What are the consequences of heteroscedasticity when estimating a model with OLS?

Heteroskedasticity has serious consequences for the OLS estimator. Although the OLS estimator remains unbiased, the estimated SE is wrong. Because of this, confidence intervals and hypotheses tests cannot be relied on. In addition, the OLS estimator is no longer BLUE.

What effect does heteroskedasticity in the error terms have on the properties of the OLS estimators and test statistics?

Heteroscedasticity tends to produce p-values that are smaller than they should be. This effect occurs because heteroscedasticity increases the variance of the coefficient estimates but the OLS procedure does not detect this increase.

What problem does heteroskedasticity cause for the OLS estimators show mathematically?

A nonconstant error variance, heteroscedasticity, causes the OLS estimates to be inefficient, and the usual OLS covariance matrix, ∑, is generally invalid: (6.22) for some, j > 1.

Can heteroskedasticity cause OLS estimators to be biased?

The only circumstance that will cause the OLS point estimates to be biased is b, omission of a relevant variable. Heteroskedasticity biases the standard errors, but not the point estimates. High (but not unitary) correlations among regressors do not cause any sort of bias. 3.

What happens if there is heteroskedasticity?

Heteroskedasticity refers to a situation where the variance of the residuals is unequal over a range of measured values. If heteroskedasticity exists, the population used in the regression contains unequal variance, the analysis results may be invalid.

What are the causes and consequences of heteroscedasticity?

Heteroscedasticity is mainly due to the presence of outlier in the data. Outlier in Heteroscedasticity means that the observations that are either small or large with respect to the other observations are present in the sample. Heteroscedasticity is also caused due to omission of variables from the model.

What are the bad consequences of heteroskedasticity?

Recall that the two main consequences of heteroskedasticity are 1) ordinary least squares no longer produces the best estimators and 2) standard errors computed using least squares can be incorrect and misleading. Let’s first deal with the issue of incorrect standard errors.

Do you think that in the presence of heteroscedasticity OLS estimators are biased as well as inefficient Why?

In the presence of heteroscedasticity, OLS estimators are biased as well as inefficient. If heteroscedasticity is present, the conventional t and F tests are invalid. In the presence of heteroscedasticity, the usual OLS method always overestimates the standard errors of estimators.

How does heteroskedasticity affect standard errors?

Heteroscedasticity does not cause ordinary least squares coefficient estimates to be biased, although it can cause ordinary least squares estimates of the variance (and, thus, standard errors) of the coefficients to be biased, possibly above or below the true of population variance.

Does heteroskedasticity cause inconsistency?

plays no role in showing whether OLS was unbiased or consistent. If heteroskedasticity does not cause bias or inconsistency in the OLS estimators, why did we introduce it as one of the Gauss-Markov assumptions? The estimators of the variances, V (ˆβj), are biased without the homoskedasticity assumption.

What are the consequences of autocorrelation?

Autocorrelation can cause problems in conventional analyses (such as ordinary least squares regression) that assume independence of observations. In a regression analysis, autocorrelation of the regression residuals can also occur if the model is incorrectly specified.

What is econometrics specification error?

Specification Error is defined as a situation where one or more key feature, variable or assumption of a statistical model is not correct. Specification is the process of developing the statistical model in a regression analysis.

What are the consequences for the OLS estimator if heteroscedasticity is present in a regression model but ignored?

Correct! Under heteroscedasticity, provided that all of the other assumptions of the classical linear regression model are adhered to, the coefficient estimates will still be consistent and unbiased, but they will be inefficient.

How is heteroskedasticity correct in OLS?

How to Fix Heteroscedasticity
  1. Transform the dependent variable. One way to fix heteroscedasticity is to transform the dependent variable in some way. …
  2. Redefine the dependent variable. Another way to fix heteroscedasticity is to redefine the dependent variable. …
  3. Use weighted regression.

What would be the consequences of incorrectly using the ordinary least squares estimator?

Specifically, a violation would result in incorrect signs of OLS estimates, or the variance of OLS estimates would be unreliable, leading to confidence intervals that are too wide or too narrow.

What is heteroskedasticity in econometrics?

In statistics, heteroskedasticity (or heteroscedasticity) happens when the standard deviations of a predicted variable, monitored over different values of an independent variable or as related to prior time periods, are non-constant.

What causes OLS estimators to be biased?

The only circumstance that will cause the OLS point estimates to be biased is b, omission of a relevant variable.

Why do we care about heteroskedasticity?

A final reason to care about heteroskedasticity is that the potential for it to exist is more common than is usually recognized by researchers, although there has been a growing use of estimation methods in sociology which model heteroskedasticity in some fashion.

Why do we test for heteroskedasticity?

It is customary to check for heteroscedasticity of residuals once you build the linear regression model. The reason is, we want to check if the model thus built is unable to explain some pattern in the response variable Y , that eventually shows up in the residuals.

Is heteroscedasticity good or bad?

Heteroskedasticity has serious consequences for the OLS estimator. Although the OLS estimator remains unbiased, the estimated SE is wrong. Because of this, confidence intervals and hypotheses tests cannot be relied on. In addition, the OLS estimator is no longer BLUE.

What are the remedial measures of heteroscedasticity?

If V ( μ i ) = σ i 2 then heteroscedasticity is present. Given the values of σ i 2 heteroscedasticity can be corrected by using weighted least squares (WLS) as a special case of Generalized Least Square (GLS). Weighted least squares is the OLS method of estimation applied to the transformed model.