tvPLM: Time-Varying Coefficients Panel Data Models
In tvReg: Time-Varying Coefficient for Single and Multi-Equation Regressions

tvPLM

R Documentation

Time-Varying Coefficients Panel Data Models

Description

Fits a balanced panel data model using the Time-Varying Pooled Ordinary Least Squares, the Time-Varying Random Effects and the Time-Varying Fixed Effects models.

Usage

tvPLM(
  formula,
  z = NULL,
  ez = NULL,
  data,
  index = NULL,
  bw = NULL,
  bw.cov = NULL,
  cv.block = 0,
  method = c("pooling", "random", "within"),
  est = c("lc", "ll"),
  tkernel = c("Triweight", "Epa", "Gaussian"),
  control = tvreg.control(...),
  ...
)

Arguments

`formula`	An object of class formula.
`z`	A vector containing the smoothing variable.
`ez`	(optional) A scalar or vector with the smoothing values. If values are not included then the vector `z` is used instead.
`data`	An optional data frame or matrix.
`index`	Indicates the individual and time indexes.
`bw`	An opcional scalar. It represents the bandwidth in the estimation of trend coefficients. If NULL, it is selected by cross validation.
`bw.cov`	An optional scalar. It represents the bandwidth in the "lc" nonparametric estimation of the time-varying covariance matrix. If NULL, it is selected by cross validation for method `"random"`.
`cv.block`	A positive scalar with the size of the block in leave one block out cross-validation. By default 'cv.block=0' meaning leave one out cross-validation.
`method`	A character with the choice of panel model/estimation method: If `method = "pooling"` (default) then the data is pooled estimated with time-varying OLS. No individual or time effects are estimated If `method = "random"` then individual effects are considered random and independent of the regressors. If `method = "within"` then individual effects which might be correlated with the regressors are estimated.
`est`	The nonparametric estimation method, one of "lc" (default) for linear constant
`tkernel`	A character, either "Triweight" (default), "Epa" or "Gaussian" kernel function.
`control`	list of control parameters. The default is constructed by the function `tvreg.control`. See the documentation of `tvreg.control` for details.
`...`	Other parameters passed to specific methods.

Details

This function wraps up the kernel smoothing time-varying coefficient pooled, random effects and fixed effects estimators.

Bandwidth selection is of great importance in kernel smoothing methodologies and it is done automatically by cross-validation.

A panel data model consists of "neq" elements in the cross-sectional dimention and "obs" number of time observations for each cross-section. All variables are the same for each equation which have common coefficients.

Value

tvPLM returns a list of the class tvplm containing the results of model, results of the estimation and confidence instervals if chosen. The object of class tvplm have the following components:

`coefficients`	An array of dimension obs x nvar x neq (obs = number of observations, nvar = number of variables in each equation, neq = number of equations in the system) with the time-varying coefficients estimates.
`Lower`	If `level` non equal zero, an array of dimension obs x nvar x neq containing the confidence interval lower band.
`Upper`	If `level` non equal zero, an array of dimension obs x nvar x neq containing the confidence interval upper band.
`fitted`	The fitted values.
`residuals`	Estimation residuals.
`x`	A list with the regressors data.
`y`	A matrix with the dependent variable data.
`z`	A vector with the smoothing variable.
`ez`	A vector with the smoothing estimation values.
`alpha`	A vector with the individual fixed effects, if chosen.
`bw`	Bandwidth of mean estimation.
`totobs`	Integer specifying the total number of observations.
`neq`	Integer specifying the number of cross-section observations.
`obs`	Integer specifying the number of time observations per cross-section.
`nvar`	Number of variables.
`method`	Estimation method.
`est`	Nonparemtric estimation methodology.
`tkernel`	Kernel type.
`level`	Confidence interval range.
`runs`	Number of bootstrap replications.
`tboot`	Type of bootstrap.
`BOOT`	List with all bootstrap replications of `coefficients`, if done.
`formula`	Initial formula.
`call`	Matched call.

References

Casas, I., Gao, J., Peng, B. and Xie, S. (2021). Time-Varying Income Elasticities of Healthcare Expenditure for the OECD and Eurozone. Journal of Applied Econometrics, 36, pp. 328-345.

Sun, Y., Carrol, R.J and Li, D. (2009). Semiparametric Estimation of Fixed-Effects Panel Data Varying Coefficient Models. Advances in Econometrics, 25, pp. 101-129.

Examples

data(OECD)
##TVPOLS estimation of the model
tvpols <- tvPLM(lhe~lgdp+pop65+pop14+public, index = c("country", "year"),
 data = OECD, method ="pooling", bw = 0.3)
## Not run: 
tvfe <- tvPLM(lhe~lgdp+pop65+pop14+public, index = c("country", "year"),
 data = OECD, method ="within", bw = 0.8)
tvre <- tvPLM(lhe~lgdp+pop65+pop14+public, index = c("country", "year"),
 data = OECD, method ="random", bw = 0.3)

## End(Not run)

tvReg documentation built on Sept. 1, 2023, 5:07 p.m.