 | This article may be too technical for most readers to understand.(October 2014) |
In statistics, the Glejser test for heteroscedasticity, developed in 1969 by Herbert Glejser (fr: Herbert Glejser), regresses the residuals on the explanatory variable that is thought to be related to the heteroscedastic variance. [1] After it was found not to be asymptotically valid under asymmetric disturbances, [2] similar improvements have been independently suggested by Im, [3] and Machado and Santos Silva. [4]
Step 1: Estimate original regression with ordinary least squares and find the sample residuals ei.
Step 2: Regress the absolute value |ei| on the explanatory variable that is associated with the heteroscedasticity.
Step 3: Select the equation with the highest R2 and lowest standard errors to represent heteroscedasticity.
Step 4: Perform a t-test on the equation selected from step 3 on γ1. If γ1 is statistically significant, reject the null hypothesis of homoscedasticity.
Glejser's Test can be implemented in R software using the glejser function of the skedastic package. [5] It can also be implemented in SHAZAM econometrics software. [6]
Breusch–Pagan test
Goldfeld–Quandt test
Park test
White test
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In econometrics, the Park test is a test for heteroscedasticity. The test is based on the method proposed by Rolla Edward Park for estimating linear regression parameters in the presence of heteroscedastic error terms.
Control functions are statistical methods to correct for endogeneity problems by modelling the endogeneity in the error term. The approach thereby differs in important ways from other models that try to account for the same econometric problem. Instrumental variables, for example, attempt to model the endogenous variable X as an often invertible model with respect to a relevant and exogenous instrument Z. Panel analysis uses special data properties to difference out unobserved heterogeneity that is assumed to be fixed over time.
Richard T. Baillie is a British–American economist and statistician who is currently the A J Pasant Professor of Economics at the Michigan State University. He is also part time professor at King's College, London, and Senior Scientific Officer for the Rimini Center for Economic Analysis in Italy, and also on the Executive Council of the Society for Nonlinear Dynamics in Econometrics (SNDE).
In statistics, a sequence of random variables is homoscedastic if all its random variables have the same finite variance; this is also known as homogeneity of variance. The complementary notion is called heteroscedasticity, also known as heterogeneity of variance. The spellings homoskedasticity and heteroskedasticity are also frequently used. Assuming a variable is homoscedastic when in reality it is heteroscedastic results in unbiased but inefficient point estimates and in biased estimates of standard errors, and may result in overestimating the goodness of fit as measured by the Pearson coefficient.
