plm (version 1.2-3)

purtest: Unit root tests for panel data

Description

purtest implements several testing procedures that have been proposed to test unit root hypotheses with panel data.

Usage

purtest(object, data = NULL, index = NULL,
        test= c("levinlin", "ips", "madwu", "hadri"),
        exo = c("none", "intercept", "trend"),
        lags = c("SIC", "AIC", "Hall"), pmax = 10, Hcons = TRUE,
        q = NULL, dfcor = FALSE, fixedT = TRUE, ...)
## S3 method for class 'purtest':
print(x, ...)
## S3 method for class 'purtest':
summary(object, ...)
## S3 method for class 'summary.purtest':
print(x, ...)

Arguments

object, x

Either a 'data.frame' or a matrix containing the time series, a 'pseries' object, a formula, or the name of a column of a 'data.frame', or a 'pdata.frame' on which the test has to be computed; a'pu

data

a 'data.frame' or a 'pdata.frame' object,

index

the indexes,

test

the test to be computed: one of levinlin for Levin, Lin and Chu (2002), ips for Im, Pesaran and Shin (2003), madwu for Maddala and Wu (1999), and hadri for Hadri (2000),

exo

the exogenous variables to introduce in the augmented Dickey-Fuller regressions: this can be nothing ('none'), individual intercepts ('intercept') or individual intercepts and trends ('trend'),

lags

the number of lags to be used for the augmented Dickey-Fuller regressions: either an integer (the number of lags for all time series), a vector of integers (one for each time series), or a character string for an automatic computation of the number of lag

pmax

maximum number of lags,

Hcons

a boolean indicating whether the heteroscedasticity-consistent test of Hadri should be computed,

q

the bandwidth for the estimation of the long-run variance,

dfcor

should the standard deviation of the regressions be computed using a degrees-of-freedom correction?

fixedT

should the different ADF regressions be computed using the same number of observations?,

...

further arguments.

Value

• An object of class 'purtest': a list with the elements 'statistic' (a 'htest' object), 'call', 'args', 'idres' (containing results from the individual regressions), and 'adjval' (containing the simulated means and variances needed to compute the statistics).

Details

All these tests except 'hadri' are based on the estimation of augmented Dickey-Fuller regressions for each time series. A statistic is then computed using the t-statistic associated with the lagged variable. The kind of test to be computed can be specified in several ways:

A formula/data interface (if data is a data.frame, an additional index argument should be specified); the formula should be of the form: y~0, y~1 or y~trend for a test with no exogenous variable, with an intercept or with a time trend, respectively.

A data.frame, a matrix, a pseries: in this case, the exogenous variables are specified using the exo argument.

References

Hadri K. (2000). ``Testing for Unit Roots in Heterogeneous Panel Data'', The Econometrics Journal, 3, pp. 148--161.

Im K.S., Pesaran M.H. and Shin Y. (2003). ``Testing for Unit Roots in Heterogeneous Panels'', Journal of Econometrics, 115(1), pp. 53--74.

Levin A., Lin C.F. and Chu C.S.J. (2002). ``Unit Root Test in Panel Data: Asymptotic and Finite Sample Properties'', Journal of Econometrics, 108, pp. 1--24.

Maddala G.S. and Wu S. (1999). ``A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test'', Oxford Bulletin of Economics and Statistics, 61, Supplement 1, pp. 631--652.

Examples

data("Grunfeld", package = "plm")
y <- data.frame(split(Grunfeld$inv, Grunfeld$firm))
purtest(y, pmax = 4, type = "intercept", test = "madwu")