Statsmodels Library Summary Col Function With Fixed Effect Capability

Leo Migdal

-Dec 4, 2025, 10:35 AM

statsmodels library summary col function with fixed effect capability

Instantly share code, notes, and snippets. The summary_col function in statsmodels makes nice regression tables easy to create. When you add a categorical variable to your model, it automatically adds a variable for each level. Sometimes, these coefficients have meaning and are of interest. However, this isn’t always true. For example, in an earlier page noted that you can modify a model from \(profits=a+b*investment+c*X+u\), where the focus is on understanding how investments translate to profits, to \(profits=a+b*investment+c*X+d*C(gsector)+e*C(year)+u\).

The latter model is better, but the coefficients on gsector and year are not the focus (and are difficult to interpret). Aside: When a categorical variable has many levels, it is often called a “fixed effect”. So the latter model, which adds industry and year to a regression as a categorical variable, is said to include “industry fixed effects” and “year fixed effect”. The point of industry fixed effects is usually not to understand the coefficients on the industry dummy variables. It is to “control for industry”, and it changes the interpretation of \(b\): It is the relationship between investment and profits, holding fixed the industry. The same goes for the year fixed effects.

Thus, in the improved model, \(b\) shows the relationship for two firms in the same industry in the same year. When a categorical variable has a lot of levels, and seeing those values is not important, the output tables are easier to read if you drop those coefficients. This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository. We also encourage users to submit their own examples, tutorials or cool statsmodels trick to the Examples wiki page SARIMAX: Frequently Asked Questions (FAQ)

State space modeling: Local Linear Trends Fixed / constrained parameters in state space models Communities for your favorite technologies. Explore all Collectives Stack Overflow for Teams is now called Stack Internal. Bring the best of human thought and AI automation together at your work.

Bring the best of human thought and AI automation together at your work. Learn more 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. Summarize multiple results instances side-by-side (coefs and SEs) results : statsmodels results instance or list of result instances

float format for coefficients and standard errors Default : ‘%.4f’ model_names : list of strings of length len(results) if the names are not unique, a roman number will be appended to all model names When building a regression model using Python’s statsmodels library, a key feature is the detailed summary table that is printed after fitting a model. This summary provides a comprehensive set of statistics that helps you assess the quality, significance, and reliability of your model. In this article, we’ll walk through the major sections of a regression summary output in statsmodels and explain what each part means.

Before you can get a summary, you need to fit a model. Here’s a basic example: Let’s now explore each section of the summary() output. The regression summary indicates that the model fits the data reasonably well, as evidenced by the R-squared and adjusted R-squared values. Significant predictors are identified by p-values less than 0.05. The sign and magnitude of each coefficient indicate the direction and strength of the relationship.

The F-statistic and its p-value confirm whether the overall model is statistically significant. If the key assumptions of linear regression are met, the model is suitable for inference and prediction. There was an error while loading. Please reload this page. (Following up from thread on mailing list: https://groups.google.com/forum/?hl=en#!topic/pystatsmodels/BnySoFBCcAE) I'm estimating some simple OLS models that have dozens or hundreds of fixed effects terms, but I want to omit these estimates from the summary_col.

Looking under the hood, it appears that the Summary object is just a DataFrame which means it should be possible to do some index slicing here to return the appropriate rows, but the Summary... This returns a Summary object that has 55 rows (52 for the two fixed effects + the intercept + exogenous C and D terms). I would like a summary object that excludes the 52 fixed effects estimates and only includes the estimates for C, D, and the intercept for all four models. What's the best way to remove fixed effects from the summary_col? Alternatively, how can I create a Summary object that only includes specific regressors and excludes the rest? There was an error while loading.

Please reload this page. It looks like info_dict is in rows before fixed_effects fixed_effect presence/non-presence should be considered as part of params and be before extra info from info_dict dropping fixed effects was added in #9280 support for infodict for non-OLS models #9281

Statsmodels Library Summary Col Function With Fixed Effect Capability

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