This package covers a large range of semiparametric regression methods with time-varying coefficients using nonparametric kernel smoothing for the estimation.
The five basic functions in this package are tvLM, tvAR,
tvSURE, tvPLM, tvVAR and tvIRF.
Moreover, this package provides the confint, fitted,
forecast, plot, predict, print,
resid and summary methods adapted to the class attributes
of the tvReg.
In addition, it includes bandwidth selection methods, time-varying variance-covariance
estimators and four estimation procedures: the time-varying ordinary least squares,
which are implemented in the tvOLS methods, the time-varying
generalised least squares for a list of equations, which is implemented in the
tvGLS methods, time-varying pooled and random effects estimators for
panel data, which are implemented in the tvRE and the time-varying
fixed effects estimator, which is implemente in the tvFE.
Details on the theory and applications to finance and macroeconomics can be found in Casas and Fernandez-Casal (2019, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3363526), and in the package vignette https://icasas.github.io/tvReg/articles/tvReg.html.
Funded by the Horizon 2020. Framework Programme of the European Union.
Casas, I. and Fernandez-Casal, R., tvReg: Time-varying Coefficient Linear Regression for Single and Multi-Equations in R (April 1, 2019). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3363526.