Resumen de Smooth varying-coefficient models in Stata
Nonparametric regressions are powerful statistical tools that can beused to model relationships between dependent and independent variables withminimal assumptions on the underlying functional forms. Despite their poten-tial benefits, these models have two weaknesses: The added flexibility creates acurse 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 standardmodels. 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 B55: 757–796), basedon kernel regression methods, using a new set of commands withinvcpack. Thesecommands aim to facilitate bandwidth selection and model estimation as well ascreate visualizations of the results.
