www.gusucode.com > econ 案例源码程序 matlab代码 > econ/DiagnosticPlotsForRecursiveRegressionCoefficientExample.m
%% Diagnostic Plots for Recursive Regression Coefficient Estimates % Plot the recursive regression coeffiicent estimates of the explanatory % model of real gross national product (GNP) as outlined in % <docid:econ_ug.bu30834-2>. %% % Load the Nelosson-Plosser data set. load Data_NelsonPlosser %% % Several series have missing data. Focus the sample to measurements from % 1915 to 1970. span = (1915 <= dates) & (dates <= 1970); %% % Collect the model variables into a tabular array. Position the % response as the last variable. Mdl = DataTable(span,[4,5,10,1]); %% % Conduct a forward and backward cusum test. Store the recursive % regression coefficient estimates. [~,~,~,~,B] = cusumtest(Mdl,'Direction',{'forward','backward'},'Plot','on'); %% % The forward test rejects the null hypothesis that the coefficients are % stable. The backward test fails to reject the null hypothesis. %% % B is a 4-by-53-by-2 numeric array containing the coefficient estimates. % % * Rows correspond to coefficients starting with the intercept, and then % following the order of the predictors in |Mdl|. % * Columns correspond to recursive regression iterations. The first % column is a vector of |NaN| values because the model has an intercept. % * Column 2 includes observations 1 - 4 for the forward test, and % observations 53 - 56 for the backward test. Column 3 includes % observations 1 - 5 for the forward test, and observations 52 - 56 for the % backward test, and so on. % * Pages correspond to tests: the first page is the forward test and the % second page is the backward test. % %% % Plot the recursive regression coefficients for each test. coeffName = [{'Intercept'} Mdl.Properties.VariableNames(1:end-1)]; for k = 1:size(B,3) figure; for j = 1:size(B,1) subplot(2,2,j); plot(B(j,:,k)); title(sprintf('%s',coeffName{j})); xlabel('Iteration') ylabel('Coefficient Value') axis tight end end %% % The coefficients from the forward recursive regression seem to vary % throughout the sample. However, the coefficients from the backward % regression seem to settle midway through the backward recursive % regressions.