Handbook of Applied Economic Statistics

636 Pages·1998·12.86 MB·English

by  O. Thomas Holland

HANDBOOK OF APPLIED
ECONOMIC STATISTICS
edited by
Aman Ullah
University of California
Riverside, California
David E. A. Giles
University of Victoria
Victoria, British Columbia, Canada
MARCEI
MARCELD EKKEIRN,C . NEWY ORK BASEL HONGK ONG
DEKKER
Library of Congress Cataloging-in-Publication Data
Handbook of applied economic statistics/edited by Aman Ullah, David E. A.
Giles .
p. cm.-(Statistics, textbooks, and monographs; v. 155)
Includes bibliographical references and index.
ISBN 0-8247-0 129-1
1. Economics-Statistical methods. I. Ullah, Aman. 11. Giles, David E.
A. 111. Series.
HB137.H36 1998
330 ' .O 1' 5 195-dc2 1 97-47379
CIP
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Preface
Many applied subjects, including economic statistics, deal with the collection of
data, measurement of variables, and the statistical analysis of key relationships and
hypotheses. The attempts to analyze economic data go back to the late eighteenth
century, when the first examinations of the wages of the poor were done in the United
Kingdom, followed by the the mid-nineteenth century research by Engle on food
expenditure and income (or total expenditure). These investigations led to the early
twentieth-century growth of empirical studies on demand, production, and cost func-
tions, price determination, and macroeconomic models. During this period the sta-
tistical theory was developed through the seminal works of Legendre, Gauss, and
Pearson. Finally, the works of Fisher and Neyman and Pearson laid the foundations
of modern statistical inference in the form of classical estimation theory and hypoth-
esis testing. These developments in statistical theory, along with the growth of data
collections and economic theory, generated a demand for more rigorous research in
the metholodogy of economic data analysis and the establishment of the International
Statistical Institute and the Econometric Society.
The post-World War I1 period saw significant advances in statistical science,
and the transformation of economic statistics into a broader subject: econometrics,
which is the application of mathematical and statistical methods to the analysis of
economic data. During the last four decades, significant works have appeared on
econometric techniques of estimation and hypothesis testing, leading to the appli-
cation of econometrics not only in economics but also in sociology, psychology, his-
tory, political science, and medicine, among others. We also witnessed major de-
velopments in the literature associated with the research at the interface between
econometrics and statistics, especially in the areas of censored models, panel (lon-
gitudinal) data models, the analysis of nonstationary time series, cointegration and
volatility, and finite sample and asymptotic theories, among others. These common
grounds are of considerable importance for researchers, practitioners, and students
of both of these disciplines and are of direct interest to those working in other areas
of applied statistics.
...
111
iv PREFACE
The most important objective of this volume is to cover the developments in
both applied economics statistics and the econometric techniques of estimation and
hypothesis testing. It is in this respect that our book differs from other publications
in which the emphasis is on econometric methodology. With the above purpose in
view, we deal with the material that is of direct interest to researchers, practitioners,
and graduate students in many applied fields, especially economics and statistics. It
covers reasonably comprehensive and up-to-date reviews of developments in various
aspects of economic statistics and econometrics, and also contains papers with new
results and scopes for future research. The objective behind all this was to produce a
handbook that could be used by professionals in economics, sociology, econometrics,
and statistics, and by teachers of graduate courses.
The Handbook consists of eighteen chapters that can be broadly classified into
the following three groups:
1. Applied Economic Statistics
2. Econometric Methodology and Data Issues
3. Model Specification and Simulation
Chapters 1-5 belong to Part 1 and they are applied papers dealing with impor-
tant statistical issues in development economics and microeconomics. The chapter
by Davies, Green, and Paarsch reviews the literature on using economics statistics,
such as income inequality and other aggregate poverty indices, and they make a
strong case for the use of disaggregated dominance criteria to make social welfare
comparisons. They also discuss some statistical issues related to parametric and non-
parametric inference concerning Lorenz Curves, with reference to stochastic dom-
inance. In contrast, Kramer’s chapter develops two ways of looking at inequality
measurement: the first, a preordering based on majorization defined over income
vectors, and the second, an axiomatic-based approach in which axioms are defined
over inequality measurements. The chapter also includes the empirical application
of inequality measurement primarily focused on dealing with the fact that data is mu-
ally grouped by quantile. Ravallion’s chapter addresses an important issue of persis-
tence in the geography of poverty. It proposes a methodology for empirically testing
the validity of two competing explanations of poverty: an individualistic model and
a geographic model. His proposed approach contributes to our understanding of the
determinants of poverty and provides information for policymakers regarding which
policy interventions are likely to be most effective for its alleviation. The chapter by
Deolalikar explores another dimension of the poverty issue in developing countries,
that is, whether decreased spending on government health programs will reduce the
demand for public health services by the poor and hence will adversely affect the
health status of the poor. This question is analyzed using data from research con-
ducted in Indonesia. The chapter also attempts to address the shortcomings of the
existing literature. Finally, the chapter on mobility by Maasoumi reviews two differ-
ent, but related, approaches to testing for income mobility and shows that the two
PREFACE v
ways converge to the same ordering of states. The relationship between this ordering
and the partial ordering given by Lorenz dominance is shown.
Chapters 6-10 and 15-17 deal with econometric methodologies related to dif-
ferent kinds of data used in empirical research. Chapter 6 by Russell, Breunig and
Chiu is perhaps the first comprehensive treatment of the problem of aggregation as
it relates to empirical estimation of aggregate relationships. It is well known that the
analysis of individual behavior based on aggregate data is justified if the estimated
aggregate relationships can be consistently disaggregated to the individual relation-
ships and vice versa. Most empirical studies have ignored this problem; those that
have not are reviewed in this chapter. Anselin and Bera’s chapter details another
data problem ignored in the econometric analysis of regression model: the problem
of spatial autocorrelation and the correlation in cross-sectional data. This chapter
reviews the methodological issues related to the treatment of spatial dependence in
linear models. Another data issue often ignored in empirical development economics
and labor economics is related to the fact that most of the survey data is based on
complex sampling from a finite population, such as stratified, cluster, and systematic
sampling. However, the econometric analysis is carried out under the assumption of
random sampling from an infinite population. The chapter by Ullah and Breunig
reviews the literature on complex sampling and indicates that the effect of misspec-
ifying or ignoring true sampling schemes on the econometric inference can be quite
serious.
Panel data is the multiple time series observations on the same set of cross-
sectional survey units (e.g., households). Baltagi’s chapter reviews the extensive ex-
isting literature on econometric inference in linear and nonlinear parametric panel
data models. In a related chapter, Ullah and Roy develop the nonparametric kernel
estimation of panel data models without assuming their functional forms. The chapter
by Golan, Judge, and Miller proposes a maximum-entropy approach to the estimation
of simultaneous equations models when the economic data is partially incomplete.
In Chapter 15 Terasvirta looks into the modeling of time series data that exhibit non-
linear relationships due to discrete or smooth transitions and to regimes’ switching.
He proposes and develops a smooth transition regression analysis for such situations.
Finally, the chapter by Franses surveys econometric issues concerning seasonality in
economic time series data due to weather or other institutional factors. He discusses
the statistical models that can describe forecasts of economic time series with sea-
sonal variations encountered in macroeconomics, marketing and finance.
Chapters 12 and 18 are related to the simulation procedures and 11, 13, and
14 to the model and selection procedures in econometrics. The chapter by DeBene-
dictis and Giles surveys the diagnostic tests for the model misspecifications that can
have serious consequences on the sampling properties of both estimators and tests.
In a related chapter, Hadi and Son look into diagnostic procedures for revealing
outliers (influential observations) in the data which, if present, could also affect the
estimators and tests. They also propose a methodology of estimating linear models
vi PREFACE
with outliers, which is an alternative to computer-intensive quantile estimation tech-
niques used in practice. Next, the chapter by Dufour and Torrks systematically de-
velops the general theory of union-intersection and sample split methods in various
specification testing problems in econometrics. They apply their results for testing
problems in the SURE model and a model with MA(1) errors. In contrast to the an-
alytical approaches of specification testing, the chapter by Veal1 provides a survey
of bootstrap simulation procedures that is especially useful in small samples. The
book concludes with the chapter by Pagan, in which he debates about the calibra-
tion methodology of estimation and specification analysis. Several thought-provoking
questions are raised and discussed.
In summary, this volume brings together survey material and new methodolog-
ical results which are vitally important to modern developments in applied economic
statistics and econometrics. The emphasis is on data problems, methodological is-
sues, and inferential techniques that arise in practice in a wide range of situations
that are frequently encountered by researchers in many related disciplines. Accord-
ingly, the contents of the book should have wide appeal and application. We are
very pleased with the end product and would like to thank all the authors for their
contributions, and for their cooperation during the preparation of this volume. We
are also most grateful to Benicia Chatman, University of California at Riverside, for
the efficient assistance that she has provided, and to the editorial and production
staff at Marcel Dekker, especially Maria Allegra and Lia Pelosi, for their patience,
guidance, and expertise.
Aman Ullah
David E. A. Giles
Contents
...
Preface LLL
Contributors LX
Part I Applied Economic Statistics
I. Economic Statistics and Social Welfare Comparisons: A Review I
James B. Davies, David A. Green, and Harry J. Paarsch
2. Measurement of Inequality 39
Walter Krarner
3. Poor Areas 63
Martin Ravallion
4. The Demand for Health Services in a Developing Country:
The Role of Prices, Service Quality, and Reporting of Illnesses 93
Anil B. Deolalikar
5. On Mobility I19
Esfandiar Maasoumi
Part 2 Econometric Methodology and Data Issues
6. Aggregation and Econometric Analysis of Demand and Supply I77
R. Robert Russell, Robert I! Breunig, and Chia-Hui Chiu
7. Spatial Dependence in Linear Regression Models
with an Introduction to Spatial Econometrics 23 7
Luc Anselin and Anil K. Bera
8. Panel Data Methods 29 I
Badi H. Baltagi
vii
viii CONTENTS
9. Econometric Analysis in Complex Surveys 325
Aman Ullah and Robert I! Breunig
10. Information Recovery in Simultaneous-Equations’
Statistical Models 365
Amos Golan, George Judge, and Douglas Miller
I I. Diagnostic Testing in Econometrics: Variable Addition, RESET,
and Fourier Approximations 383
Linda DeBenedictis and David E. A. Giles
12. Applications of the Bootstrap in Econometrics and
Economic Statistics 419
Michael R. Veal1
13. Detection of Unusual Observations in Regression and
Multivariate Data 44 I
Ali S. Hadi and Mun S. Son
14. Union-Intersection and Sample-Split Methods in Econometrics
with Applications to MA and SURE Models 465
Jean-Marie Dufour and Olivier Torrb
Part 3 Model Specification and Simulation
I5 . Modeling Economic Relationships with Smooth Transition
Regressions 507
Timo Terasvirta
16. Modeling Seasonality in Economic Time Series 553
Philip Ham Frames
I7 . Nonparametric and Semiparametric Econometrics of Panel Data 579
Aman Ullah and Nilanjana Roy
18. On Calibration 605
Adrian Rodney Pagan
Index 61 9
Contributors
Luc Anselin, Ph.D. Research Professor, Regional Research Institute and Depart-
ment of Economics, West Virginia University, Morgantown, West Virginia
Badi H. Baltagi, Ph.D. Professor, Department of Economics, Texas A&M University,
College Station, Texas
Anil K. Bera, Ph.D. Professor, Department of Economics, University of Illinois,
Champaign, Illinois
Robert V. Breunig Graduate Student, Department of Economics, University of Cal-
ifornia at Riverside, Riverside, California
Chia-Hui Chiu Graduate Student, Department of Economics, University of Cali-
fornia at Riverside, Riverside, California
James B. Davies, Ph.D. Professor and Chair, Department of Economics, University
of Western Ontario, London, Ontario, Canada
Linda F. DeBenedictis, M.A. Senior Policy Analyst, Policy and Research Division,
Ministry of Human Resources, Victoria, British Columbia, Canada
Anil B. Deolalikar, Ph.D. Professor, Department of Economics, University of Wash-
ington, Seattle, Washington
Jean-Marie Dufour, Ph.D. Professor, C.R.D.E. and Department of Economic Sci-
ences, University of Montreal, Montreal, Quebec, Canada
Philip Hans Franses, Ph.D. Associate Professor, Department of Econometrics, Eras-
mus University Rotterdam, Rotterdam, The Netherlands
ix