Name

Maddala_Wu — Maddala and Wu Panel Unit Root Test

CALLING SEQUENCE

res = Maddala_Wu(arg1,…, argn)

PARAMETERS

Input

• arg1,…, argn: arguments that can be:

• the strings:

  - 'lagorders=x' with x=%nan (if the lags for the ADF test are to be determined automatically) or a (N x 1) or (1 x N) vector of lags for the ADF test

  - 'pmax=x' with x=%nan or a number if for the maximal # of lags for the ADF test

  - 'signif=x' with x the signifance level for the individual ADF tests (0.05, 0.01 or 0.1)

  - 't_order=x' for the trend order with x:

     -1: no constant, no trend

• 0: a constant, no trend (default)

• 1: a constant and a trend

  - 'noprint' if the user does not want to print the results of the test

  - 'namevar=xx' where xx is the name of the variable in the panel (only if the data are in a 'panel data' tlist, see help paneldb)

• a time series

• a real (nxp) matrix

• a string equal to the name of a time series or a (nxp) real matrix between quotes (note that there must be several variables of this type tos be able to perform a panel unit root test)

Output

• res a results tlist with:

  - res('meth') = 'Maddala-Wu'

  - res('y') = (T x k) matrix of data

  - res('P') = Fisher statistic based on Individual ADF statistics

  - res('P_Critical') = Critical Values of the Fisher statistic at 1%, 5% and 10%

  - res('P_pvalue') = Pvalue Pooled test statistic (Maddala Wu 1999)

  - res('Z') = Choi (2001) statistic based on Individual ADF statistics (for large N tends to N(0,1))

  - res('Z_Critical') = Critical Values of the pooled test statistic (Choi, 2001) at 1%, 5% and 10%

  - res('Z_pvalue') = Pvalue Pooled test statistic (Choi 2001)

  - res('ADF_pvalues') = Individual pvalues of individual ADF statitics

  - res('pi') = Individual lag order in individual ADF models

  - res('pmax') = Maximum Lag Order for individual ADF regressions

  - res('Ti') = Adjusted Individual Size

  - res('ADF_tstats') = Individual ADF statistics

  - res('sample') = Starting and Ending Dates of Adjusted Sample

  - res('prests') = boolean indicating the presence or absence of a time series in the regression

  - res('namey') = name of the y variable

  - res('dropna') = boolean indicating if NAs have been dropped

  - res('bounds') = if there is a timeseries in the regression, the bounds of the regression

  - res('nonna') = vector indicating position of non-NAs

  - res('prests') = boolean indicating the presence or absence of a time series in the regression

  - res('namey') = name of the y variable

  - res('dropna') = boolean indicating if NAs have been dropped

  - res('bounds') = if there is a timeseries in the regression, the bounds of the regression

  - res('nonna') = vector indicating position of non-NAs

DESCRIPTION

Maddala and Wu(1999) Test of Unit Root "A Comparative Study of Unit Root Tests with Panel Data and a New simple test", Oxford Bulletin of Economics and Statistics, Special Issue, p. 631-652. The variant proposed by Choi (Choi, I. (2001) "Unit Root Tests for Panel Data", Journal of International Money and Finance 20, 249-272.) is also performed.

EXAMPLE

// load the database containing the GDP for 25 countries in the OECD over the period 1963-2003
load(GROCERDIR+'/macros/grocer/db/gdpan_oecd.dat');
// retrieve the names of all variables in database
listvar=dblist(GROCERDIR+'/macros/grocer/db/gdpan_oecd.dat')
res = Maddala_Wu('log('+listvar+')','pmax=4','t_order=0')
 
Example provides the Maddala and Wu test for all variables in the database, taken in logarithm, with a constant but not trend ('t_order=0'), the Lag order to be determined by the programm, the maximum number of lags set to 4 (arg 'pmax=4'). 

               

AUTHOR

Christophe Hurlin 2004 / Eric Dubois 2008