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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. [3]
Statistical tests are used to test the fit between a hypothesis and the data. [1] [2] Choosing the right statistical test is not a trivial task. [1]The choice of the test depends on many properties of the research question.
Breusch–Godfrey test; Breusch–Pagan statistic – redirects to Breusch–Pagan test; Breusch–Pagan test; Brown–Forsythe test; Brownian bridge; Brownian excursion; Brownian motion; Brownian tree; Bruck–Ryser–Chowla theorem; Burke's theorem; Burr distribution; Business statistics; Bühlmann model; Buzen's algorithm; BV4.1 (software)
He is noted for the Breusch–Pagan test from the paper (with Adrian Pagan) "A simple test for heteroscedasticity and random coefficient variation" (see Noted works, below). Another contribution to econometrics is the serial correlation Lagrange multiplier test, often called Breusch–Godfrey test after Breusch and Leslie G. Godfrey , which can ...
Student's t test for testing inclusion of a single explanatory variable, or the F test for testing inclusion of a group of variables, both under the assumption that model errors are homoscedastic and have a normal distribution. Change of model structure between groups of observations. Structural break test. Chow test; Comparing model structures
It should only contain pages that are Statistical tests or lists of Statistical tests, ... Breusch–Godfrey test; Breusch–Pagan test; Brown–Forsythe test;
The Breusch–Godfrey test is named after him and Trevor S. Breusch. [1] He is an emeritus professor of econometrics at the University of York . He is the author of "Misspecification tests in econometrics: the Lagrange multiplier principle and other approaches" [ 2 ] and "Bootstrap Tests for Regression Models".
In statistics, the Durbin–Watson statistic is a test statistic used to detect the presence of autocorrelation at lag 1 in the residuals (prediction errors) from a regression analysis. It is named after James Durbin and Geoffrey Watson. The small sample distribution of this ratio was derived by John von Neumann (von Neumann, 1941).