The sample consists of 51 observations of per capita expenditure on public schools and per capita income for each state and Income, and assuming that the variance of the error term is proportional to x i 2, then the regression model in this example can be written as If y is public school spending and x is per capita This example uses the MODEL procedure to perform the preceding tests and the WLS correction in an investigation of public Often this specification is one of the regressors or its square. One way to correct for heteroscedasticity is to compute the weighted least squares (WLS) estimator using an hypothesized Which is asymptotically distributed as chi-square with degrees of freedom equal to the number of variables in Z. , e n 2), i equals an n Г—1 column of ones, and, then Koenkar andД«assett's (1982) robust variance estimator The Breusch-Pagan test is a Lagrange multiplier test of the hypothesis that the independent variables have no explanatory This statistic is asymptoticallyДistributed as chi-square with k-1 degrees of freedom, where k is the number of regressors, excluding the constant term. The White test is computed by finding nR 2 from a regression of e i 2 on all of the distinct variables in, where X is the vector of dependent variables including a constant. Tests are White's General test (White 1980) and the Breusch-Pagan test (Breusch and Pagan 1979). This test involves looking for patterns in a plot of the residuals from a regression. The most commonly used is the Time-Honored Method There are several methods of testing for the presence of heteroscedasticity. Thus, inferences from the standard errors are likely to be misleading. Income by state and its square is computed, the parameter estimates are still consistent but they are no longer efficient.

If heteroscedasticity is present and a regression of spending on per capita Greater variation in expenditure than others. For example, in analyzing public school spending, certain states may have Often arises in the analysis of cross-sectional data. If this assumption is violated, the errors are said to be 'heteroscedastic.' Heteroscedasticity One of the classical assumptions of the ordinary regression model is that the disturbance variance is constant, or homogeneous,ДЄcross observations. A Simple Regression Model with Correction of Heteroscedasticity

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