R: Wooldridge first-difference-based test for AR(1) errors in...

pwfdtest {plm} | R Documentation

## Wooldridge first–difference–based test for AR(1) errors in levels or first–differenced panel models

### Description

First–differencing–based test of serial correlation for (the idiosyncratic component of) the errors in either levels or first–differenced panel models.

### Usage

    pwfdtest(x, ...)
    ## S3 method for class 'panelmodel'
    pwfdtest(x, ..., h0 = c("fd", "fe"))
    ## S3 method for class 'formula'
    pwfdtest(x, data, ..., h0 = c("fd", "fe"))

### Arguments

`x` | an object of class `formula` or a `"fd"`-model (plm object),
`data` | a `data.frame`,
`h0` | the null hypothesis: one of `"fd"`, `"fe"`,
`...` | further arguments to be passed on to `linearHypothesis` or to `vcovHC`.

### Details

As Wooldridge (2003/2010, Sec. 10.6.3) observes, if the idiosyncratic errors in the model in levels are uncorrelated (which we label hypothesis `"fe"`), then the errors of the model in first differences (FD) must be serially correlated with cor(\hat{e}_{it}, \hat{e}_{is}) = -0.5 for each t,s. If on the contrary the levels model's errors are a random walk, then there must be no serial correlation in the FD errors (hypothesis `"fd"`). Both the fixed effects (FE) and the first–differenced (FD) estimators remain consistent under either assumption, but the relative efficiency changes: FE is more efficient under `"fe"`, FD under `"fd"`.

Wooldridge (ibid.) suggests basing a test for either hypothesis on a pooled regression of FD residuals on their first lag: \hat{e}_{i,t}=α + ρ \hat{e}_{i,t-1} + η_{i,t}. Rejecting the restriction ρ = -0.5 makes us conclude against the null of no serial correlation in errors of the levels equation (`"fe"`). The null hypothesis of no serial correlation in differenced errors (`"fd"`) is tested in a similar way, but based on the zero restriction on ρ (ρ = 0). Rejecting `"fe"` favours the use of the first–differences estimator and the contrary, although it is possible that both be rejected.

`pwfdtest` estimates the `fd` model (or takes an `fd` model as input for the panelmodel interface) and retrieves its residuals, then estimates an AR(1) `pooling` model on them. The test statistic is obtained by applying `linearHypothesis()` from package `car` to the latter model to test the relevant restriction on ρ, setting the covariance matrix to `vcovHC` with the option `method="arellano"` to control for serial correlation.

Unlike the `pbgtest` and `pdwtest`, this test does not rely on large–T asymptotics and has therefore good properties in ”short” panels. Furthermore, it is robust to general heteroskedasticity. The `"fe"` version can be used to test for error autocorrelation regardless of whether the maintained specification has fixed or random effects (see Drukker (2003)).

### Value

An object of class `"htest"`.

### Author(s)

Giovanni Millo

### References

Drukker, D.M. (2003) Testing for serial correlation in linear panel–data models, The Stata Journal, 3(2), pp. 168–177.

Wooldridge, J.M. (2003) Econometric analysis of cross–section and panel data, MIT Press, Sec. 10.6.3, pp. 282–283.

Wooldridge, J.M. (2010) Econometric analysis of cross–section and panel data, 2nd ed., MIT Press, Sec. 10.6.3, pp. 319–320.

### See Also

`pdwtest`, `pbgtest`, `pwartest`,

### Examples

    data(EmplUK)
    pwfdtest(log(emp) ~ log(wage) + log(capital), data = EmplUK)
    pwfdtest(log(emp) ~ log(wage) + log(capital), data = EmplUK, h0 = "fe")

    # pass argument 'type' to vcovHC used in test
    pwfdtest(log(emp) ~ log(wage) + log(capital), data = EmplUK, type = "HC3", h0 = "fe")

    # same with panelmodel interface
    mod <- plm(log(emp) ~ log(wage) + log(capital), data = EmplUK, model = "fd")
    pwfdtest(mod)
    pwfdtest(mod, h0 = "fe")
    pwfdtest(mod, type = "HC3", h0 = "fe")

[Package plm version 1.6-5 Index]