www.gusucode.com > econ 案例源码程序 matlab代码 > econ/RegressionModelwithARErrorsandtInnovationsExample.m
%% Regression Model with AR Errors and t Innovations % This example shows how to set the innovation distribution of a regression % model with AR errors to a $t$ distribution. % Copyright 2015 The MathWorks, Inc. %% % Specify the regression model with AR(4) errors: % % $$\begin{array}{l}{y_t} = {X_t}\left[ \begin{array}{l} - 2\\0.5\end{array} \right] + {u_t}\\{u_t} = 0.2{u_{t - 1}} + 0.1{u_{t - 4}} + {\varepsilon _t},\end{array}$$ % % where $\varepsilon_t$ has a $t$ distribution with the default degrees of freedom % and unit variance. Mdl = regARIMA('AR',{0.2,0.1},'ARLags',[1,4],... 'Constant',0,'Beta',[-2;0.5],'Variance',1,... 'Distribution','t') %% % The default degrees of freedom is |NaN|. If you don't know the degrees % of freedom, then you can estimate it by passing |Mdl| and the data to % |estimate|. %% % Specify a $t_{10}$ distribution. Mdl.Distribution = struct('Name','t','DoF',10) %% % You can simulate or forecast responses using |simulate| or |forecast| % because |Mdl| is completely specified. %% % In applications, such as simulation, the software normalizes the random % $t$ innovations. In other words, |Variance| overrides the theoretical % variance of the $t$ random variable (which is |DoF|/(|DoF| - 2)), but % preserves the kurtosis of the distribution.