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.

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 On the Fixed-Effects Vector Decomposition

2011 ◽  
Vol 19 (2) ◽  
pp. 123-134 ◽  
Author(s):  
Trevor Breusch ◽  
Michael B. Ward ◽  
Hoa Thi Minh Nguyen ◽  
Tom Kompas
Keyword(s):  
Monte Carlo ◽  
Instrumental Variables ◽  
Fixed Effects ◽  
Formal Analysis ◽  
Standard Errors ◽  
Panel Data Models ◽  
Time Varying ◽  
Vector Decomposition ◽  
Time Invariant ◽  
Instrumental Variables Approach

This paper analyzes the properties of the fixed-effects vector decomposition estimator, an emerging and popular technique for estimating time-invariant variables in panel data models with group effects. This estimator was initially motivated on heuristic grounds, and advocated on the strength of favorable Monte Carlo results, but with no formal analysis. We show that the three-stage procedure of this decomposition is equivalent to a standard instrumental variables approach, for a specific set of instruments. The instrumental variables representation facilitates the present formal analysis that finds: (1) The estimator reproduces exactly classical fixed-effects estimates for time-varying variables. (2) The standard errors recommended for this estimator are too small for both time-varying and time-invariant variables. (3) The estimator is inconsistent when the time-invariant variables are endogenous. (4) The reported sampling properties in the original Monte Carlo evidence do not account for presence of a group effect. (5) The decomposition estimator has higher risk than existing shrinkage approaches, unless the endogeneity problem is known to be small or no relevant instruments exist.

Of Fixed-Effects and Time-Invariant Variables

2011 ◽  
Vol 19 (2) ◽  
pp. 119-122 ◽  
Author(s):  
Nathaniel Beck
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Fixed Effects ◽  
Fixed Effect ◽  
Panel Data Model ◽  
Vector Decomposition ◽  
Time Invariant

What follows is a longish controversy (two critiques, a reply and two rejoinders) over the quality of the estimates and associated SEs provided by Plümper and Troeger's (2007) “fixed-effect vector decomposition” (FEVD) procedure; Plümper and Troeger (PT) will refer to that article and not any persons. My role is to lay out some issues that separate the authors rather than to adjudicate between them. As with many controversies, a bit of heat is generated along with some light. Readers care a bit less than the authors about what was said when, but they do care a lot about what appropriate method to use when a panel data model has both unit-specific intercepts and variables that are invariant over a unit. Thus, I also take it upon myself to discuss some things that I gleaned from this controversy; this discussion has a bit less heat than what follows, but of course readers should judge the evidence for themselves.

FEVD: Just IV or Just Mistaken?

2011 ◽  
Vol 19 (2) ◽  
pp. 165-169 ◽  
Author(s):  
Trevor Breusch ◽  
Michael B. Ward ◽  
Hoa Thi Minh Nguyen ◽  
Tom Kompas
Keyword(s):  
Monte Carlo ◽  
Instrumental Variables ◽  
Fixed Effects ◽  
Ordinary Least Squares ◽  
Research Papers ◽  
Knowing That ◽  
Vector Decomposition ◽  
Published Research ◽  
Choice Of Instruments ◽  
Special Case

Fixed effects vector decomposition (FEVD) is simply an instrumental variables (IV) estimator with a particular choice of instruments and a special case of the well-known Hausman-Taylor IV procedure. Plümper and Troeger (PT) now acknowledge this point and disown the three-stage procedure that previously defined FEVD. Their old recipe for SEs, which has regrettably been used in dozens of published research papers, produces dramatic overconfidence in the estimates. Again PT concede the point and now adopt the standard IV formula for SEs. Knowing that FEVD is an application of IV also has the benefit of focusing attention on the choice of instruments. Now it seems PT claim that the FEVD instruments are always the best choice, on the grounds that one cannot know whether any potential instrument is correlated with the unit effect. One could just as readily make the same specious claim about other estimators, such as ordinary least squares, and support it with similar Monte Carlo assumptions and evidence.

scholarly journals A closer look at fixed effects regression in SEM

2019 ◽  
Author(s):  
Henrik Kenneth Andersen
Keyword(s):  
Least Squares ◽  
Latent Variables ◽  
Latent Variable ◽  
Fixed Effects ◽  
Structural Equation ◽  
Panel Data Analysis ◽  
Ordinary Least Squares ◽  
Equation Modeling ◽  
Real World Data ◽  
Fixed Effects Regression

This article provides an in-depth look at the method of fixed-effects regression in the structural equation modeling (SEM) framework. It is meant for those who are less familiar with SEM but interested in panel data analysis as well as those familiar with SEM but new to fixed-effects regression. It demonstrates the decomposition of observed variables into within- and between-unit variance components using latent variables and gives an intuitive least squares-based explanation of latent variable estimation. The estimation of the substantive effect coefficients is shown analytically. The procedure is demonstrated on simulated as well as real-world data using the German Family Panel Survey (pairfam). The example analyses show the SEM results are identical to the conventional methods of pooled ordinary least squares on demeaned data. The supplementary materials provide the model code for use in replication and further study.

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.

Fixed-Effects Vector Decomposition: Properties, Reliability, and Instruments

2011 ◽  
Vol 19 (2) ◽  
pp. 147-164 ◽  
Author(s):  
Thomas Plümper ◽  
Vera E. Troeger
Keyword(s):  
Fixed Effects ◽  
Shrinkage Estimator ◽  
Time Varying ◽  
Finite Sample ◽  
Simultaneous Presence ◽  
Political Analysis ◽  
Vector Decomposition ◽  
Finite Sample Properties ◽  
Time Invariant ◽  
Decomposition Properties

This article reinforces our 2007 Political Analysis publication in demonstrating that the fixed-effects vector decomposition (FEVD) procedure outperforms any other estimator in estimating models that suffer from the simultaneous presence of time-varying variables correlated with unobserved unit effects and time-invariant variables. We compare the finite-sample properties of FEVD not only to the Hausman-Taylor estimator but also to the pretest estimator and the shrinkage estimator suggested by Breusch, Ward, Nguyen and Kompas (BWNK), and Greene in this symposium. Moreover, we correct the discussion of Greene and BWNK of FEVD's asymptotic and finite-sample properties.

The antecedents of corporate social and environmental responsibility discourse in Pakistan: Multiple theoretical perspectives

2019 ◽  
Author(s):  
Muhammad Farhan Basheer ◽  
Saqib Muneer ◽  
Muhammad Atif ◽  
Zubair Ahmad
Keyword(s):  
Institutional Theory ◽  
Least Squares ◽  
Agency Theory ◽  
Stakeholder Theory ◽  
Fixed Effects ◽  
Ordinary Least Squares ◽  
Environmental Responsibility ◽  
Legitimacy Theory ◽  
Discourse Practices ◽  
Corporate Social

The primary purpose of the study is to explore the antecedents of corporate social and environmental responsibilities discourse practices in Pakistan. The industry sensitivity, government shareholding, block holder ownership, print media coverage, environmental monitoring programs, and strategic posture are examined as antecedents of corporate social and environmental responsibility practices. A multidimensional theoretical perspective namely stakeholder theory (ST), institutional theory (IT), agency theory (PAT), and legitimacy theory (LT) is used to conceptualize the phenomena. All the four of perspective theories (positive accounting theory, legitimacy theory, stakeholder theory, and institutional theory) claim that there are ‘pressures’ that impact the organization. How much ‘pressures’ are recognized, managed or satisfied differs from one perspective of theory to the other. To estimate the data, this study uses three sets of panel data models, i.e., the pooled ordinary least squares model (POLS) or constant coefficients model, fixed effects (FEM or least squares dummy variable/LSDV model) and random-effects models. The final sample is comprising of 173 firms over eight years from 2011 to 2017. The firms listed in PSX are included in the sample. Overall the findings of the study have shown agreement with the proposed results. However, the study has provided more support to the institutional theory and stakeholder theory. Keywords: Corporate Social Responsibility, Stakeholders Theory, Agency Theory, Pakistan

scholarly journals Understanding, Choosing, and Unifying Multilevel and Fixed Effect Approaches

2020 ◽  
pp. 1-20
Author(s):  
Chad Hazlett ◽  
Leonard Wainstein
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Multilevel Models ◽  
Standard Errors ◽  
Point Estimate ◽  
Fe Model ◽  
Grouped Data ◽  
Coefficient Estimates ◽  
Political Science Education ◽  
Robust Standard Errors

Abstract When working with grouped data, investigators may choose between “fixed effects” models (FE) with specialized (e.g., cluster-robust) standard errors, or “multilevel models” (MLMs) employing “random effects.” We review the claims given in published works regarding this choice, then clarify how these approaches work and compare by showing that: (i) random effects employed in MLMs are simply “regularized” fixed effects; (ii) unmodified MLMs are consequently susceptible to bias—but there is a longstanding remedy; and (iii) the “default” MLM standard errors rely on narrow assumptions that can lead to undercoverage in many settings. Our review of over 100 papers using MLM in political science, education, and sociology show that these “known” concerns have been widely ignored in practice. We describe how to debias MLM’s coefficient estimates, and provide an option to more flexibly estimate their standard errors. Most illuminating, once MLMs are adjusted in these two ways the point estimate and standard error for the target coefficient are exactly equal to those of the analogous FE model with cluster-robust standard errors. For investigators working with observational data and who are interested only in inference on the target coefficient, either approach is equally appropriate and preferable to uncorrected MLM.

Sign in / Sign up

Export Citation Format

Share Document