Fixed Effects Vector Decomposition: Reply

2011 ◽  
Author(s):  
Thomas Plümper ◽  
Vera E. Troeger
Keyword(s):  
Fixed Effects ◽  
Vector Decomposition

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.

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.

Das Sozio-Oekonomische Panel als Datenbasis für die Arbeits- und Organisationspsychologie

2018 ◽  
Vol 62 (3) ◽  
pp. 111-125 ◽  
Author(s):  
Marco Giesselmann ◽  
Mila Staneva ◽  
Jürgen Schupp ◽  
David Richter
Keyword(s):  
Fixed Effects

Zusammenfassung. Der Beitrag zeigt die Analysepotentiale der repräsentativen Mikrodaten des Sozio-oekonomischen Panels (SOEP) für die Arbeits- und Organisationspsychologie (A/O-Psychologie) auf. Dabei werden allgemeine Charakteristika von Stichprobe und Erhebung des SOEP vorgestellt, sowie Konstrukte mit besonderer Relevanz für die Psychologie eingeführt. Zudem diskutieren wir Analysemethoden für Paneldaten, mit denen sich die Potentiale des SOEP realisieren lassen. Neben den Möglichkeiten des SOEP für Stabilitäts- und Verlaufsanalysen stellen wir die Potentiale längsschnittlicher Daten für kausale Analysen heraus. Dabei erläutern wir insbesondere die Analyselogik längsschnittlicher Fixed Effects Modellierungen und vergleichen diese mit weiteren längsschnittlichen Analyseverfahren. Wir argumentieren, dass bei Anwendung akkurater Methoden Teilaspekte der experimentellen Analyselogik auf Grundlage längsschnittlicher Surveydaten angenähert werden können. Folglich stellen die Daten des SOEP immer dann eine wertvolle Ressource für die A/O-Psychologie dar, wenn a) unabhängige Merkmale aus ethischen oder praktischen Gründen nicht systematisch manipuliert werden können, b) die Kernbefunde experimenteller Primärstudien auf Grundlage eines repräsentativen Samples repliziert werden sollen oder c) Interesse am langfristigen Verlauf eines Indikators besteht.

Dampak Kebijakan Moneter Pada Output Di Negara-Negara ASEAN Tahun 1980-2014

2017 ◽  
Vol 4 (1) ◽  
pp. 58-70
Author(s):  
Nur Widiastuti
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Fixed Effects ◽  
Empirical Studies ◽  
Negative Relationship ◽  
Least Square ◽  
Fixed Effects Model ◽  
State Data ◽  
Ordinary Least Square ◽  
The Impact

The Impact of monetary Policy on Ouput is an ambiguous. The results of previous empirical studies indicate that the impact can be a positive or negative relationship. The purpose of this study is to investigate the impact of monetary policy on Output more detail. The variables to estimatate monetery poicy are used state and board interest rate andrate. This research is conducted by Ordinary Least Square or Instrumental Variabel, method for 5 countries ASEAN. The state data are estimated for the period of 1980 – 2014. Based on the results, it can be concluded that the impact of monetary policy on Output shown are varied.Keyword: Monetary Policy, Output, Panel Data, Fixed Effects Model

Spatial regularities between non-immigrant and immigrant numbers in Canada

2014 ◽  
Vol 11 (1) ◽  
pp. 90-100
Author(s):  
Yigit Aydede
Keyword(s):  
Fixed Effects ◽  
Spatial Scales ◽  
National Scale ◽  
Pooled Data

The present study intends to reveal spatial regularities between non-immigrant and immigrant numbers in two different ways. First, it questions the existence of those regularities when spatial scales get finer. Second, it uses pooled data over four population censuses covering the period from 1991 to 2006, which enabled us to apply appropriate techniques to remove those unobserved fixed effects so that the estimations would accurately identify the linkage between local immigrant and non-immigrant numbers. The results provide evidence about the existence of negative spatial regularities between non-immigrant and immigrant numbers in Canada at national scale.

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