plm (version 2.1-0)

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", "Pm", "invnormal", "logit", "hadri"), exo = c("none",
  "intercept", "trend"), lags = c("SIC", "AIC", "Hall"), pmax = 10,
  Hcons = TRUE, q = NULL, dfcor = FALSE, fixedT = TRUE, ...)

# S3 method for purtest print(x, ...)

# S3 method for purtest summary(object, ...)

# S3 method for 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 "purtest" object for the print and summary methods,

data

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

index

the indexes,

test

the test to be computed: one of "levinlin" for LEVIN:LIN:CHU:02;textualplm, "ips" for IM:PESAR:SHIN:03;textualplm, "madwu" for MADDA:WU:99;textualplm, "Pm" , "invnormal", or "logit" for various tests as in CHOI:01;textualplm, or "hadri" for HADR:00;textualplm, see Details,

exo

the exogenous variables to introduce in the augmented Dickey--Fuller (ADF) regressions, one of: no exogenous variables ("none"), individual intercepts ("intercept"), or individual intercepts and trends ("trend"), but see Details,

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 lags, based on either the AIC ("AIC"), the SIC ("SIC"), or on the method by HALL:94;textualplm ("Hall"),

pmax

maximum number of lags,

Hcons

logical, only relevant for test = "hadri", indicating whether the heteroskedasticity-consistent test of HADR:00;textualplm should be computed,

q

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

dfcor

logical, indicating whether the standard deviation of the regressions is to be computed using a degrees-of-freedom correction,

fixedT

logical, indicating whether the different ADF regressions are to 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 statistic).

Details

All these tests except "hadri" are based on the estimation of augmented Dickey-Fuller (ADF) regressions for each time series. A statistic is then computed using the t-statistics associated with the lagged variable. The Hadri residual-based LM statistic is the cross-sectional average of the individual KPSS statistics KWIA:PHIL:SCHM:SHIN:92plm, standardized by their asymptotic mean and standard deviation.

Several Fisher-type tests that combine p-values from tests based on ADF regressions per individual are available:

  • "madwu" is the inverse chi-squared test MADDA:WU:99plm, also called P test by CHOI:01;textualplm.

  • "Pm" is the modified P test proposed by CHOI:01;textualplm for large N,

  • "invnormal" is the inverse normal test by CHOI:01plm, and

  • "logit" is the logit test by CHOI:01plm.

The individual p-values for the Fisher-type tests are approximated as described in MACK:94;textualplm.

The kind of test to be computed can be specified in several ways, depending on how the data is handed over to the function:

  • For the 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 variables, with an intercept, or with individual intercepts and time trend, respectively. The exo argument is ignored in this case.

  • For the data.frame, matrix, and pseries interfaces: in these cases, the exogenous variables are specified using the exo argument.

With the associated summary and print methods, additional information can be extracted/displayed (see also Value).

References

See Also

cipstest()

Examples

Run this code
# NOT RUN {
data("Grunfeld", package = "plm")
y <- data.frame(split(Grunfeld$inv, Grunfeld$firm))

purtest(y, pmax = 4, exo = "intercept", test = "madwu")

## same via formula interface
purtest(inv ~ 1, data = Grunfeld, index = c("firm", "year"), pmax = 4, test = "madwu")

# }

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