scholarly journals On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data

2020 ◽  
pp. 1-11 ◽  
Author(s):  
Kosuke Imai ◽  
In Song Kim
Keyword(s):  
Panel Data ◽  
Causal Inference ◽  
Fixed Effects ◽  
Regression Models ◽  
Applied Research ◽  
Causal Effects ◽  
Difference In Differences ◽  
The Difference ◽  
Fixed Effects Regression ◽  
Time Specific

Abstract The two-way linear fixed effects regression (2FE) has become a default method for estimating causal effects from panel data. Many applied researchers use the 2FE estimator to adjust for unobserved unit-specific and time-specific confounders at the same time. Unfortunately, we demonstrate that the ability of the 2FE model to simultaneously adjust for these two types of unobserved confounders critically relies upon the assumption of linear additive effects. Another common justification for the use of the 2FE estimator is based on its equivalence to the difference-in-differences estimator under the simplest setting with two groups and two time periods. We show that this equivalence does not hold under more general settings commonly encountered in applied research. Instead, we prove that the multi-period difference-in-differences estimator is equivalent to the weighted 2FE estimator with some observations having negative weights. These analytical results imply that in contrast to the popular belief, the 2FE estimator does not represent a design-based, nonparametric estimation strategy for causal inference. Instead, its validity fundamentally rests on the modeling assumptions.

scholarly journals Asymmetric Fixed Effects Models for Panel Data

2018 ◽  
Author(s):  
Paul D Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed effects methods presume that effects of variables are symmetric: the effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light (2017) showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this paper, I show that there are several aspects of their method that need improvement. I also develop a data generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

scholarly journals Asymmetric Fixed-effects Models for Panel Data

2019 ◽  
Vol 5 ◽  
pp. 237802311982644 ◽  
Author(s):  
Paul D. Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed-effects methods presume that effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this article, I show that there are several aspects of their method that need improvement. I also develop a data-generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

Estimation of pre- and posttreatment average treatment effects with binary time-varying treatment using Stata

2019 ◽  
Vol 19 (3) ◽  
pp. 551-565 ◽  
Author(s):  
Giovanni Cerulli ◽  
Marco Ventura
Keyword(s):  
Program Evaluation ◽  
Longitudinal Data ◽  
Causal Inference ◽  
Graphical Representation ◽  
Causal Effects ◽  
Necessary Condition ◽  
Time Varying ◽  
Difference In Differences ◽  
Average Treatment Effects ◽  
The Difference

In this article, we describe tvdiff, a community-contributed command that implements a generalization of the difference-in-differences estimator to the case of binary time-varying treatment with pre- and postintervention periods. tvdiff is flexible and can accommodate many actual situations, enabling the user to specify the number of pre- and postintervention periods and a graphical representation of the estimated coefficients. In addition, tvdiff provides two distinct tests for the necessary condition of the identification of causal effects, namely, two tests for the so-called parallel-trend assumption. tvdiff is intended to simplify applied works on program evaluation and causal inference when longitudinal data are available.

scholarly journals Interactions in Fixed Effects Regression Models

2020 ◽  
pp. 004912412091493
Author(s):  
Marco Giesselmann ◽  
Alexander W. Schmidt-Catran
Keyword(s):  
Monte Carlo ◽  
Time Constant ◽  
Fixed Effects ◽  
Regression Models ◽  
Time Varying ◽  
Unit Level ◽  
Monte Carlo Experiments ◽  
Product Term ◽  
Fixed Effects Regression ◽  
Effect Heterogeneity

An interaction in a fixed effects (FE) regression is usually specified by demeaning the product term. However, algebraic transformations reveal that this strategy does not yield a within-unit estimator. Instead, the standard FE interaction estimator reflects unit-level differences of the interacted variables. This property allows interactions of a time-constant variable and a time-varying variable in FE to be estimated but may yield unwanted results if both variables vary within units. In such cases, Monte Carlo experiments confirm that the standard FE estimator of x ⋅ z is biased if x is correlated with an unobserved unit-specific moderator of z (or vice versa). A within estimator of an interaction can be obtained by first demeaning each variable and then demeaning their product. This “double-demeaned” estimator is not subject to bias caused by unobserved effect heterogeneity. It is, however, less efficient than standard FE and only works with T > 2.

scholarly journals Let’s Talk About Fixed Effects: Let’s Talk About All the Good Things and the Bad Things

2020 ◽  
Vol 72 (2) ◽  
pp. 289-299
Author(s):  
Matthias Collischon ◽  
Andreas Eberl
Keyword(s):  
Panel Data ◽  
Least Squares ◽  
Fixed Effects ◽  
Regression Models ◽  
Ordinary Least Squares

Abstract With the broader availability of panel data, fixed effects (FE) regression models are becoming increasingly important in sociology. However, in some studies the potential pitfalls of these models may be ignored, and common critiques of FE models may not always be applicable in comparison to other methods. This article provides an overview of linear FE models and their pitfalls for applied researchers. Throughout the article, we contrast FE and classical pooled ordinary least squares (OLS) models. We argue that in most cases FE models are at least as good as pooled OLS models. Therefore, we encourage scholars to use FE models if possible. Nevertheless, the limitations of FE models should be known and considered.

scholarly journals Language Variation Suite: A theoretical and methodological contribution for linguistic data analysis

2016 ◽  
Vol 1 ◽  
pp. 29 ◽  
Author(s):  
Olga Scrivner ◽  
Manuel Díaz-Campos
Keyword(s):  
Fixed Effects ◽  
Regression Models ◽  
Quantitative Methods ◽  
Language Variation ◽  
Mixed Effects ◽  
Logistic Regression Models ◽  
Linguistic Data ◽  
Diachronic Study ◽  
Phonetic Context ◽  
Fixed Effects Regression

In recent years there has been growing interest in quantitative methods for analyzing linguistic data.  Advanced multifactorial statistical analyses, such as inferential trees and mixed-effects logistic regression models, have become more accessible for linguistic research as a result of the availability of an open source programming environment provided by the statistical software R. In the present paper, we introduce a novel toolkit, Language Variation Suite, a software program that offers a friendly environment for conducting quantitative analyses. We demonstrate how theory built on traditional monofactorial analysis can be extended to macro and micro multifactorial approaches allowing for a deeper understanding of language variation. The focus of the analysis is based on intervocalic /d/ deletion in Spanish from the Diachronic Study of the Speech of Caracas 1987 and 2004-2010. In contrast to traditional methodological approaches we have treated intervocalic /d/ as a continuous dependent variable according to the intensity ratio measurements obtained. Furthermore, we have integrated various syntactic, phonetic and sociolinguistic factors. Non-parametric and fixed-effects regression models revealed that overall age (younger speakers), sex (male speakers), phonetic context (low vowels), token frequency and morphosyntactic category (past participles) have a significant effect on the lenition of intervocalic /d/. In contrast, the mixed-effects model selected only phonetic context, frequency and category, showing that individual speaker variation is higher than group variation.

scholarly journals The Interplay of Having an Abortion, Relationship Satisfaction, and Union Dissolution

2021 ◽  
Vol 46 ◽  
Author(s):  
Kristin Hajek
Keyword(s):  
Panel Data ◽  
Relationship Satisfaction ◽  
Fixed Effects ◽  
Union Dissolution ◽  
Prospective Data ◽  
Causal Mechanisms ◽  
Significant Difference ◽  
Negative Effect ◽  
Using Data ◽  
Fixed Effects Regression

This study researches the associations between having an abortion, relationship satisfaction, and union dissolution. Empirical evidence on this topic is scarce, and there is a pronounced lack of studies analysing longitudinal data: Most previous studies have used data from women recruited from abortion clinics who are about to undergo an abortion, and therefore do not incorporate a prospective measure of relationship satisfaction pre-pregnancy. Panel studies, on the other hand, collect prospective data on various topics and allow for the estimation of more advanced models that can help identify causal mechanisms. Using data from the German Family Panel pairfam in combination with pooled logistic regressions, discrete-time event history models, as well as fixed effects regression models, this study compares relationships up to nine years before having had an abortion and eight years afterwards. The findings of the analyses can neither confirm that relationship satisfaction acts as a confounding factor that influences both the likelihood of terminating a pregnancy and union dissolution, nor as a mediating factor between having an abortion and union dissolution. A negative effect of having an abortion on relationship satisfaction appears to be only temporary. In the year of an abortion, relationship satisfaction decreases slightly. In the following years, a significant difference in relationship satisfaction to pre-abortion years is no longer visible. By using panel data, the temporal order of events can be retraced, resulting in the discovery that relationship satisfaction and union dissolution do not change drastically from pre-abortion values after having an abortion. * This article belongs to a special issue on "Identification of causal mechanisms in demographic research: The contribution of panel data".

scholarly journals Winner–loser effects in contentious constitutional referenda: Perceptions of procedural fairness and the Brexit referendum

2020 ◽  
pp. 136914812093285
Author(s):  
Cees van der Eijk ◽  
Jonathan Rose
Keyword(s):  
European Union ◽  
United Kingdom ◽  
Panel Data ◽  
Public Perceptions ◽  
Causal Effects ◽  
Difference In Differences ◽  
Critical Gap ◽  
The United Kingdom ◽  
General Elections ◽  
Over Time

This article addresses a critical gap in the literature on winner–loser effects that consists of the lack of attention for highly contentious constitutional referenda. It uses unique multi-wave panel data of over 13,000 people that is unrivalled in size and richness. We estimate causal effects of the referendum on rarely studied but crucial public perceptions of the fairness of the way a referendum is conducted. These perceptions pertain to the highly contentious 2016 European Union (Brexit) referendum in the United Kingdom, which is an ideal-type example of a wider class of referenda for which similar outcomes can be expected. We use difference-in-differences methods and find winner–loser effects of a magnitude far greater than ever observed for general elections. Moreover, we find that these effects not only persist, but even grow over time. The findings have profound implications for the use of such referenda.

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