Panel series unit root testing. The z-statistic constructed from the mean t-statistic has an asymptotic standardized normal distribution and tests the null hypothesis that all series are I(1) against the alternative that all series are I(0)
breitung(y, trend, constant, demean, lags)¶
y (TxM matrix) – data, M > 5.
trend (scalar) – 0 = no trend, 1 = trend.
constant (scalar) – if nonzero constant included in model.
demean (scalar) – 0 to specify no demeaning or 1 to subtract cross-sectional means.
lags (scalar) – number of lags.
bstat (matrix) – test statistic
new; library tsmt; //Load data fname = getGAUSSHome() $+ "pkgs/tsmt/examples/index.dat"; y00 = loadd(fname); //Assign y y0 = y00[., 2:9]; //Percent Change in Y y = 100*ln(y0[2:rows(y0), .]./y0[1:rows(y0)-1, .]); //Indicator to run test with trend variable trend = 1; //Indicator to run test with constant const = 1; //Turn off data demeaning demean = 0; //Set number of lags to 3 lags = 3; //Compute test statistics tstat = breitung(y, trend, const, demean, lags); print "The Breitung test statistic = ";; tstat;
The Breitung panel series unit root test utilizes the sample mean of the t-statistics across all individual series within a panel of time series variables. However, the procedure pre-adjusts data to address biased estimation.
It is assumed that the autoregressive parameter is constant across all panels. This allows the use of the standard t-statistic but requires that the panels be strongly balanced.
The procedure performs an individual ADF test on each series n then forms the sample mean of the t-statistic. The z-statistic constructed from the mean t-statistic has an asymptotic standardized normal distribution and tests the null hypothesis that all series are I(1) against the alternative that all series are I(0).