purtest: Unit root tests for panel data

In plm: Linear Models for Panel Data

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,
  ips.stat = NULL,
  ...
)

## 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 (individuals as columns), a "pseries" object, a formula; a "purtest" object for the print and summary methods,
data	a "data.frame" or a "pdata.frame" object (required for formula interface, see Details and Examples),
index	the indexes,
test	the test to be computed: one of "levinlin" for \insertCiteLEVIN:LIN:CHU:02;textualplm, "ips" for \insertCiteIM:PESAR:SHIN:03;textualplm, "madwu" for \insertCiteMADDA:WU:99;textualplm, "Pm" , "invnormal", or "logit" for various tests as in \insertCiteCHOI:01;textualplm, or "hadri" for \insertCiteHADR: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 a single value 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 the AIC ("AIC"), the SIC ("SIC"), or on the method by \insertCiteHALL:94;textualplm ("Hall"); argument is irrelevant for test = "hadri",
pmax	maximum number of lags (irrelevant for test = "hadri"),
Hcons	logical, only relevant for test = "hadri", indicating whether the heteroskedasticity-consistent test of \insertCiteHADR:00;textualplm should be computed,
q	the bandwidth for the estimation of the long-run variance (only relevant for test = "levinlin", the default (q = NULL) gives the value as suggested by the authors as round(3.21 * T^(1/3))),
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 individual ADF regressions are to be computed using the same number of observations (irrelevant for test = "hadri"),
ips.stat	NULL or character of length 1 to request a specific IPS statistic, one of "Wtbar" (also default if ips.stat = NULL), "Ztbar", "tbar",
...	further arguments (can set argument p.approx to be passed on to non-exported function padf to either "MacKinnon1994" or "MacKinnon1996" to force a specific method for p-value approximation, the latter only being possible if package 'urca' is installed).

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 \insertCiteKWIA:PHIL:SCHM:SHIN:92;textualplm, 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 \insertCiteMADDA:WU:99;textualplm, also called P test by \insertCiteCHOI:01;textualplm.

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

• "invnormal" is the inverse normal test by \insertCiteCHOI:01;textualplm, and

• "logit" is the logit test by \insertCiteCHOI:01;textualplm.

The individual p-values for the Fisher-type tests are approximated as described in \insertCiteMACK:96;textualplm if the package urca (\insertCitePFAFF:08;textualplm) is available, otherwise as described in \insertCiteMACK:94;textualplm.

For the test statistic tbar of the test of Im/Pesaran/Shin (2003) (ips.stat = "tbar"), no p-value is given but 1%, 5%, and 10% critical values are interpolated from paper's tabulated values via inverse distance weighting (printed and contained in the returned value's element statistic$ips.tbar.crit).

Hadri's test, the test of Levin/Lin/Chu, and the tbar statistic of Im/Pesaran/Shin are not applicable to unbalanced panels; the tbar statistic is not applicable when lags > 0 is given.

The exogenous instruments of the tests (where applicable) 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).

Value

For purtest: An object of class "purtest": a list with the elements named:

• "statistic" (a "htest" object),

• "call",

• "args",

• "idres" (containing results from the individual regressions),

• "adjval" (containing the simulated means and variances needed to compute the statistic, for test = "levinlin" and "ips", otherwise NULL),

• "sigma2" (short-run and long-run variance for test = "levinlin", otherwise NULL).

Author(s)

Yves Croissant and for "Pm", "invnormal", and "logit" Kevin Tappe

References

\insertAllCited

See Also

cipstest(), phansitest()

Examples

data("Grunfeld", package = "plm")
y <- data.frame(split(Grunfeld$inv, Grunfeld$firm)) # individuals in columns

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

## same via pseries interface
pGrunfeld <- pdata.frame(Grunfeld, index = c("firm", "year"))
purtest(pGrunfeld$inv, pmax = 4, exo = "intercept", test = "madwu")

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

plm documentation built on April 9, 2023, 5:06 p.m.