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%% Specify ARIMAX Model Using Name-Value Pairs % This example shows how to specify an ARIMAX model using |arima|. % Copyright 2015 The MathWorks, Inc. %% % Specify the ARIMAX(1,1,0) model that includes three predictors: % % $$(1 - 0.1L){(1 - L)^1}{y_t} = x_t^\prime {\left[ {\begin{array}{*{20}{c}}3&{ - 2}&5\end{array}} \right]^\prime } + {\varepsilon _t}.$$ % model = arima('AR',0.1,'D',1,'Beta',[3 -2 5]) %% % The output shows that the ARIMAX model, |model|, has the following qualities: % %% % * Property |P| in the output is the sum of the autoregressive lags and % the degree of integration, i.e., |P| = |p| + |D| = |2|. %% % * |Beta| contains three coefficients corresponding to the effect that % the predictors have on the response. %% % * The rest of the properties are 0, |NaN|, or empty cells. %% % Be aware that if you specify nonzero |D| or |Seasonality|, then % Econometrics Toolbox(TM) differences the response series $y_t$ before the % predictors enter the model. Therefore, the predictors enter a stationary % model with respect to the response series $y_t$. You should preprocess % the predictors $x_t$ by testing for stationarity and differencing if any % are unit root nonstationary. If any nonstationary predictor % enters the model, then the false negative rate for significance tests of % $\beta$ can increase.