Linearmodels Compat Statsmodels Summary Linearmodels 7 0

add additional text that will be added at the end in text format add_table_2cols(res[, title, gleft, gright, ...]) Add a double table, 2 tables with one column merged horizontally add_table_params(res[, yname, xname, alpha, ...]) create and add a table for the parameter estimates pip install linearmodels Copy PIP instructions

Linear Panel, Instrumental Variable, Asset Pricing, and System Regression models for Python Linear (regression) models for Python. Extends statsmodels with Panel regression, instrumental variable estimators, system estimators and models for estimating asset prices: Designed to work equally well with NumPy, Pandas or xarray data. Like statsmodels to include, supports formulas for specifying models. For example, the classic Grunfeld regression can be specified

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Find centralized, trusted content and collaborate around the technologies you use most. Bring the best of human thought and AI automation together at your work. Name of endogenous (response) variable. The Default is y. Names for the exogenous variables. Default is var_## for ## in the number of regressors.

Must match the number of parameters in the model. Title for the top table. If not None, then this replaces the default title. The significance level for the confidence intervals. Flag indicating to produce reduced set or diagnostic information. Default is False.

Summary table of model estimation results Supports export to csv, html and latex using the methods summary.as_csv(), summary.as_html() and summary.as_latex(). I’ve built dozens of regression models over the years, and here’s what I’ve learned: the math behind linear regression is straightforward, but getting it right requires understanding what’s happening under the hood. That’s where statsmodels shines. Unlike scikit-learn, which optimizes for prediction, statsmodels gives you the statistical framework to understand relationships in your data. Let’s work through linear regression in Python using statsmodels, from basic implementation to diagnostics that actually matter.

Statsmodels is a Python library that provides tools for estimating statistical models, including ordinary least squares (OLS), weighted least squares (WLS), and generalized least squares (GLS). Think of it as the statistical counterpart to scikit-learn. Where scikit-learn focuses on prediction accuracy, statsmodels focuses on inference: understanding which variables matter, quantifying uncertainty, and validating assumptions. The library gives you detailed statistical output including p-values, confidence intervals, and diagnostic tests. This matters when you’re not just predicting house prices but explaining to stakeholders why square footage matters more than the number of bathrooms. Start with the simplest case: one predictor variable.

Here’s a complete example using car data to predict fuel efficiency: Linear models with independently and identically distributed errors, and for errors with heteroscedasticity or autocorrelation. This module allows estimation by ordinary least squares (OLS), weighted least squares (WLS), generalized least squares (GLS), and feasible generalized least squares with autocorrelated AR(p) errors. See Module Reference for commands and arguments. \(Y = X\beta + \epsilon\), where \(\epsilon\sim N\left(0,\Sigma\right).\) Depending on the properties of \(\Sigma\), we have currently four classes available:

GLS : generalized least squares for arbitrary covariance \(\Sigma\) Stable documentation for the latest release is located at doc. Documentation for recent developments is located at devel. Estimation and inference in some common linear models that are missing from statsmodels: Single equation Instrumental Variables (IV) models Generalized Method of Moments (GMM, IVGMM)

Continuously Updating GMM (CUE-GMM, IVGMMCUE) csv – concatenated summary tables in comma delimited format Summary table of model estimation results Supports export to csv, html and latex using the methods summary.as_csv(), summary.as_html() and summary.as_latex().

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