Maddala and Wu Panel Unit Root Test
res = Maddala_Wu(arg1,..., argn)
* arg1,..., argn = arguments that can be:
-a panel data tlist (in that case there must be an argument 'namevar= xxx' to indicate the name of the variable in the panel, see below)
-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 significance 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 (n x p) matrix
- a string equal to the name of a time series or a (n x p) real matrix between quotes (note that there must be several variables of this type tos be able to perform a panel unit root test)
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
// load the database containing the GDP for 25 countries in the OECD over the period 1963-2003 load(GROCERDIR+'/data/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') // 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'). | ![]() | ![]() |