4.3.4.0 SUMMARY
In this unit, an indirect technique known as the adaptive expectations process is explained as a makeshift solution used in some models. This involves simple learning process for which, in each period, the size of adjustment is proportional to the discrepancy between the actual and expected value.
4.3.5.0 CONCLUSION
The unit introduces the application of regression analysis to time series data, starting with static models and then proceeding to dynamic models with lagged variables used as descriptive variables. In this unit, the adaptive expectations process was mainly used for explanation but the multicollinearity problem in time series models, especially dynamic ones with lagged descriptive variables was also explained. The students may use the reference materials for more understanding and further readings.
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