Xlstat breusch pagan test windows#
More details Android, Windows Econometrics The concept of distributed lag models easily generalizes to the context of more than one right-side explanatory variable.
Select the Residuals (Sugar) column in the Residuals box, and the Age column in the explanatory variables box. Open the XLSTAT menu and click on Time / Tests for heteroscedasticity. In a finite distributed lag model, the parameters could be directly estimated by ordinary least squares (assuming the number of data points sufficiently exceeds the number of lag weights) nevertheless, such estimation may give very imprecise results due to extreme multicollinearity among the various lagged values of the independent variable, so again it may be necessary to assume some structure for the relation between the various lag weights. Performing Breusch-Pagan and White heteroscedasticity tests in XLSTAT. In an infinite distributed lag model, an infinite number of lag weights need to be estimated clearly this can be done only if some structure is assumed for the relation between the various lag weights, with the entire infinitude of them expressible in terms of a finite number of assumed underlying parameters. In the alternative, second, equation, there are only a finite number of lag weights, indicating an assumption that there is a maximum lag beyond which values of the independent variable do not affect the dependent variable a model based on this assumption is called a finite distributed lag model. In the first equation, the dependent variable is assumed to be affected by values of the independent variable arbitrarily far in the past, so the number of lag weights is infinite and the model is called an infinite distributed lag model. Suppose that we estimate the regression model y = β 0 + β 1 x + u, where yt is the value at time period t of the dependent variable y, a is the intercept term to be estimated, and wi is called the lag weight (also to be estimated) placed on the value i periods previously of the explanatory variable x. In that case, heteroskedasticity is present. It tests whether the variance of the errors from a regression is dependent on the values of the independent variables. Dennis Cook and Sanford Weisberg in 1983. It was independently suggested with some extension by R. Breusch–Pagan testIn statistics, the Breusch–Pagan test, developed in 1979 by Trevor Breusch and Adrian Pagan, is used to test for heteroskedasticity in a linear regression model.