Panel Data Analysis

222 Pages·1992·4.733 MB·English

by  Baldev Raj, Badi H. Baltagi (auth.), Professor Baldev Raj, Professor Badi H. Baltagi (eds.)

Studies in Empirical Economics
Aman Ullah (Ed.)
Semi parametric and
Nonparametric Econometrics
1989. VII, 172 pp. Hard cover DM 120,
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Walter Kramer (Ed.)
Econometrics of Structural Change
1989. X, 128 pp. Hard cover DM 85,
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Hysteresis Effects in Economic Models
1990. VIII, 121 pp. Hard cover DM 90,
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Applied General Equilibrium
1991. VI, 153 pp. Hard cover DM 98,
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Baldev Raj and
Badi H. Baltagi (Eds.)
Panel Data
Analysis
With 6 Figures
Physica-Verlag Heidelberg
A Springer-Verlags Company
Editorial Board
Wolfgang Franz, University of Konstanz, FRG
Baldev Raj, Wilfrid Laurier University, Waterloo, Canada
Andreas Wörgötter, Institute for Advanced Studies, Vienna, Austria
Editors
Professor Baldev Raj
Wilfrid Laurier University
School of Business and Economics
Waterloo, Ontario N2L 3C5, Canada
Professor Badi H. Baltagi
Texas A&M University
Department of Economics
College Station
Texas 77843-4228, USA
First published in "Empirical Economics"
Yol. 17, No. I, 1992
ISBN 978-3-642-50129-6 ISBN 978-3-642-50127-2 (eBook)
DOI 10.1007/978-3-642-50127-2
CIP·Titelaufnahme der Deutschen Bibliothek
Panel data analysis / Baldev Raj and Badi H. Baltagi (ed.). -
Heidelberg : Physica·Yerl., 1992
(Studies in empirical economics)
NE: Raj, Baldev [Hrsg.]
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Preface
The idea of publishing a volume on panel data was proposed by Baldev
Raj at one of the meetings of the editors of Empirical Economics and it
was enthusiastically supported. The editor was asked to coordinate and
co-edit this volume. He then approached Badi Baltagi and asked him if he
would consider assisting in putting together the volume as a guest editor.
This collection is the final result of our collaborative effort.
The successful completion of this volume owes a great deal to the excel
lent papers submitted to us by the contributors and their cooperation
throughout the review process. The contribution of the reviewers by mak
ing available their expertise in this area along with their suggestions for
improvements, was equally invaluable. We express our sincere thanks to
both the contributors and the referees for their assistance throughout the
review process. The cooperation of the contributors in keeping their final
papers within the set page limit is particularly appreciated. We sincerely
hope that in reducing the size of some of the papers the exposition and
proper documentation of the results have not been compromised. A dou
ble blind review process has been followed for the papers in this collec
tion. We express regret to some contributors whose papers could not be
published for a variety of reasons.
It gives us great pleasure to express our sincere thanks for the research
support of our respective academic institutions. Badi Baltagi received fi
nancial support from the Advanced Research Program of the Texas
Higher Education Board while Baldev Raj received support from the Ac
ademic Development Fund both as the co-editor of Empirical Economics
and one one-term course relief for this special research project.
A number of individuals whose encouragement, support and sugges
tions made the completion of this volume possible are: Wolfgang Franz,
Frank W. Millerd, Werner A. Muller, Alex J. Murray, Barry McPherson,
Andreas Worgotter and several other of the editors' colleagues. Anne
Marie Arndt, Secretary, Editorial Office at Wilfrid Laurier University pro
vided efficient assistance with the review process of the papers in this col
lection.
Baldev Raj Badi H. Baltagi
Wilfrid Laurier University Texas A&M University
Waterloo, Ontario, Canada College Station, Texas, USA
Contents
Preface
Introduction and Overview
Data and Modelling Strategies
Can Cohort Data Be Treated as Genuine Panel Data?
M. Verbeek and T. Nijman ............... . II
Estimating Time-Dependent Means in Dynamic Models for Cross-Sections
of Time Series
P. Marshall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 27
Using Panel Data to Estimate Risk Effects in Seemingly Unrelated Pro
duction Functions
G. H. Wan. W. E. Griffiths and J. R. Anderson ............... 37
II Theoretical Results
The Bias of some Estimators for Panel Data Models With Measurement
Errors
E. BifJrn ................................ . 53
Models for Which the MLE and the Conditional MLE Coincide
Ch. Cornwell and P. Schmidt .................... . 69
Exact Equivalence of Instrumental Variable Estimators in an Error Com
ponent Structural System
N. S. Revankar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 79
A Survey of Recent Theoretical Developments in the Econometrics of
Panel Data
B. H. Baltagi and B. Raj . . . . . . . . . . . . . . . . . . . . . . . . . . .. 87
III Applications
a) Models of Dividend Behavior, Long-Distance Calling and Freestand
ing Health Centres
Estimation and Specification Analysis of Models of Dividend Behavior
Based on Censored Panel Data
B. S. Kim and G. S. Maddala ......................... 113
Econometric Modelling of Canadian Long Distance Calling: A Compari-
son of Aggregate Time Series Versus Point-to-Point Panel Data Ap
proaches
T. W. Appelbe. Ch. R. Dineen. D. L. Solvason and Ch. Hsiao .... 127
A Panel Data Analysis of Productive Efficiency in Freestanding Health
Clinics
S. C. Johnson and K. Lahiri 143
b) Applied Labor Studies
Heterogeneous Labor and the Dynamics of Aggregate Labor Demand:
Some Estimations Using Panel Data
G. Bresson, F. Kramarz, and P. Sevestre ......... . 155
How Fragile are Male Labor Supply Function Estimates?
K. Smith Conway and T. J. Kniesner ........... . 171
c) Studies of Unemployment Compensation and Direct Foreign Invest
ment
Unemployment Compensation and Episodes of Nonemployment
R. M. Gritz and T. E. MaCurdy ...... ,............ 185
A Random Coefficient Simultaneous Equation System with an Applica-
tion to Direct Foreign Investment by French Firms
P. Balestra and S. Negassi ........................... 207
Introduction and Overview
By B. Raj 1 and B. H. Baltagi 2
Abstract: An overview of the organization and contents of the papers in this collection is provided.
This collection of fourteen papers on the econometrics of panel data aims to provide the readers
with a select group of contemporary theoretical and applied research topics in panel data analysis.
1 Motivation
In the past three decades considerable research has been carried out on theoretical
issues and applied topics related to the econometrics of panel data. The easy
availability of panel data in recent years has contributed to increased interest in
modelling, estimation, hypothesis testing, model evaluation, forecasting, policy
analysis and related econometric issues in panel data analysis.
An inventory of longitudinal data has been compiled by Ashenfelter and
Solon (1982) and Borus (1982). Introductory discussion on panel data
econometrics can be found in virtually every econometrics text book while more
comprehensive overviews are provided by Hsiao (1986) and Chamberlain (1984).
A number of well known commerically available computer software packages
such as RATS 3, SAS/STAT4, SHAZAMs, LIMDEp6, and the TSCS program
for TSP 7 are available to implement several of the popular econometric methods
for analyzing panel data.
Baldev Raj, Wilfrid Laurier University, School of Business and Economics, Waterloo, Ontario,
Canada N2L 3C5, Canada.
2 Badi H. Baltagi, Texas A&M University, College Station, Texas, USA.
RATS.YAR Econometrics. p.o. Box 1818, Evanston, Illinois 60204-1818. (312-864-8772).
SAS/STAT. SAS Institute Incorporated, P.O. Box 8000, SAS Circle, Cary, N.C. 27511-8000.
(919-467 -8000).
5 SHAZAM. Kenneth J. White, Department of Economics, University of British Columbia,
Vancouver, B.C., Canada, V6T 1Y 2. (604-228-5062).
6 LIMDEP. William H. Greene, Stern Graduate School of Business, New York University,
100 Trinity Place, New York, N.Y. 10006.
7 TSP. Bronwyn H. Hall, TSP User's Manuel. TSP International, 928 Mears Court, Stanford,
California 94305 (415-326-1927).
2 B. Raj and B. H. Baltagi
There are a number of advantages to using panel data in applied research; for
example, its use provides the researcher with a large number of observations
leading to improved efficiency of econometric estimates. Another advantage is
that the researcher is able to undertake in depth analysis of complex economic
hypotheses by controlling for influences corresponding to both individual and
time heterogeneities. These and other advantages are discussed in more detail by
Chamberlain (1984), Hsiao (1985, 1986), Maddala (1987 a, b), Klevmarken (1989),
and in the survey paper of BaJtagi and Raj in this collection.
1.1 Relationship to Existing Literature
The literature on the econometrics of panel data continues to proliferate and
research interests among econometricians cover a wide variety of theoretical and
applied topics. A bibliographical search of the Economic Literature Index which
covers some 300 journals was conducted by the editors and it yielded some 121
listings over the period 1969 - 1986. To accommodate increased research interest
and to provide a thematic focus to the diverse interests of researchers, a large
number of monographs have been written on this topic; e.g., see Heckman and
Singer (1985), Hsiao (1986), Dielman (1989) and Dormont (1989). A number of
conferences have been organized since 1977 and several proceedings volumes bas
ed on a select group of papers presented at these conferences have been published;
e.g., see Mazodier (1978), Atkinson and Cowell (1983), Klevmarken (1989) and
Hartog, Ridder and Theeuwes (1990). A number of journals including Annales
de I'INSEE, The European Economic Review, Journal of Econometrics and
Transportation Research have devoted a special issue each to a specific research
topic dealing with panel data issues. These monographs and other references to
the literature have been conveniently listed in the survey article by Baltagi and Raj
in this collection.
2 Organization of the Paper
The articles in this collection have been organized into three sections viz, i) Data
and Modelling Strategies, ii) Theoretical Results and iii) Applications. There are
a total of seven papers in the third section which cover a variety of applications
in various subdisciplines in economics; the papers have been divided into three
sub-sections; (a) Modelling of Dividend Behavior in the United States,
Econometrics of Long-Distance Calling in Canada, and Productive Efficiency of
Free Standing Health Clinics in the United States, (b) Applied Labor Studies and
(c) Studies of Unemployment Compensation and Direct Foreign Investment.
Introduction and Overview 3
A brief account of the articles in these sections is given below to provide the
readers with an overview of their focus and contents.
3 Data and Modelling Strategies: An Overview
The paper by Marno Verbeek and Theo Nijman addresses the question of
whether cohort data can conveniently by treated as genuine panel data by applied
researchers. In many situations instead of genuine panel data, a series of indepen
dent cross sections are available over time. It is tempting to treat a group of
"similar individuals" as cohorts and treat averages within these cohorts as obser
vations in (synthetic) panel data, see Deaton (1985). This aggregate data on
cohort averages can be used to formulate the fixed effects model in the
econometric analysis of behaviour models. Modelling strategists have argued that
cohort data invariably will involve the errors-in-variables problem as cohorts are
error-ridden measurements of the true cohort values in the population. This issue,
however, has largely been ignored by applied researchers. Verbeek and Nijman's
paper asks the question: how reliable can estimates be when the analyst might
decide to ignore the errors-in-variables problem (as has been done in several prac
tical situations documented by them) on the premise that the use of aggregate
data can mitigate the errors-in-variables problem to a large extent? Their results
show that, in practice, fairly large cohort size (100 to 200 individuals) is required
before the common practice of ignoring the errors-in-variables issue in cohort
data analysis can be considered valid. In the absence of a fairly large cohort size,
estimates obtained from the model will involve a large bias, making the applied
analysis suspect for policy use.
Pablo Marshall's paper focuses on dynamic modelling strategies based on the
ideas of structural time series modelling. A common approach to dynamic
modelling for panel data in the literature (see Anderson and Hsiao 1981, 1982 and
Hsiao 1986) has been to introduce lag-dependent variables in the model and/or
autoregressive processes for the components corresponding to time-specific and
time-unit specific effects within the error-component framework. A novelty of the
modelling strategy used by Marshall is that it allows for both the time specific and
time-unit specific effects to evolve smoothly over time according to a random
walk combined with noise processes. This type of formulation may be justified
on the principle of parsimony in modelling the observed persistence in economic
behavioral response. The illustrated example provided in this paper shows that
this modelling strategy has merit.
The final paper is this section by Guang H. Wan, William E. Griffiths and
Jock R. Anderson is concerned with modelling and estimating risk effects from
panel data in the seemingly unrelated regressions (SUR) model using a production
functions framework. Their modelling strategy begins with specifying both time-