Forecasting

When I go for forecasting any variable, I normally use few methods. ARIMA (Auto-regressive integrated moving average method) is one of them where four steps are involved. They are identification meaning how many would be AR and MA. Afterwards I estimate the model using appropriate AR and MA. Then I go for diagnostic checking of the model meaning that whether the residual of the model is stationary or not. If it is stationary, meaning that our ARIMA model is perfect. Finally I go for forescting of the variable.

But in ARIMA make it sure, variable must be stationary. Some time ARIMA model is called Box Jenkins Methodology. It is one of the complex models in econometrics analysis.

The complicated part of ARIMA model is to find out approproaite number of AR and MA. There is some guideline such as shape of the ACF and PACF distribution from there we can know how many would be AR and MA>

Second appropach I use for forecsting is VAR model. It is easy to use. Specially under EVIESW it is very simple. But make sure data must ne statioanry.

In time series operation, data must be stationary.