TD_BREUSCH_GODFREY Function | Teradata Vantage - TD_BREUSCH_GODFREY - Teradata Vantage

Teradata® VantageCloud Lake

Deployment
VantageCloud
Edition
Lake
Product
Teradata Vantage
Published
January 2023
ft:locale
en-US
ft:lastEdition
2024-12-11
dita:mapPath
phg1621910019905.ditamap
dita:ditavalPath
pny1626732985837.ditaval
dita:id
phg1621910019905

TD_BREUSCH_GODFREY checks for the presence of serial correlation among the residual and error terms after running a regression associated with a fitted model. With respect to regression models, it is expected that there is no serial correlation among the error terms.

The Breusch-Godfrey test determines if there is evidence of autocorrelation in the residuals of a model. Autocorrelation is correlation between the errors or residuals of a regression model meaning errors are not independent. The value of an error at a given point in time is related to the earlier error values.

TD_BREUSCH_GODFREY tests the hypothesis that there is or is not autocorrelation in the residuals. The test regresses the residuals on their lagged values for a number of lags, and then performing a chi-squared test on the residuals of the regression.

If autocorrelation is present in the residuals of a regression model, the standard errors of the estimates of the regression coefficients may be biased, and lead to incorrect inference and predictions. Using TD_BREUSCH_GODFREY in time-series analysis and econometrics, you can identify potential problems with you regression model and correct them.

The following procedure is an example of how to use TD_BREUSCH_GODFEY:
  1. Use TD_LINEAR_REGR to produce an ART containing a residual layer.
  2. Use TD_BREUSCH_GODFREY on the ART produced by the TD_LINEAR_REGR function to perform the Breusch Godfrey Statistical Test.
  3. Retrieve the TD_BREUSCH_GODFREY results from the primary layer to determine if there is serial correlation. A returned NULL_HYPOTH value of ACCEPT means no serial correlation. A returned NULL_HYPOTH value of REJECT means there is evidence of serial correlation.