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%% Test Model of Real U.S. GNP for Structural Change % Using the Chow test, assess the stability of an explanatory model of U.S. % real gross national product (GNP) using the end of World War II as a % break point. %% % Load the Nelson-Plosser data set. % Copyright 2015 The MathWorks, Inc. load Data_NelsonPlosser %% % The time series in the data set contain annual, macroeconomic % measurements from 1860 to 1970. For more details, a list of variables, % and descriptions, enter |Description| in the command line. %% % Several series have missing data. Focus the sample to measurements from % 1915 to 1970. span = (1915 <= dates) & (dates <= 1970); %% % Assume that an appropriate multiple regression model to describe real GNP % is % % $$\texttt{GNPR}_t=\beta_0+\beta_1\texttt{IPI}_t+\beta_2\texttt{E}_t+\beta_3\texttt{WR}_t.$$ % %% % Collect the model variables into a tabular array. Position the predictors % in the first three columns, and the response in the last column. Mdl = DataTable(span,[4,5,10,1]); %% % Select the index corresponding to 1945, the end of World War II. bp = find(strcmp(Mdl.Properties.RowNames,'1945')); %% % Using 1945 as a break point, conduct a break point test to assess whether % all regression coefficients are stable. h = chowtest(Mdl,bp) %% % |h = 1| indicates to reject the null hypothesis that the regression % coefficients between the subsamples are equivalent. %% % In addition to returning a test decision, you can request that a test % summary display in the Command Window. h = chowtest(Mdl,bp,'Display','summary');