Test Time Series Data for a Unit Root
This example shows how to test a univariate time series for a unit root. It uses wages data (1900-1970) in the manufacturing sector. The series is in the Nelson-Plosser data set.
Load the Nelson-Plosser data. Extract the nominal wages data.
load Data_NelsonPlosser wages = DataTable.WN;
Trim the NaN values from the series and the corresponding dates (this step is optional, since the test ignores NaN values).
wDates = dates(isfinite(wages)); wages = wages(isfinite(wages));
Plot the data to look for trends.
The plot suggests exponential growth.
Transform the data using the log function to linearize the series.
logWages = log(wages); plot(wDates,logWages) title('Log Wages')
The data appear to have a linear trend.
Test the null hypothesis that there is no unit root (trend stationary) against the alternative hypothesis that the series is a unit root process with a trend (difference stationary). Set 'lags',[7:2:11], as suggested in Kwiatkowski et al., 1992.
[h,pValue] = kpsstest(logWages,'lags',[7:2:11])
h = 1x3 logical array 0 0 0 pValue = 0.1000 0.1000 0.1000
kpsstest fails to reject the hypothesis that the wages series is trend stationary. If the result would have been [1 1 1], the two inferences would provide consistent evidence of a unit root. It remains unclear whether the data has a unit root. This is a typical result of tests on many macroeconomic series.
kpsstest has a limited set of calculated critical values. When it calculates a test statistic that is outside this range, the test reports the p-value at the appropriate endpoint. So, in this case, pValue reflects the closest tabulated value. When a test statistic lies inside the span of tabulated values, kpsstest linearly interpolates the p-value.