The Estimation of Time-Invariant Variables in Panel Analyses with Unit Fixed Effects

2004 ◽  
Author(s):  
Thomas Plümper ◽  
Vera E. Troeger
Keyword(s):  
Fixed Effects ◽  
Time Invariant ◽  
Estimation Of Time

Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects

2007 ◽  
Vol 15 (2) ◽  
pp. 124-139 ◽  
Author(s):  
Thomas Plümper ◽  
Vera E. Troeger
Keyword(s):  
Fixed Effects ◽  
Error Term ◽  
Efficient Estimation ◽  
Fixed Effects Model ◽  
Finite Sample ◽  
Real World Data ◽  
Third Stage ◽  
Finite Sample Properties ◽  
Time Invariant ◽  
Estimation Of Time

This paper suggests a three-stage procedure for the estimation of time-invariant and rarely changing variables in panel data models with unit effects. The first stage of the proposed estimator runs a fixed-effects model to obtain the unit effects, the second stage breaks down the unit effects into a part explained by the time-invariant and/or rarely changing variables and an error term, and the third stage reestimates the first stage by pooled OLS (with or without autocorrelation correction and with or without panel-corrected SEs) including the time-invariant variables plus the error term of stage 2, which then accounts for the unexplained part of the unit effects. We use Monte Carlo simulations to compare the finite sample properties of our estimator to the finite sample properties of competing estimators. In doing so, we demonstrate that our proposed technique provides the most reliable estimates under a wide variety of specifications common to real world data.

Dental Care Use, Edentulism, and Systemic Health among Older Adults

2021 ◽  
pp. 002203452110190
Author(s):  
C.D. Meyerhoefer ◽  
J.V. Pepper ◽  
R.J. Manski ◽  
J.F. Moeller
Keyword(s):  
Older Adults ◽  
Heart Disease ◽  
Chronic Illness ◽  
Dental Care ◽  
Fixed Effects ◽  
Past Research ◽  
Positive Correlations ◽  
Systemic Health ◽  
Time Invariant ◽  
Retirement Study

Past research suggests there are systematic associations between oral health and chronic illness among older adults. Although causality has not yet been credibly established, periodontitis has been found to be associated with higher risk of both heart disease and stroke. We advance this literature by estimating the direct association between dental care use and systemic health using multiple waves of the 1992 to 2016 Health and Retirement Study. Through the inclusion of individual fixed effects in our regression models, we account for unobservable time-invariant characteristics of individuals that might otherwise bias estimates of the association between dental care use and health. We find statistically significant negative associations between dental care use and the number of health conditions, self-reported overall health, the incidence of heart disease, and the incidence of stroke. In particular, the use of dental care within the past 2 y is associated with a 2.7% reduction in the likelihood of being diagnosed with a heart condition and a reduction in the likelihood of a stroke diagnosis of between 5.3% and 11.6%. We also find large positive correlations between edentulism and the measures of chronic illness. Associations from models estimated separately for men and women are qualitatively similar to one another. These findings provide additional motivation for the consideration of a Medicare dental benefit.

scholarly journals Fixed Effects Vector Decomposition: A Magical Solution to the Problem of Time-Invariant Variables in Fixed Effects Models?

2011 ◽  
Vol 19 (2) ◽  
pp. 135-146 ◽  
Author(s):  
William Greene
Keyword(s):  
Fixed Effects ◽  
Ordinary Least Squares ◽  
Fe Model ◽  
Real World Data ◽  
Problem Of Time ◽  
Dummy Variable ◽  
Vector Decomposition ◽  
Step Procedure ◽  
Time Invariant ◽  
Subsequent Literature

Plümper and Troeger (2007) propose a three-step procedure for the estimation of a fixed effects (FE) model that, it is claimed, “provides the most reliable estimates under a wide variety of specifications common to real world data.” Their fixed effects vector decomposition (FEVD) estimator is startlingly simple, involving three simple steps, each requiring nothing more than ordinary least squares (OLS). Large gains in efficiency are claimed for cases of time-invariant and slowly time-varying regressors. A subsequent literature has compared the estimator to other estimators of FE models, including the estimator of Hausman and Taylor (1981) also (apparently) with impressive gains in efficiency. The article also claims to provide an efficient estimator for parameters on time-invariant variables (TIVs) in the FE model. None of the claims are correct. The FEVD estimator simply reproduces (identically) the linear FE (dummy variable) estimator then substitutes an inappropriate covariance matrix for the correct one. The consistency result follows from the fact that OLS in the FE model is consistent. The “efficiency” gains are illusory. The claim that the estimator provides an estimator for the coefficients on TIVs in an FE model is also incorrect. That part of the parameter vector remains unidentified. The “estimator” relies upon a strong assumption that turns the FE model into a type of random effects model.

Application of Generalized Orthogonal Polynomials to Parameter Estimation of Time-Invariant and Bilinear Systems

1987 ◽  
Vol 109 (1) ◽  
pp. 7-13 ◽  
Author(s):  
Maw-Ling Wang ◽  
Shwu-Yien Yang ◽  
Rong-Yeu Chang
Keyword(s):  
Parameter Estimation ◽  
Orthogonal Polynomial ◽  
Orthogonal Polynomials ◽  
Computational Algorithm ◽  
State Equation ◽  
Dynamic State ◽  
Generalized Orthogonal Polynomials ◽  
Generalized Orthogonal ◽  
Time Invariant ◽  
Estimation Of Time

Generalized orthogonal polynomials (GOP) which can represent all types of orthogonal polynomials and nonorthogonal Taylor series are first introduced to estimate the parameters of a dynamic state equation. The integration operation matrix (IOP) and the differentiation operation matrix (DOP) of the GOP are derived. The key idea of deriving IOP and DOP of these polynomials is that any orthogonal polynomial can be expressed by a power series and vice versa. By employing the IOP approach to the identification of time invariant systems, algorithms for computation which are effective, simple and straightforward compared to other orthogonal polynomial approximations can be obtained. The main advantage of using the differentiation operation matrix is that the parameter estimation can be carried out starting at an arbitrary time of interest. In addition, the computational algorithm is even simpler than that of the integral operation matrix. Illustrative examples for using IOP and DOP approaches are given. Very satisfactory results are obtained.

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.

Spatial Errors in Count Data Regressions

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Marinho Bertanha ◽  
Petra Moser
Keyword(s):  
Cross Section ◽  
Count Data ◽  
Spatial Dependence ◽  
Fixed Effects ◽  
Negative Binomial ◽  
Variance Estimator ◽  
Research Fields ◽  
Patent Counts ◽  
The Cross ◽  
Time Invariant

AbstractCount data regressions are an important tool for empirical analyses ranging from analyses of patent counts to measures of health and unemployment. Along with negative binomial, Poisson panel regressions are a preferred method of analysis because the Poisson conditional fixed effects maximum likelihood estimator (PCFE) and its sandwich variance estimator are consistent even if the data are not Poisson-distributed, or if the data are correlated over time. Analyses of counts may however also be affected by correlation in the cross-section. For example, patent counts or publications may increase across related research fields in response to common shocks. This paper shows that the PCFE and its sandwich variance estimator are consistent in the presence of such dependence in the cross-section – as long as spatial dependence is time-invariant. We develop a test for time-invariant spatial dependence and provide code in STATA and MATLAB to implement the test.

scholarly journals The Impact of Job Stress on Smoking and Quitting: Evidence from the HRS

2010 ◽  
Vol 10 (1) ◽  
Author(s):  
Padmaja Ayyagari ◽  
Jody L Sindelar
Keyword(s):  
Job Stress ◽  
Fixed Effects ◽  
Smoking Behavior ◽  
Occupational Characteristics ◽  
Secular Decline ◽  
Related Stress ◽  
Job Related Stress ◽  
Time Invariant ◽  
The Impact ◽  
Invariant Factors

Abstract Job-related stress might affect smoking behavior because smoking may relieve stress and stress can make individuals more present-focused. Alternatively, individuals may both self-select into stressful jobs and choose to smoke based on unobserved factors. We use data from the Health and Retirement Study to examine how job stress affects the probability that smokers quit and the number of cigarettes smoked for current smokers. To address the potential endogeneity of job stress based on time invariant factors, we include individual fixed effects, which control for factors such as ability to handle stress. Occupational fixed effects are also included to control for occupational characteristics other than stress; time dummies control for the secular decline in smoking rates. Using a sample of people who smoked in the previous wave, we find that job stress is positively related to continuing to smoke among recent smokers. The results indicate that the key impact of stress is on the extensive margin of smoking, as opposed to the number of cigarettes smoked.

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

2020 ◽  
Vol 20 (2) ◽  
pp. 253-275 ◽  
Author(s):  
Gregori Baetschmann ◽  
Alexander Ballantyne ◽  
Kevin E. Staub ◽  
Rainer Winkelmann
Keyword(s):  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Logit Models ◽  
Ordered Logit ◽  
The Social ◽  
The Standard Model ◽  
Dependent Variables ◽  
Using Data ◽  
New Community ◽  
Time Invariant

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.

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