mtest.fct: Arellano and Bond Serial Correlation Test.

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

Arellano and Bond Serial Correlation Test.

Description

mtest.pdynmc Methods to test for serial correlation in the error terms for objects of class 'pdynmc'.

Usage

mtest.fct(object, order = 2, ...)

Arguments

object	An object of class 'pdynmc'.
order	A number denoting the order of serial correlation to test for (defaults to '2').
...	further arguments.

Details

The null hypothesis is that there is no serial correlation of a particular order. The test statistic is computed as proposed by \insertCiteAreBon1991;textualpdynmc and \insertCiteAre2003;textualpdynmc.

Value

An object of class 'htest' which contains the Arellano and Bond m test statistic and corresponding p-value for the null hypothesis that there is no serial correlation of the given order.

References

\insertAllCited

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")
mtest.fct(m1, order = 2)


## 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")
 mtest.fct(m1, order = 2)

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