Using Heteroscedasticity Consistent Standard Errors in the Linear Regression ModelANALYSIS of covarianceSTATISTICSHETEROSCEDASTICITYIn the presence of heteroscedasticity, ordinary least squares (OLS) estimates are unbiased, but the usual tests of significance are generally inappropriate and their use can lead ...
Communications in statistics, B. Simulation and computationUsing Heteroscedasticity-Consistent Standard Errors for the Linear Regression Model with Correlated Regressors[J] . Muhammad Aslam.Communications in Statistics - Simulation and Computation . 2014 (10)...
In my post aboutchecking the residual plots, I explain the importance of verifying theOLS linear regression assumptions. You want these plots to display random residuals (no patterns) that are uncorrelated and uniform. Generally speaking, if you see patterns in the residuals, your model has a pr...
tions in Economics. Local Polynomial Estimation of Heteroscedasticity in a Multivariate Linear Regression Model and Its Applications in Economics.Local Polynomial Estimation of Heteroscedasticity in a Multivariate Linear Regression Model and Its Applications in Economics.doi:10.1371/journal.pone.0043719...
In a heteroscedastic linear model, it is known that if the variances are a parametric function of the design, then one can construct an estimate of the regression parameter which is asymptotically equivalent to the weighted least squares estimate with known variances. We show that the same is tr...
Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contrib...
Breusch Pagan test (named after Trevor Breusch and Adrian Pagan) is used to check for the presence of heteroscedasticity in a linear regression
Heteroskedasticity is a violation of the assumptions for linear regression modeling, and so it can impact the validity ofeconometric analysisor financial models like CAPM. While heteroskedasticity does not cause bias in the coefficient estimates, it does make them less precise; lower precision increases...
Heteroscedasticity is a problem that arises in linear regression due to the unequal error variances. One of the methods to deal heteroscedasticity in classical regression theory is weighted least square regression (WLS). In order to deal the problem of heteroscedasticity, backpropagation neural network...
The scope of this paper is the presentation of a test that enables to detect heteroscedasticity in univariate regression model. The test is simple to compute and very general since no hypothesis is made on the regularity of the response function or on the normality of errors. Simulations show ...