By using the quarterly time-series data an appliance manufacturer is doing a regression analysis, of the factors affecting its sales of appliances. A regression equation was estimated among the appliance sales as the dependent variable and disposable the personal income and the new housing start as the independent variables. The statistical tests of the model showed large t-values for both independent variables, along with a high r2 value. However, analysis of the residuals indicated that substantial autocorrelation was present.
1. Explain some of the possible causes of this autocorrelation?
2. Discuss how does this autocorrelation affect the conclusions concerning the significance of the individual explanatory variables and the overall explanatory power of the regression model?
3. Given that a person uses the model for forecasting future appliance sales, explain how does this autocorrelation affect the accuracy of these forecasts?
4. Explain what techniques might be used to remove this autocorrelation from the model?