Presentation on theme: "Heteroskedasticity Hill et al Chapter 11. Predicting food expenditure Are we likely to be better at predicting food expenditure at: –low incomes; –high."— Presentation transcript:
Heteroskedasticity Hill et al Chapter 11
Predicting food expenditure Are we likely to be better at predicting food expenditure at: –low incomes; –high incomes?
The nature of heteroskedasticity
Violation of assumption MR. 3
Consequences of Heteroskedasticity The least squares estimator is still a linear and unbiased estimator, but it is no longer best. It is no longer B.L.U.E. The standard errors usually computed for the least squares estimator are incorrect. Confidence intervals and hypothesis tests that use these standard errors may be misleading.
Whites estimator of the standard error in the presence of hetero.
Transforming the model to make it homoskedastic
Comparing the estimates from OLS and GLS GLS OLS and White
Detecting Hetero. Residual plots. –Simple regression –Multiple regression, plot against: each explanatory variable time fitted values Goldfield and Quandt test
The Goldfield and Quandt Test Split the sample in two (according to expected pattern of hetero.) Compute variances for both samples. Compute GQ stat: Reject null of equal variances if:
Example of GQ test
A sample with a heteroskedastic partition Quantity = f (Price, Technology, Weather)
Testing the Variance Assumption
GLS through transformation
Implementation of GLS Estimate 2 for each sub-sample by OLS