*! version 1.0.0 February 2020

Title

vc_pack -- Modules for the model selection, estimation, and visualization of Smooth Varying Coefficient Models (SVCM).

Description

Non-parametric 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 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 computational
cost in samples with even moderate sizes.

An alternative to fully-nonparametric models are semiparametric models that combine the flexibility
of non-parametric regressions with the structure of standard models.

This package estimates a particular type of semiparametric models known as Smooth Varying Voefficient Models
(Hastie and Tibshirani 1993), based on kernel regression methods, assuming a single smoothing variable.

These commands aim to facilitate bandwidth selection, model estimation, implementation of specification tests, and create visualizations of the results.

In this package you will find the following commands:

vc_bw and vc_bwalt are commands used for the bandwidth selection, using two maximization methods for the crossvalidation procedure.

vc_reg, vc_bsreg and vc_preg are commands used for the estimation of the SVCM, based on different strategies for the estimation of the Variance Covariance matrix of the coefficients.

vc_predict is a commands used for the the estimation of predicted values, predicted errors, and leave-one-out errors.
It also provides basic summary statistics for the SVCM, and performs the approximate F test for model specification.

vc_test is a commands used to implement the J-statistic specification test, using a wild bootstrap procedure.

vc_graph is a commands used to obtain plots of the estimated smooth coefficients after the model has been estimated using
vc_reg, vc_bsreg or vc_preg.

For details on the commands, please refer to Rios-Avila(2020).

Author

Fernando Rios-Avila

Levy Economics Institute of Bard College

Blithewood-Bard College

Annandale-on-Hudson, NY

friosavi@levy.org

References

Hastie, Trevor, and Robert Tibshirani. 1993. "Varying-Coefficient Models." Journal of the Royal Statistical Society. Series B (Methodological) 55 (4):757-796.

Rios-Avila, Fernando (2020) Smooth varying coefficient models in Stata. Working paper. vc_reg paper