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68 lines
2.9 KiB
Markdown
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68 lines
2.9 KiB
Markdown
Vendored
---
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title: dwat
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description: OpenBB Terminal Function
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---
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# dwat
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Show autocorrelation tests from Durbin-Watson. Needs OLS to be run in advance with independent and dependent variables
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### Usage
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```python
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dwat [-p]
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```
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---
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## Parameters
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| Name | Description | Default | Optional | Choices |
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| ---- | ----------- | ------- | -------- | ------- |
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| plot | Plot the residuals | False | True | None |
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---
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## Examples
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```python
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2022 Feb 24, 05:59 (🦋) /econometrics/ $ ols -d adj_close-msft -i adj_close-aapl -i adj_close-googl -i adj_close-tsla
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OLS Regression Results
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==============================================================================
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Dep. Variable: adj_close_msft R-squared: 0.977
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Model: OLS Adj. R-squared: 0.977
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Method: Least Squares F-statistic: 1.068e+04
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Date: Thu, 24 Feb 2022 Prob (F-statistic): 0.00
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Time: 12:00:01 Log-Likelihood: -2830.6
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No. Observations: 759 AIC: 5669.
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Df Residuals: 755 BIC: 5688.
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Df Model: 3
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Covariance Type: nonrobust
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===================================================================================
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coef std err t P|t| [0.025 0.975]
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-----------------------------------------------------------------------------------
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Intercept 27.7984 2.166 12.832 0.000 23.546 32.051
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adj_close_aapl 0.8662 0.034 25.503 0.000 0.800 0.933
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adj_close_googl 0.0508 0.002 30.374 0.000 0.048 0.054
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adj_close_tsla -0.0007 0.004 -0.181 0.856 -0.009 0.007
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==============================================================================
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Omnibus: 41.445 Durbin-Watson: 0.044
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Prob(Omnibus): 0.000 Jarque-Bera (JB): 47.398
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Skew: 0.612 Prob(JB): 5.10e-11
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Kurtosis: 2.995 Cond. No. 1.16e+04
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==============================================================================
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Notes:
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[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
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[2] The condition number is large, 1.16e+04. This might indicate that there are
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strong multicollinearity or other numerical problems.
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2022 Feb 24, 06:00 (🦋) /statistics/ $ dwat -p
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The result 0.04 is outside the range 1.5 and 2.5 and therefore autocorrelation can be problematic.
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Please consider lags of the dependent or independent variable.
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```
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---
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