What happens when errors are Heteroskedastic?

When the scatter of the errors is different, varying depending on the value of one or more of the independent variables, the error terms are heteroskedastic. Heteroskedasticity has serious consequences for the OLS estimator. Although the OLS estimator remains unbiased, the estimated SE is wrong.

Does heteroskedasticity increase variance?

Typically, the telltale pattern for heteroscedasticity is that as the fitted values increases, the variance of the residuals also increases.

How does heteroskedasticity affect standard errors and how do we fix that?

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.

What does it mean that the error term is Heteroskedastic?

Heteroskedastic refers to a condition in which the variance of the residual term, or error term, in a regression model varies widely. If so, then the model may be poorly defined and should be modified so that this systematic variance is explained by one or more additional predictor variables.

What is econometrics specification error?

In the context of a statistical model, specification error means that at least one of the key features or assumptions of the model is incorrect. Some forms of misspecification will result in misleading estimates of the parameters, and other forms will result in misleading confidence intervals and test statistics.

Does heteroskedasticity increase standard errors?

Only if there is heteroskedasticity will the “normal” standard error be inappropriate, which means that the White Standard Error is appropriate with or without heteroskedasticity, that is, even when your model is homoskedastic.

What is Heteroscedasticity in econometrics?

As it relates to statistics, heteroskedasticity (also spelled heteroscedasticity) refers to the error variance, or dependence of scattering, within a minimum of one independent variable within a particular sample.

When should you use robust standard errors?

Robust standard errors can be used when the assumption of uniformity of variance, also known as homoscedasticity, in a linear-regression model is violated. This situation, known as heteroscedasticity, implies that the variance of the outcome is not constant across observations.

When errors take the shape of a tube This phenomenon is referred to as?

This phenomenon is known as homoskedasticity. The presence of non-constant variance is referred to heteroskedasticity. The error terms must be normally distributed.

What are the assumptions of CLRM?

Assumptions of Classical Linear Regression Models (CLRM)

• Assumption 1: Linear Parameter and correct model specification.
• Assumption 2: Full Rank of Matrix X.
• Assumption 3: Explanatory Variables must be exogenous.
• Assumption 4: Independent and Identically Distributed Error Terms.

What is the difference between homoskedastic and heterosksedastic errors?

When this condition holds, the error terms are homoskedastic, which means the errors have the same scatter regardless of the value of X. When the scatter of the errors is different, varying depending on the value of one or more of the independent variables, the error terms are heteroskedastic.

What is heteroskedasticity-consistent standard error?

heteroskedasticity is heteroskedasticity-consistent standard errors (or robust errors) developed by White. We use OLS (inefficient but) consistent estimators, and calculate an alternative (“robust”) standard error that allows for the possibility of heteroskedasticity. From above, 2 2 1 2 2 1 var N ii i N n n xx b xx

What does homoskedastic mean in statistics?

We focus in this chapter on the requirement that the tickets in the box for each draw are identically distributed across every X variable. When this condition holds, the error terms are homoskedastic, which means the errors have the same scatter regardless of the value of X.

Is there a formal hypothesis test to detect heteroskedasticity?

Although heteroskedasticity can sometimes be identified by eye, Section 19.4 presents a formal hypothesis test to detect heteroskedasticity. Section 19.5 describes the most common way in which econometricians handle the problem of heteroskedasticity – using a modified computation of the estimated SE that yields correct reported SEs.