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Arima Model

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   ARIMA Modelling and Forecasting  Introduction ã Describe the stationarity of the AR process ã Determine the mean and variance of the  AR process ã  Assess the importance of Box-Jenkins methodology ã Describe the various types of forecasts ã Evaluate the measures of forecast importance  Stationarity of the AR process ã If an AR model is not stationary, this implies that previous values of the error term will have a non-declining effect on the current value of the dependent variable. ã This implies that the coefficients on the MA process would not converge to zero as the lag length increases. ã For an AR model to be stationary, the coefficients on the corresponding MA process decline with lag length, converging on 0.   AR Process ã The test for stationarity in an AR model (with p lags)is that the roots of the characteristic equation lie outside the unit circle (i.e. > 1), where the characteristic equation is: 0...1  221     p  p  z  z  z      
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