What is the difference between Homoskedasticity and heteroskedasticity?

What is the difference between Homoskedasticity and heteroskedasticity?

Homoskedasticity occurs when the variance of the error term in a regression model is constant. Oppositely, heteroskedasticity occurs when the variance of the error term is not constant.

What is Homoscedasticity and heteroscedasticity?

Simply put, homoscedasticity means “having the same scatter.” For it to exist in a set of data, the points must be about the same distance from the line, as shown in the picture above. The opposite is heteroscedasticity (“different scatter”), where points are at widely varying distances from the regression line.

Is serial correlation heteroskedasticity?

Serial correlation or autocorrelation is usually only defined for weakly stationary processes, and it says there is nonzero correlation between variables at different time points. Heteroskedasticity means not all of the random variables have the same variance.

How do you know if you have heteroskedasticity?

To check for heteroscedasticity, you need to assess the residuals by fitted value plots specifically. Typically, the telltale pattern for heteroscedasticity is that as the fitted values increases, the variance of the residuals also increases.

What is Multicollinearity econometrics?

Multicollinearity is the occurrence of high intercorrelations among two or more independent variables in a multiple regression model. In general, multicollinearity can lead to wider confidence intervals that produce less reliable probabilities in terms of the effect of independent variables in a model.

Does IID imply Homoskedasticity?

Viewed in this light, the “i.i.d. assumption” has some implications for the marginal distribution of the errors, as well as for autocorrelation (it implies no-autocorrelation of the errors), but it does not cover conditional homo/heteroskedasticity.

What is autocorrelation econometrics?

Autocorrelation represents the degree of similarity between a given time series and a lagged version of itself over successive time intervals. Autocorrelation measures the relationship between a variable’s current value and its past values.

What if data is Heteroscedastic?

How to Deal with Heteroscedastic Data

1. Give data that produces a large scatter less weight.
2. Transform the Y variable to achieve homoscedasticity. For example, use the Box-Cox normality plot to transform the data.

What are the consequences of autocorrelation in econometrics?

The OLS estimators will be inefficient and therefore no longer BLUE. The estimated variances of the regression coefficients will be biased and inconsistent, and therefore hypothesis testing is no longer valid. In most of the cases, the R2 will be overestimated and the t-statistics will tend to be higher.

What is serial correlation econometrics?

Serial correlation is the relationship between a given variable and a lagged version of itself over various time intervals. It measures the relationship between a variable’s current value given its past values. A variable that is serially correlated indicates that it may not be random.

Why is 58.7% of cruncheconometrix models heteroscedastic?

Since there is no economic phenomenon to support that outrageous figure, then 58.7% is an outlier which may cause your model to become heteroscedastic. · Wrongly specifying your model is another factor. This can be related to the functional form by which your model is specified.

How is heteroscedasticity related to model misspecification?

• Heteroscedasticity can also be the result of model misspecification. • It can arise as a result of the presence of outliers (either very small or very large). The inclusion/exclusion of an outlier, especially if T is small, can affect the results of regressions.

What does heteroskedasticity do to a statistic?

· Heteroskedasticity causes statistical inference based on the usual t and F statistics to be invalid, even in large samples. As heteroskedasticity is a violation of the Gauss-Markov assumptions, OLS is no longer BLUE.

Which is the best test for heteroscedasticity?

Testing for Heteroscedasticity: BP Test The LM test is asymptotically equivalent to a TR2test, where R2is calculated from a regression of ei2/ R2on the variables Z. Usual calculation of the Breusch-Pagan test