Semi-parametric test for the presence of (individual or time) unobserved effects in panel models.

`pwtest(x, ...)`# S3 method for formula
pwtest(x, data, effect = c("individual", "time"), ...)

# S3 method for panelmodel
pwtest(x, effect = c("individual", "time"), ...)

x

an object of class `"formula"`

, or an estimated model of class
`panelmodel`

,

…

further arguments passed to `plm`

.

data

a `data.frame`

,

effect

the effect to be tested for, one of `"individual"`

(default) or `"time"`

,

An object of class `"htest"`

.

This semi-parametric test checks the null hypothesis of zero correlation between errors of the same group. Therefore, it has power both against individual effects and, more generally, any kind of serial correlation.

The test relies on large-N asymptotics. It is valid under error heteroskedasticity and departures from normality.

The above is valid if `effect="individual"`

, which is the most
likely usage. If `effect="time"`

, symmetrically, the test relies on
large-T asymptotics and has power against time effects and, more
generally, against cross-sectional correlation.

If the panelmodel interface is used, the inputted model must be a pooling model.

WOOL:02plm

WOOL:10plm

`pbltest()`

, `pbgtest()`

,
`pdwtest()`

, `pbsytest()`

, `pwartest()`

,
`pwfdtest()`

for tests for serial correlation in panel models.
`plmtest()`

for tests for random effects.

# NOT RUN { data("Produc", package = "plm") ## formula interface pwtest(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc) pwtest(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, effect = "time") ## panelmodel interface # first, estimate a pooling model, than compute test statistics form <- formula(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp) pool_prodc <- plm(form, data = Produc, model = "pooling") pwtest(pool_prodc) # == effect="individual" pwtest(pool_prodc, effect="time") # }