www.gusucode.com > econ 案例源码程序 matlab代码 > econ/EstimateParametersofaRegressionModelwithARIMAErrorsWithoExample.m
%% Estimate Parameters of Regression Model Containing ARIMA Errors Without Initial Values % Fit this regression model with ARMA(2,1) errors to simulated data: % % $$\begin{array}{*{20}{l}} % \begin{array}{c} % {y_t} = {X_t}\left[ {\begin{array}{*{20}{c}} % {0.1}\\ % { - 0.2} % \end{array}} \right] + {u_t}\\ % {u_t} = 0.5{u_{t - 1}} - 0.8{u_{t - 2}} + {\varepsilon _t} - 0.5{\varepsilon _{t - 1}}, % \end{array} % \end{array}$$ % % where $\varepsilon_{t}$ is Gaussian with variance 0.1. % Copyright 2015 The MathWorks, Inc. %% % Specify the regression model ARMA(2,1) errors. Simulate responses from % the model and two predictor series. Mdl = regARIMA('Intercept',0,'AR',{0.5 -0.8}, ... 'MA',-0.5,'Beta',[0.1 -0.2],'Variance',0.1); rng(1); X = randn(100,2); y = simulate(Mdl,100,'X',X); %% % Specify a regression model with ARMA(2,1) errors with no intercept, and % unknown coefficients and variance. ToEstMdl = regARIMA(2,0,1); ToEstMdl.Intercept = 0 % Exclude the intercept %% % The AR coefficients, MA coefficients, and the innovation variance % are |NaN| values. |estimate| estimates those parameters, but not the intercept. % The intercept is held fixed at 0. %% % Fit the regression model with ARMA(2,1) errors to the data. EstMdl = estimate(ToEstMdl,y,'X',X,'Display','params'); %% % The result, |EstMdl|, is a new |regARIMA| model. The estimates in |EstMdl| % resemble the parameter values that generated the simulated data.