www.gusucode.com > econ 案例源码程序 matlab代码 > econ/InspectRealUSGNPModelForInstabilityExample.m
%% Inspect Real U.S. GNP Model for Instability % Apply recursive regressions using nested windows to look for % instability in an explanatory model of real GNP for a period spanning % World War II. %% % Load the Nelson-Plosser data set. 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. Identify the breakpoint index corresponding to 1945, the end of the % war. span = (1915 <= dates) & (dates <= 1970); bp = find(dates(span) == 1945); %% % Consider the multiple linear regression model % % $$\texttt{GNPR}_t=\beta_0+\beta_1\texttt{IPI}_t+\beta_2\texttt{E}_t+\beta_3\texttt{WR}_t.$$ % %% % Collect the model variables into a tabular array. Position the predictors % in the first three columns, and the response in the last column. Compute % the number of coefficients in the model. Mdl = DataTable(span,[4,5,10,1]); numCoeff = size(Mdl,2); % Three predictors and an intercept %% % Estimate the coefficients using recursive regressions, and return % separate plots for the iterative estimates. Identify the iteration % corresponding to the end of the war. recreg(Mdl); bpIter = bp - numCoeff %% % By default, |recreg| forms the subsamples using nested windows. The end % of the war (1945) occurs at the 27th iteration. %% % All coefficients show some initial, transient instability during the the % "burn-in" period (see <docid:econ_ug.bu55p4z-5>). The plot of |WR| seems % stable since the line is relatively flat. However, the plots of |E|, % |IPI|, and the intercept (|Const|) show instability, particularly just % after iteration 27.