Smooth varying-coefficient models in Stata

Fernando Rios-Avila ()

Stata Journal, 2020, vol. 20, issue 3, 647-679

Abstract: Nonparametric regressions are powerful statistical tools that can be used to model relationships between dependent and independent variables with minimal assumptions on the underlying functional forms. Despite their poten- tial benefits, these models have two weaknesses: The added flexibility creates a curse of dimensionality, and procedures available for model selection, like cross- validation, have a high computational cost in samples with even moderate sizes. An alternative to fully nonparametric models is semiparametric models that com- bine the flexibility of nonparametric regressions with the structure of standard models. In this article, I describe the estimation of a particular type of semipara- metric model known as the smooth varying-coefficient model (Hastie and Tibshi- rani, 1993, Journal of the Royal Statistical Society, Series B 55: 757–796), based on kernel regression methods, using a new set of commands within vc pack. These commands aim to facilitate bandwidth selection and model estimation as well as create visualizations of the results.

Keywords: vc pack; vc bw; vc bwalt; vc reg; vc bsreg; vc preg; vc predict; vc test; vc graph; smooth varying-coefficient models; kernel regression; cross-vali- dation; semiparametric estimations (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1177/1536867X20953574

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