random walk, structural breaks, the threshold model and their mixtures. We show that those time-varying parameters can be consistently estimated by kernel regression. Our analysis exploits the smoothness of the time-varying parameter quantified by a single parameter. The second contribution is to reveal that the bandwidth used in kernel regression determines a trade-off between the rate of convergence and the size of the class of time-varying parameters that can be estimated. We demonstrate that an improper choice of the bandwidth yields biased estimation, and argue that the bandwidth should be selected according to the smoothness of the time-varying parameter. Our third contribution is to propose a data-driven procedure for bandwidth selection that is adaptive to the smoothness of the time-varying parameter.
appendix boundary found by appendix_titled_section at “Appendix to ``Estimating Time-Varying Parameters of Various Smoothness in Linear Models via Kernel Regression" by M. Nishi” · 51% of the source is main text. Read the extracted text to check this.
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 | |
|---|---|---|---|---|---|
| caiTrendingTimevaryingCoefficient2007 | unmatched citation key caiTrendingTimevaryingCoefficient2007 | 1.000 | 11 | 4 | 100% |
| hansenSampleSplittingThreshold2000 | unmatched citation key hansenSampleSplittingThreshold2000 | 1.000 | 9 | 4 | 100% |
| zhangInferenceTimeVaryingRegression2012 | unmatched citation key zhangInferenceTimeVaryingRegression2012 | 1.000 | 7 | 3 | 100% |
| chenTestingSmoothStructural2012 | unmatched citation key chenTestingSmoothStructural2012 | 1.000 | 6 | 3 | 100% |
| 5 | Giraitis, Kapetanios and Marcellino (2021) Time-Varying Instrumental Variable Estimation | 0.920 | 9 | 5 | 78% |
| zhouSimultaneousInferenceLinear2010 | unmatched citation key zhouSimultaneousInferenceLinear2010 | 0.874 | 5 | 2 | 100% |
| 7 | Bai and Perron (1998) Estimating and Testing Linear Models with Multiple Structural Changes | 0.843 | 4 | 4 | 75% |
| kristensenNonparametricDetectionEstimation2012 | unmatched citation key kristensenNonparametricDetectionEstimation2012 | 0.843 | 3 | 3 | 100% |
| friedrichSieveBootstrapInference2024 | unmatched citation key friedrichSieveBootstrapInference2024 | 0.737 | 3 | 2 | 100% |
| giraitisInferenceStochasticTimevarying2014 | unmatched citation key giraitisInferenceStochasticTimevarying2014 | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 43 scored citations. 8 of these could not be matched to a bibliography entry, so only the citation key is shown.