mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-11-13 04:41:24 +00:00
f2ca215132
* Add Statistics Menu * Add Granger Causality test * Apply Black formatting * Add Cointegration Tests * Adjust plotting for Cointegration test * Add Significant parameter to Cointegration tests * Move regression functions to seperate .py files * Progress on Panel Data * A lot of progress for Panel Data * Make functions robust and improve documentation * Re-enable Breusch-Godfrey * Add modify functionality * Improve modify functionality * Add Breusch-Pagan heteroscedasticity test * Capitalize a word * Include documentatin for the Statistics Menu * Update _index.md * Update _index.md * Update _index.md * Fix export statements and add Example datasets * Update example with Longley's dataset * Update documentation with a legit source * Compare the results from each regression models based on the wage_panel dataset * Updated with all relevant types of regression tables * Update with all relevant regression types for Panel data * Update _index.md * Add change column type, improve OLS, add time and entity effects for FE * Update documentation and fix a small bug * Add in Statistics menu, replacing Custom menu * Remove custom menu * Add in documentation * Add in gst files * Cointegration can be used on any amount of columns * Add Tests * Make tests time invariant * Update Poetry and Requirements * Change name of Statistics menu to Econometrics menu * Rename scripts * Add type in Documentation * Change names from Statistics to Econometrics * Add graph * Update tests with rounding and naming * Make minor adjustments to fix the tests * Updating tests : allow extra args for capture * Apply recorder formatting * Adding some minor formatting * Fix error with MyPy * Attempt to fix MyPy annoyance * super small style things * Fix small bugs and add plot command to dwat * Small description mistake * Update documentation with missing argument * Update styling * Add error handling and add improve show functionality * Fix merge issue * Allow import from custom_imports Co-authored-by: Jeroen Bouma <jeroenbouma@MacBook-Pro-van-Jeroen.local> Co-authored-by: jmaslek <jmaslek11@gmail.com> Co-authored-by: Chavithra PARANA <chavithra@gmail.com>
7.0 KiB
Vendored
7.0 KiB
Vendored
usage: ols [-r OPTIONS [OPTIONS ...]] [-h] [-h] [--export {csv,json,xlsx}]
Performs an OLS regression.
In statistics, ordinary least squares (OLS) is a type of linear least squares method for estimating the unknown parameters in a linear regression model. OLS chooses the parameters of a linear function of a set of explanatory variables by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values of the variable being observed) in the given dataset and those predicted by the linear function of the independent variable. [Source: Wikipedia]
optional arguments:
-r {OPTIONS} --regression {OPTIONS}
The regression you would like to perform (default: None)
-h, --help show this help message (default: False)
--export {csv,json,xlsx}
Export raw data into csv, json, xlsx (default: )
Example (based on Longley's Dataset):
2022 Feb 25, 06:58 (✨) /econometrics/ $ load longley ll
2022 Feb 25, 06:58 (✨) /econometrics/ $ info ll
ll
┏━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓
┃ ┃ totemp ┃ gnpdefl ┃ gnp ┃ unemp ┃ armed ┃ pop ┃ year ┃
┡━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩
│ count │ 16.00 │ 16.00 │ 16.00 │ 16.00 │ 16.00 │ 16.00 │ 16.00 │
├───────┼──────────┼─────────┼───────────┼─────────┼─────────┼───────────┼─────────┤
│ mean │ 65317.00 │ 101.68 │ 387698.44 │ 3193.31 │ 2606.69 │ 117424.00 │ 1954.50 │
├───────┼──────────┼─────────┼───────────┼─────────┼─────────┼───────────┼─────────┤
│ std │ 3511.97 │ 10.79 │ 99394.94 │ 934.46 │ 695.92 │ 6956.10 │ 4.76 │
├───────┼──────────┼─────────┼───────────┼─────────┼─────────┼───────────┼─────────┤
│ min │ 60171.00 │ 83.00 │ 234289.00 │ 1870.00 │ 1456.00 │ 107608.00 │ 1947.00 │
├───────┼──────────┼─────────┼───────────┼─────────┼─────────┼───────────┼─────────┤
│ 25% │ 62712.50 │ 94.53 │ 317881.00 │ 2348.25 │ 2298.00 │ 111788.50 │ 1950.75 │
├───────┼──────────┼─────────┼───────────┼─────────┼─────────┼───────────┼─────────┤
│ 50% │ 65504.00 │ 100.60 │ 381427.00 │ 3143.50 │ 2717.50 │ 116803.50 │ 1954.50 │
├───────┼──────────┼─────────┼───────────┼─────────┼─────────┼───────────┼─────────┤
│ 75% │ 68290.50 │ 111.25 │ 454085.50 │ 3842.50 │ 3060.75 │ 122304.00 │ 1958.25 │
├───────┼──────────┼─────────┼───────────┼─────────┼─────────┼───────────┼─────────┤
│ max │ 70551.00 │ 116.90 │ 554894.00 │ 4806.00 │ 3594.00 │ 130081.00 │ 1962.00 │
└───────┴──────────┴─────────┴───────────┴─────────┴─────────┴───────────┴─────────┘
2022 Feb 25, 06:59 (✨) /econometrics/ $ ols totemp-ll gnpdefl-ll gnp-ll unemp-ll armed-ll pop-ll year-ll
OLS Regression Results
=======================================================================================
Dep. Variable: totemp_ll R-squared (uncentered): 1.000
Model: OLS Adj. R-squared (uncentered): 1.000
Method: Least Squares F-statistic: 5.052e+04
Date: Fri, 25 Feb 2022 Prob (F-statistic): 8.20e-22
Time: 12:59:32 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]
------------------------------------------------------------------------------
gnpdefl_ll -52.9936 129.545 -0.409 0.691 -341.638 235.650
gnp_ll 0.0711 0.030 2.356 0.040 0.004 0.138
unemp_ll -0.4235 0.418 -1.014 0.335 -1.354 0.507
armed_ll -0.5726 0.279 -2.052 0.067 -1.194 0.049
pop_ll -0.4142 0.321 -1.289 0.226 -1.130 0.302
year_ll 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] R² 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