Fitting exponential regression models with two-way fixed effects

2020 ◽  
Vol 20 (2) ◽  
pp. 468-480
Author(s):  
Koen Jochmans ◽  
Vincenzo Verardi
Keyword(s):  
Panel Data ◽  
Cross Section ◽  
Fixed Effects ◽  
Regression Models ◽  
Bilateral Trade ◽  
Dyadic Interactions ◽  
Cross Sectional ◽  
Trade Data ◽  
Exponential Regression ◽  
Special Case

In this article, we introduce the commands twexp and twgravity, which implement the estimators developed in Jochmans (2017, Review of Economics and Statistics 99: 478–485) for exponential regression models with two-way fixed effects. twexp is applicable to generic n × m panel data. twgravity is written for the special case where the dataset is a cross-section on dyadic interactions between n agents. A prime example is cross-sectional bilateral trade data, where the model of interest is a gravity equation with importer and exporter effects. Both twexp and twgravity can deal with data where n and m are large, that is, where there are many fixed effects. These commands use Mata and are fast to execute.

scholarly journals Modeling the consequences of the transition to parenthood: Applications of panel regression methods

2019 ◽  
Vol 36 (11-12) ◽  
pp. 4005-4026 ◽  
Author(s):  
Francisco Perales
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Regression Models ◽  
Transition To Parenthood ◽  
National Sample ◽  
Panel Regression ◽  
Cross Sectional ◽  
Time Dynamics ◽  
Advantages And Disadvantages ◽  
Panel Regression Models

The transition to parenthood is a topic of substantial interest to family researchers across the social sciences, and many theoretical paradigms have been invoked to understand how it affects men’s and women’s lives. While early empirical scholarship on the transition to parenthood relied on cross-sectional data and methods, the increasing availability of panel data has opened up new analytical pathways—including the possibility to track the same individuals over time as they approach and experience parenthood and their children grow older. By making full use of longitudinal data, researchers can both improve estimation of the consequences of parenthood, as well as advance knowledge by testing more nuanced and complex theoretical premises involving time dynamics. In this article, I present an overview of panel regression models, a family of specifications that can be leveraged for these purposes. In doing so, I discuss the data requirements, advantages and disadvantages of different models, pointing to useful examples of published research. The approaches considered include random effects and fixed effects panel regression models, specifications to model linear and nonlinear time dynamics, and specifications to handle dyadic data structures. The use of these techniques is exemplified via an application considering the effect of motherhood on time pressure using long-running panel data from an Australian national sample, the Household, Income and Labour Dynamics in Australia Survey ( n = 68,911 observations; 10,734 women).

THE MOVING BLOCKS BOOTSTRAP FOR PANEL LINEAR REGRESSION MODELS WITH INDIVIDUAL FIXED EFFECTS

2011 ◽  
Vol 27 (5) ◽  
pp. 1048-1082 ◽  
Author(s):  
Sílvia Gonçalves
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Cross Section ◽  
Fixed Effects ◽  
Regression Models ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Cross Section Dependence ◽  
Cross Sectional Dependence ◽  
Moving Blocks Bootstrap

In this paper we propose a bootstrap method for panel data linear regression models with individual fixed effects. The method consists of applying the standard moving blocks bootstrap of Künsch (1989, Annals of Statistics 17, 1217–1241) and Liu and Singh (1992, in R. LePage & L. Billiard (eds.), Exploring the Limits of the Bootstrap) to the vector containing all the individual observations at each point in time. We show that this bootstrap is robust to serial and cross-sectional dependence of unknown form under the assumption that n (the cross-sectional dimension) is an arbitrary nondecreasing function of T (the time series dimension), where T → ∞, thus allowing for the possibility that both n and T diverge to infinity. The time series dependence is assumed to be weak (of the mixing type), but we allow the cross-sectional dependence to be either strong or weak (including the case where it is absent). Under appropriate conditions, we show that the fixed effects estimator (and also its bootstrap analogue) has a convergence rate that depends on the degree of cross-section dependence in the panel. Despite this, the same studentized test statistics can be computed without reference to the degree of cross-section dependence. Our simulation results show that the moving blocks bootstrap percentile-t intervals have very good coverage properties even when the degree of serial and cross-sectional correlation is large, provided the block size is appropriately chosen.

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.

scholarly journals Bias correction methods for dynamic panel data models with fixed effects

2017 ◽  
Vol 6 (2) ◽  
pp. 58
Author(s):  
Mohamed Abonazel
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Estimation Methods ◽  
Time Dimension ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Cross Sectional Dimension ◽  
Ls Estimator

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.

scholarly journals Forecasting using cross-section average–augmented time series regressions

2020 ◽  
Author(s):  
Hande Karabiyik ◽  
Joakim Westerlund
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Regression Models ◽  
Common Factor ◽  
Principal Component ◽  
Small Sample ◽  
Interactive Effects ◽  
Cross Sectional ◽  
Second Development ◽  
The Cross

Summary There is a large and growing body of literature concerned with forecasting time series variables by the use of factor-augmented regression models. The workhorse of this literature is a two-step approach in which the factors are first estimated by applying the principal components method to a large panel of variables, and the forecast regression is then estimated, conditional on the first-step factor estimates. Another stream of research that has attracted much attention is concerned with the use of cross-section averages as common factor estimates in interactive effects panel regression models. The main justification for this second development is the simplicity and good performance of the cross-section averages when compared with estimated principal component factors. In view of this, it is quite surprising that no one has yet considered the use of cross-section averages for forecasting. Indeed, given the purpose to forecast the conditional mean, the use of the cross-sectional average to estimate the factors is only natural. The present paper can be seen as a reaction to this. The purpose is to investigate the asymptotic and small-sample properties of forecasts based on cross-section average–augmented regressions. In contrast to most existing studies, the investigation is carried out while allowing the number of factors to be unknown.

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.

Impact of the Presence of Embassies on Trade: Evidence from Turkey

2019 ◽  
Vol 19 (1) ◽  
pp. 51-60
Author(s):  
Yusuf Kenan Bagir
Keyword(s):  
Panel Data ◽  
Bilateral Trade ◽  
Panel Data Analysis ◽  
Empirical Studies ◽  
Volume Effect ◽  
Foreign Missions ◽  
Cross Sectional ◽  
Exporting Firms ◽  
Main Driver ◽  
The Impact

AbstractThis paper analyzes the impact of the presence of foreign missions on trade using Turkey's unique expansion in its foreign embassy network (39 new embassies in 8 years) as the source of variation in a panel data setting. A majority of the existing empirical studies use cross-sectional bilateral trade data due to lack of variation over time (Rose, 2007; Moons and Bergeijk, 2013). Employing a panel data analysis, this paper is able to address the endogeneity issues that are associated with a standard cross-sectional analysis. The dependent variable in the paper is the trade between Turkey and 190 countries for 2006 to 2016. The results indicate that presence of an embassy increases export value by 30% and this increase comes mainly from the volume effect. Categorizing goods by the Rauch (1999) classification shows that the increase in differentiated goods exports is the main driver of the export surge. The number of exporting firms increases by about 8%. There is no statistically significant impact on the exports of homogeneous goods. Replication of the analysis for imports suggests no impact on imports.

PANEL DATA MODELS WITH FINITE NUMBER OF MULTIPLE EQUILIBRIA

2009 ◽  
Vol 26 (3) ◽  
pp. 863-881 ◽  
Author(s):  
Jinyong Hahn ◽  
Hyungsik Roger Moon
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Multiple Equilibria ◽  
Finite Sample ◽  
Finite Support ◽  
Time Dimension ◽  
Cross Sectional ◽  
Incidental Parameters ◽  
Cross Sectional Dimension ◽  
Fixed Effects Estimator

We study a nonlinear panel data model in which the fixed effects are assumed to have finite support. The fixed effects estimator is known to have the incidental parameters problem. We contribute to the literature by making a qualitative observation that the incidental parameters problem in this model may not be not as severe as in the conventional case. Because fixed effects have finite support, the probability of correctly identifying the fixed effect converges to one even when the cross sectional dimension grows as fast as some exponential function of the time dimension. As a consequence, the finite sample bias of the fixed effects estimator is expected to be small.

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