What is breusch Godfrey serial correlation LM test?
The Breusch–Godfrey test is a test for autocorrelation in the errors in a regression model. It makes use of the residuals from the model being considered in a regression analysis, and a test statistic is derived from these. The null hypothesis is that there is no serial correlation of any order up to p.
How do you check Heteroskedasticity in EViews?
To test for this form of heteroskedasticity, an auxiliary regression of the log of the original equation’s squared residuals on is performed. The LM statistic is then the explained sum of squares from the auxiliary regression divided by , the derivative of the log gamma function evaluated at 0.5.
What does serially correlated mean?
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.
What is LM test used for?
The Lagrange Multiplier (LM) test is a general principle for testing hy- potheses about parameters in a likelihood framework. The hypothesis under test is expressed as one or more constraints on the values of parameters. To perform an LM test only estimation of the parameters subject to the re- strictions is required.
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 Heteroskedasticity test?
Breusch-Pagan & White heteroscedasticity tests let you check if the residuals of a regression have changing variance. In Excel with the XLSTAT software.
What is the difference between the breusch Godfrey test and the Durbin-Watson test?
The Durbin-Watson test only looks at autocorrelation at lag 1, while the Breusch-Godfrey test looks at all autocorrelations up to lag h. If you can rule out autocorrelations beyond order 1 a priori (which may or may not be the case depending on your application), the Durbin-Watson test will be sufficient.
How do you test for serial correlation?
The presence of serial correlation can be detected by the Durbin-Watson test and by plotting the residuals against their lags. The subscript t represents the time period. In econometric work, these u’s are often called the disturbances.