feologit: A new command for fitting fixed-effects ordered logit models

Abstract

In this article, we describe how to fit panel-data ordered logit models with fixed effects using the new community-contributed command feologit. Fixed-effects models are increasingly popular for estimating causal effects in the social sciences because they flexibly control for unobserved time-invariant heterogeneity. The ordered logit model is the standard model for ordered dependent variables, and this command is the first in Stata specifically for this model with fixed effects. The command includes a choice between two estimators, the blowup and cluster (BUC) estimator introduced in Baetschmann, Staub, and Winkelmann (2015, Journal of the Royal Statistical Society, Series A 178: 685–703) and the BUC- τ estimator in Baetschmann (2012, Economics Letters 115: 416–418). Baetschmann, Staub, and Winkelmann (2015) showed that the BUC estimator has good properties and is almost as efficient as more complex estimators such as generalized method-of-moments and empirical likelihood estimators. The command and model interpretations are illustrated with an analysis of the effect of parenthood on life satisfaction using data from the German Socio-Economic Panel.

Happiness, Domains of Life Satisfaction, Perceptions, and Valuation Differences Across Genders

“…The analysis can also be extended to other countries, and crosscultural differences can be investigated. 3, except that it fits panel data ordered logit models with fixed effects using the method proposed by Baetschmann et al (2020). *** p<0.01, ** p<0.05, * p<0.10.…”

“…Additionally, we estimate the major regressions suggested in Table 3 with fixed effects. Specifically, we fit panel data ordered Logit models with fixed rather than random effects using the method proposed by Baetschmann et al (2020). The purpose is to relax the assumptions of normally distributed and independent of the regressors individual-specific error terms suggested by random effect methodologies.…”

The Impact of Retirement on Happiness and Loneliness in Poland—Evidence from Panel Data

“…Fixed effects models are often used in social sciences, including in analyses of the effects of retirement [ 48 ], because they can be used to estimate casual effects by controlling for unobserved individual heterogeneity. While several estimators are used in model estimation, in this study, we employed the blow-up and cluster (BUC) estimator proposed by Baetschmann et al [ 74 , 75 ]. It has been proven that the BUC estimator has good properties, and is as efficient as more complex estimators.…”

Well-being Changes from Year to Year: A Comparison of Current, Remembered and Predicted Life Satisfaction

“…Furthermore, the regressions include year dummies and the time difference in months between the two surveys from which the satisfaction assessments stem. Table 2 reports the results of linear random and fixed effects regressions, a fixed effect ordered logistic regression by Baetschmann et al (2015Baetschmann et al ( , 2020 and a Gini regression (Schaffer 2015) as suggested by Schröder and Yitzhaki (2017). 9 A Hausman-test clearly rejects the assumption of random effects, but all estimated coefficients are very similar across the model specifications and for different samples as reported in Table 3.…”

“…Determinants of retrospective life satisfaction reported in t about t−1 Table reports coefficients from linear random and fixed effects regressions, a fixed effects ordered logit model(Baetschmann et al, 2020) and a Gini regression(Schaffer, 2015), standard errors adjusted for clustering on individuals in parentheses (except Gini regression). All regressions include year dummies.…”