scholarly journals Improving the Interpretation of Fixed Effects Regression Results

2018 ◽  
Vol 6 (4) ◽  
pp. 829-835 ◽  
Author(s):  
Jonathan Mummolo ◽  
Erik Peterson
Keyword(s):  
Panel Data ◽  
Selection Bias ◽  
Fixed Effects ◽  
Fixed Effects Models ◽  
Independent Variable ◽  
Fixed Effects Regression ◽  
Time Invariant ◽  
Substantive Importance

Fixed effects estimators are frequently used to limit selection bias. For example, it is well known that with panel data, fixed effects models eliminate time-invariant confounding, estimating an independent variable’s effect using only within-unit variation. When researchers interpret the results of fixed effects models, they should therefore consider hypothetical changes in the independent variable (counterfactuals) that could plausibly occur within units to avoid overstating the substantive importance of the variable’s effect. In this article, we replicate several recent studies which used fixed effects estimators to show how descriptions of the substantive significance of results can be improved by precisely characterizing the variation being studied and presenting plausible counterfactuals. We provide a checklist for the interpretation of fixed effects regression results to help avoid these interpretative pitfalls.

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.

Limitations of Fixed-Effects Models for Panel Data

2019 ◽  
Vol 63 (3) ◽  
pp. 357-369 ◽  
Author(s):  
Terrence D. Hill ◽  
Andrew P. Davis ◽  
J. Micah Roos ◽  
Michael T. French
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Statistical Power ◽  
Unobserved Heterogeneity ◽  
Critical Discussion ◽  
Causal Inferences ◽  
Cross Sectional ◽  
Fixed Effects Models ◽  
Time Invariance ◽  
Type Ii Errors

Although fixed-effects models for panel data are now widely recognized as powerful tools for longitudinal data analysis, the limitations of these models are not well known. We provide a critical discussion of 12 limitations, including a culture of omission, low statistical power, limited external validity, restricted time periods, measurement error, time invariance, undefined variables, unobserved heterogeneity, erroneous causal inferences, imprecise interpretations of coefficients, imprudent comparisons with cross-sectional models, and questionable contributions vis-à-vis previous work. Instead of discouraging the use of fixed-effects models, we encourage more critical applications of this rigorous and promising methodology. The most important deficiencies—Type II errors, biased coefficients and imprecise standard errors, misleading p values, misguided causal claims, and various theoretical concerns—should be weighed against the likely presence of unobserved heterogeneity in other regression models. Ultimately, we must do a better job of communicating the pitfalls of fixed-effects models to our colleagues and students.

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.

Forced Displacement in Colombia: What Determines Expulsion and Reception in Municipalities?

2014 ◽  
Vol 20 (4) ◽  
pp. 585-597 ◽  
Author(s):  
Ximena Dueñas ◽  
Paola Palacios ◽  
Blanca Zuluaga
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Independent Variables ◽  
Panel Data Estimation ◽  
Vector Decomposition ◽  
Displaced People ◽  
Time Invariant ◽  
Data Estimation ◽  
Conflict Intensity ◽  
Municipal Level

AbstractThis document explores the expulsion and reception determinants of displaced people among Colombian municipalities. For this purpose, we use fixed effects panel data estimations for the period 2004–2009, with municipality year as the unit of analysis. To the best of our knowledge, this is the first paper in Colombia that focuses on reception and the first one using panel data at municipal level to explain expulsion and reception. We find that, contrary to what one may expect, some independent variables affect both expulsion and reception of displaced people in the same direction; for instance, municipalities where homicide rates and conflict intensity are high, are associated with both higher reception and expulsion rates. In addition to the conventional panel data estimation, we also run a fixed effect vector decomposition to identify the explicit effects of certain time-invariant variables.

scholarly journals ANALISIS PENGARUH UTANG BERBASIS BUNGA DAN PENDAPATAN NON-HALAL TERHADAP NILAI PERUSAHAAN EMITEN SAHAM SYARIAH

2018 ◽  
Vol 4 (2) ◽  
pp. 103
Author(s):  
Muhammad Adlan ◽  
Imron Mawardi
Keyword(s):  
Panel Data ◽  
Firm Value ◽  
Fixed Effects ◽  
Lower Portion ◽  
Standard Errors ◽  
Fixed Effects Models ◽  
Total Debt ◽  
Robust Standard Errors ◽  
Operating Revenue

This study aims to determine whether interest-based debt limitation and non-halal income limitation have significant effect on the firm value. Sharia stock issuers in Indonesia are obliged to pass several conditions set by the market regulator, some of them are limitations of the interest-based debt and non-halal income. This study assumes that the lower portion of interest-based debt and non-halal income, the more the investors will prefer the stocks, thus increasing the firm value. The subjects of this study are the companies listed on JII period 2013-2017. This study measures interest-based debt with ratio of interest-based debt devided by total debt, measures non-halal income with ratio of non-halal income divided by operating revenue, and measures the value of the firm with PBV. The analysis of this study using panel data regressions with fixed effects models with robust standard errors. The results shows that interest-based debt and non-halal income have no effects on the value of the firm, partially and simultaneously

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 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 Do people reduce compliance with COVID-19 guidelines following vaccination? A longitudinal analysis of matched UK adults

2021 ◽  
pp. jech-2021-217179
Author(s):  
Liam Wright ◽  
Andrew Steptoe ◽  
Hei Wan Mak ◽  
Daisy Fancourt
Keyword(s):  
At Risk ◽  
Panel Data ◽  
Longitudinal Analysis ◽  
Fixed Effects ◽  
Social Study ◽  
Vaccination Status ◽  
Perceived Risks ◽  
Social Distancing ◽  
Fixed Effects Regression ◽  
The Uk

IntroductionCOVID-19 vaccines do not confer immediate immunity and vaccinated individuals may still be at risk of transmitting the virus. Governments have not exempted vaccinated individuals from behavioural measures to reduce the spread of COVID-19, such as practising social distancing. However, vaccinated individuals may have reduced compliance with these measures, given lower perceived risks.MethodsWe used monthly panel data from October 2020 to March 2021 in the UK COVID-19 Social Study to assess changes in compliance following vaccination. Compliance was measured with two items on compliance with guidelines in general and compliance with social distancing. We used matching to create comparable groups of individuals by month of vaccination (January, February or not vaccinated by February) and fixed effects regression to estimate changes in compliance over the study period.ResultsCompliance increased between October 2020 and March 2021, regardless of vaccination status or month of vaccination. There was no clear evidence that vaccinated individuals decreased compliance relative to those who were not yet vaccinated.ConclusionThere was little evidence that sample members vaccinated in January or February reduced compliance after receiving vaccination for COVID-19. Continued monitoring is required as younger individuals receive the vaccine, lockdown restrictions are lifted and individuals receive second doses of the vaccine.

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