wald.fct: Wald Test.

In pdynmc: Moment Condition Based Estimation of Linear Dynamic Panel Data Models

Wald Test.

Description

wald.fct computes F test statistics and corresponding p-values for 'pdynmc' objects.

Usage

wald.fct(object, param)

Arguments

object	An object of class 'pdynmc'.
param	A character string that denotes the null hypothesis. Choices are time.dum (i.e., all time dummies are jointly zero), slope (i.e., all slope coefficients are jointly zero), and all (i.e., all dummies and slope coefficients are jointly zero).

Details

The three available null hypothesis are: All time dummies are jointly zero, all slope coefficients are jointly zero, all times dummies and slope coefficients are jointly zero.

Value

An object of class 'htest' which contains the F test statistic and corresponding p-value for the tested null hypothesis.

See Also

pdynmc for fitting a linear dynamic panel data model.

Examples

## Load data
data(ABdata, package = "pdynmc")
dat <- ABdata
dat[,c(4:7)] <- log(dat[,c(4:7)])
dat <- dat[c(140:0), ]

## Code example
m1 <- pdynmc(dat = dat, varname.i = "firm", varname.t = "year",
    use.mc.diff = TRUE, use.mc.lev = FALSE, use.mc.nonlin = FALSE,
    include.y = TRUE, varname.y = "emp", lagTerms.y = 2,
    fur.con = TRUE, fur.con.diff = TRUE, fur.con.lev = FALSE,
    varname.reg.fur = c("wage", "capital", "output"), lagTerms.reg.fur = c(1,2,2),
    include.dum = TRUE, dum.diff = TRUE, dum.lev = FALSE, varname.dum = "year",
    w.mat = "iid.err", std.err = "corrected", estimation = "onestep",
    opt.meth = "none")
wald.fct(param = "all", m1)


## Load data
 data(ABdata, package = "pdynmc")
 dat <- ABdata
 dat[,c(4:7)] <- log(dat[,c(4:7)])

## Further code example
 m1 <- pdynmc(dat = dat, varname.i = "firm", varname.t = "year",
    use.mc.diff = TRUE, use.mc.lev = FALSE, use.mc.nonlin = FALSE,
    include.y = TRUE, varname.y = "emp", lagTerms.y = 2,
    fur.con = TRUE, fur.con.diff = TRUE, fur.con.lev = FALSE,
    varname.reg.fur = c("wage", "capital", "output"), lagTerms.reg.fur = c(1,2,2),
    include.dum = TRUE, dum.diff = TRUE, dum.lev = FALSE, varname.dum = "year",
    w.mat = "iid.err", std.err = "corrected", estimation = "onestep",
    opt.meth = "none")
 wald.fct(m1, param = "all")

pdynmc documentation built on Nov. 25, 2023, 1:08 a.m.