1 Suppose an appliance manufacturer is doing a regression an

1. Suppose an appliance manufacturer is doing a regression analysis, using quarterly time-series data, of the factors affecting its sales of appliances. A regression equation was estimated between appliance sales (in dollars) as the dependent variable and disposable personal income and new housing starts 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 presenta. What are some of the possible causes of this autocorrelation?;b. How does this autocorrelation affect the conclusions concerning the significance of the individual explanatory variables and the overall explanatory power of the regression model?;c. Given that a person uses the model for forecasting future appliance sales, how does this autocorrelation affect the accuracy of these forecasts?;d. What techniques might be used to remove this autocorrelation from the model?

Solution

The causes of auto correlation are

1. Bias in the data

2.The data is not reliable there must be some change in the data.

The results is

two or more independent variables are correlated ie., multicolliniearity.

the function might be sometimes non linear.

auto correlation might underestimate the true variance.

Null hyothesis might be rejected although it is true.

The remedy is

increase the number of observations

find the missing values

estimators although linear are not the efficient estimators