--- title: bgod description: This page provides the usage, parameters, and examples of the Breusch-Godfrey autocorrelation test in Python. It's useful for individuals working with OLS Regression Results and other econometrics. The BGOD page increases understanding of this functionality within Python. keywords: - Breusch-Godfrey - Autocorrelation Test - OLS Regression Results - Econometrics - BGOD - Metadata - Least Squares Method - Durbin-Watson --- import HeadTitle from '@site/src/components/General/HeadTitle.tsx'; Show Breusch-Godfrey autocorrelation test results.Needs OLS to be run in advance with independent and dependent variables ### Usage ```python bgod [-l LAGS] ``` --- ## Parameters | Name | Description | Default | Optional | Choices | | ---- | ----------- | ------- | -------- | ------- | | lags | The lags for the Breusch-Godfrey test | 3 | True | None | --- ## Examples ```python 2022 Jun 01, 06:29 (๐Ÿฆ‹) /econometrics/ $ load longley -a ll 2022 Jun 01, 06:29 (๐Ÿฆ‹) /econometrics/ $ ols -d ll.totemp -i ll.gnpdefl,ll.gnp,ll.unemp,ll.armed,ll.pop,ll.year OLS Regression Results ======================================================================================= Dep. Variable: ll.totemp R-squared (uncentered): 1.000 Model: OLS Adj. R-squared (uncentered): 1.000 Method: Least Squares F-statistic: 5.052e+04 Date: Wed, 01 Jun 2022 Prob (F-statistic): 8.20e-22 Time: 12:29:44 Log-Likelihood: -117.56 No. Observations: 16 AIC: 247.1 Df Residuals: 10 BIC: 251.8 Df Model: 6 Covariance Type: nonrobust ============================================================================== coef std err t P|t| [0.025 0.975] ------------------------------------------------------------------------------ ll.gnpdefl -52.9936 129.545 -0.409 0.691 -341.638 235.650 ll.gnp 0.0711 0.030 2.356 0.040 0.004 0.138 ll.unemp -0.4235 0.418 -1.014 0.335 -1.354 0.507 ll.armed -0.5726 0.279 -2.052 0.067 -1.194 0.049 ll.pop -0.4142 0.321 -1.289 0.226 -1.130 0.302 ll.year 48.4179 17.689 2.737 0.021 9.003 87.832 ============================================================================== Omnibus: 1.443 Durbin-Watson: 1.277 Prob(Omnibus): 0.486 Jarque-Bera (JB): 0.605 Skew: 0.476 Prob(JB): 0.739 Kurtosis: 3.031 Cond. No. 4.56e+05 ============================================================================== Notes: [1] Rยฒ is computed without centering (uncentered) since the model does not contain a constant. [2] Standard Errors assume that the covariance matrix of the errors is correctly specified. [3] The condition number is large, 4.56e+05. This might indicate that there are strong multicollinearity or other numerical problems. Warnings: kurtosistest only valid for n=20 ... continuing anyway, n=16 2022 Jun 01, 06:29 (๐Ÿฆ‹) /econometrics/ $ bgod Breusch-Godfrey autocorrelation test [Lags: 3] โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ โ”ƒ โ”ƒ Breusch-Godfrey โ”ƒ โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ โ”‚ LM-stat โ”‚ 10.35 โ”‚ โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค โ”‚ p-value โ”‚ 0.02 โ”‚ โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค โ”‚ f-stat โ”‚ 0.10 โ”‚ โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค โ”‚ fp-value โ”‚ 0.96 โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ The result 0.02 indicates no existence of autocorrelation. ``` ---