Smooth varying coefficient models in Stata

Nonparametric regressions are a powerful statistical tool to model relationships between dependent and independent variables with minimal assumptions on the underlying functional forms. Despite its potential benefits, these types of models have two weaknesses: The added flexibility creates a curse of dimensionality, and procedures available for model selection, like cross-validation, have a high computationally cost in samples with even moderate sizes. An alternative to fully nonparametric models are semiparametric models that combine the flexibility of nonparametric regressions with the structure of standard models. This presentation describes the estimation of a particular type of semiparametric modes known as smooth varying-coefficient models (Hastie and Tibshirani 1993), based on kernel regression methods, using a new set of commands in vc_pack. These commands aim to facilitate bandwidth selection and model estimation and create visualizations of the results.