--- title: dwat description: The 'dwat' documentation page provides comprehensive information about Durbin-Watson autocorrelation test, its usage with Ordinary Least Squares (OLS) regression, and potential issues and solutions with multi-collinearity and numerical problems. The page features examples with a Python command, its parameters, and regression results to provide a practical understanding of the concepts. keywords: - dwat - autocorrelation tests - Durbin-Watson - OLS - independent variables - dependent variables - regression results - plot - residuals - multicollinearity - numerical problems - analytics - statistics - econometrics - statistical analysis --- import HeadTitle from '@site/src/components/General/HeadTitle.tsx'; Show autocorrelation tests from Durbin-Watson. Needs OLS to be run in advance with independent and dependent variables ### Usage ```python dwat [-p] ``` --- ## Parameters | Name | Description | Default | Optional | Choices | | ---- | ----------- | ------- | -------- | ------- | | plot | Plot the residuals | False | True | None | --- ## Examples ```python 2022 Feb 24, 05:59 (🦋) /econometrics/ $ ols -d adj_close-msft -i adj_close-aapl -i adj_close-googl -i adj_close-tsla OLS Regression Results ============================================================================== Dep. Variable: adj_close_msft R-squared: 0.977 Model: OLS Adj. R-squared: 0.977 Method: Least Squares F-statistic: 1.068e+04 Date: Thu, 24 Feb 2022 Prob (F-statistic): 0.00 Time: 12:00:01 Log-Likelihood: -2830.6 No. Observations: 759 AIC: 5669. Df Residuals: 755 BIC: 5688. Df Model: 3 Covariance Type: nonrobust =================================================================================== coef std err t P|t| [0.025 0.975] ----------------------------------------------------------------------------------- Intercept 27.7984 2.166 12.832 0.000 23.546 32.051 adj_close_aapl 0.8662 0.034 25.503 0.000 0.800 0.933 adj_close_googl 0.0508 0.002 30.374 0.000 0.048 0.054 adj_close_tsla -0.0007 0.004 -0.181 0.856 -0.009 0.007 ============================================================================== Omnibus: 41.445 Durbin-Watson: 0.044 Prob(Omnibus): 0.000 Jarque-Bera (JB): 47.398 Skew: 0.612 Prob(JB): 5.10e-11 Kurtosis: 2.995 Cond. No. 1.16e+04 ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 1.16e+04. This might indicate that there are strong multicollinearity or other numerical problems. 2022 Feb 24, 06:00 (🦋) /statistics/ $ dwat -p The result 0.04 is outside the range 1.5 and 2.5 and therefore autocorrelation can be problematic. Please consider lags of the dependent or independent variable. ``` ![durbin_watson example](https://user-images.githubusercontent.com/46355364/155514788-caaa65a2-1f5f-41d0-8db2-06e682d5a53e.png) ---