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%% Test Trend Stationarity by Specifying Lags % Test whether the wage series in the manufacturing sector (1900-1970) has a % unit root. %% % Load the Nelson-Plosser Macroeconomic data set. % Copyright 2015 The MathWorks, Inc. load Data_NelsonPlosser wages = DataTable.WN; T = sum(isfinite(wages)); % Sample size without NaNs sqrtT = sqrt(T) % See Kwiatkowski et al., 1992 %% % Plot the wages series. plot(dates,wages) title('Wages') axis tight %% % The plot suggests that the wages series grows exponentially. %% % Linearize the wages series. logWages = log(wages); plot(dates,logWages) title('Log Wages') axis tight %% % The plot suggests that the log wages series has a linear trend. %% % Test the hypothesis that the log wages series is a unit root process with % a trend (i.e., difference stationary), against the alternative that there % is no unit root (i.e., trend stationary). Conduct the test by setting a % range of lags around $\sqrt{T}$, as suggested in Kwiatkowski et al., % 1992. [h,pValue] = kpsstest(logWages,'lags',[7:10]) %% % All tests fail to reject the null hypothesis that the log wages series is % trend stationary. %% % The warning messages do not indicate a problem. Rather, they indicate that the p-values are larger than 0.1. The software % compares the test statistic to critical values and computes p-values that % it interpolates from tables in Kwiatkowski et al., 1992.