American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
310
Symposium on Econometric Tools
Alan B.Krueger
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
1128
Nonparametric Density and Regression Estimation
JohnDiNardoJustin L.Tobias
We provide a nontechnical review of recent nonparametric methods for
estimating density and regression functions. The methods we describe
make it possible for a researcher to estimate a regression function or
density without having to specify in advance a particular--and hence
potentially misspecified functional form. We compare these methods to
more popular parametric alternatives (such as OLS), illustrate their use
in several applications, and demonstrate their flexibility with actual
data and generated-data experiments. We show that these methods are
intuitive and easily implemented, and in the appropriate context may
provide an attractive alternative to "simpler" parametric methods.
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
2942
Semiparametric Censored Regression Models
Kenneth Y.ChayJames L.Powell
When data are censored, ordinary least squares regression can provide
biased coefficient estimates. Maximum likelihood approaches to this
problem are valid only if the error distribution is correctly specified,
which can be problematic in practice. We review several semiparametric
estimators for the censored regression model that do not require
parameterization of the error distribution. These estimators are used to
examine changes in black-white earnings inequality during the 1960s
based on censored tax records. The results show that there was
significant earnings convergence among black and white men in the
American South after the passage of the 1964 Civil Rights Act.
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
4356
Binary Response Models: Logits, Probits and Semiparametrics
Joel L.HorowitzN. E.Savin
A binary-response model is a mean-regression model in which the
dependent variable takes only the values zero and one. This paper
describes and illustrates the estimation of logit and probit
binary-response models. The linear probability model is also discussed.
Reasons for not using this model in applied research are explained and
illustrated with data. Semiparametric and nonparametric models are also
described. In contrast to logit and probit models, semi- and
nonparametric models avoid the restrictive and unrealistic assumption
that the analyst knows the functional form of the relation between the
dependent variable and the explanatory variables.
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
5767
Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left
JerryHausman
The effect of mismeasured variables in the most straightforward
regression analysis with a single regressor variable leads to a least
squares estimate that is downward biased in magnitude toward zero. I
begin by reviewing classical issues involving mismeasured variables. I
then consider three recent developments for mismeasurement econometric
models. The first issue involves difficulties in using instrumental
variables. A second involves the consistent estimators that have
recently been developed for mismeasured nonlinear regression models.
Finally, I return to mismeasured left hand side variables, where I will
focus on issues in binary choice models and duration models.
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
6985
Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments
Joshua D.AngristAlan B.Krueger
Instrumental variables was first used in the 1920s to estimate supply
and demand elasticities and later to correct for measurement error in
single equation models. Recently, instrumental variables have been
widely used to reduce bias from omitted variables in estimates of causal
relationships. Intuitively, instrumental variables methods use only a
portion of the variability in key variables to estimate the
relationships of interest; if the instruments are valid, that portion is
unrelated to the omitted variables. We discuss the mechanics of
instrumental variables and the qualities that make for a good
instrument, devoting particular attention to instruments derived from
"natural experiments."
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
87100
Applications of Generalized Method of Moments Estimation
Jeffrey M.Wooldridge
I describe how the method of moments approach to estimation, including
the more recent generalized method of moments (GMM) theory, can be
applied to problems using cross section, time series, and panel data.
Method of moments estimators can be attractive because in many
circumstances they are robust to failures of auxiliary distributional
assumptions that are not needed to identify key parameters. I conclude
that while sophisticated GMM estimators are indispensable for
complicated estimation problems, it seems unlikely that GMM will provide
convincing improvements over ordinary least squares and two-stage least
squares--by far the most common method of moments estimators used in
econometrics--in settings faced most often by empirical researchers.
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
101115
Vector Autoregressions
James H.StockMark W.Watson
This paper critically reviews the use of vector autoregressions (VARs)
for four tasks: data description, forecasting, structural inference, and
policy analysis. The paper begins with a review of VAR analysis,
highlighting the differences between reduced-form VARs, recursive VARs
and structural VARs. A three variable VAR that includes the unemployment
rate, price inflation and the short term interest rate is used to show
how VAR methods are used for the four tasks. The paper concludes that
VARs have proven to be powerful and reliable tools for data description
and forecasting, but have been less useful for structural inference and
policy analysis.
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
117128
The New Econometrics of Structural Change: Dating Breaks in U.S. Labour Productivity
Bruce E.Hansen
We have seen the emergence of three major innovations in the
econometrics of structural change in the past fifteen years: (1) tests
for a structural break of unknown timing; (2) estimation of the timing
of a structural break; and (3) tests to distinguish unit roots from
broken time trends. These three innovations have dramatically altered
the face of applied time series econometrics. In this paper, we review
these three innovations, and illustrate their application through an
empirical assessment of U.S. labor productivity in the
manufacturing/durables sector.
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
129141
The Bootstrap and Multiple Imputations: Harnessing Increased Computing Power for Improved Statistical Tests
DavidBrownstoneRobertValletta
The bootstrap and multiple imputations are two techniques that can
enhance the accuracy of estimated confidence bands and critical values.
Although they are computationally intensive, relying on repeated
sampling from empirical data sets and associated estimates, modern
computing power enables their application in a wide and growing number
of econometric settings. We provide an intuitive overview of how to
apply these techniques, referring to existing theoretical literature and
various applied examples to illustrate both their possibilities and
their pitfalls.
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
143156
Quantile Regression
RogerKoenkerKevin F.Hallock
Quantile regression, as introduced by Koenker and Bassett (1978), may be
viewed as an extension of classical least squares estimation of
conditional mean models to the estimation of an ensemble of models for
several conditional quantile functions. The central special case is the
median regression estimator which minimizes a sum of absolute errors.
Other conditional quantile functions are estimated by minimizing an
asymmetrically weighted sum of absolute errors. Quantile regression
methods are illustrated with applications to models for CEO pay, food
expenditure, and infant birthweight.
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
157168
GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics
RobertEngle
ARCH and GARCH models have become important tools in the analysis of
time series data, particularly in financial applications. These models
are especially useful when the goal of the study is to analyze and
forecast volatility. This paper gives the motivation behind the simplest
GARCH model and illustrates its usefulness in examining portfolio risk.
Extensions are briefly discussed.
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
169182
Teaching Statistics and Econometrics to Undergraduates
William E.BeckerWilliam H.Greene
Traditionally econometrics and economics statistics have been taught in
the theory and proof, chalk and talk mode commonly found in the teaching
of mathematics. We advance the use of computer technology in the
teaching of quantitative methods to get students actively engaged in the
learning process. We also assert that the essential tasks for those who
teach these courses are to identify important issues that lend
themselves to quantitative analyses and then to help students develop an
understanding of the appropriate key concepts for those analyses.
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
183198
Free Labour for Costly Journals?
Theodore C.Bergstrom
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
199208
Cost-Benefit Analysis and the Classical Creed
JosephPersky
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
209217
Recommendations for Further Reading
BernardSaffran
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
219221
A Public Choice Explanation for the Budget Surplus: Comment
Russell S.Sobel
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
221221
The Political Economy of the Budget Surplus in the United States: Response
AlbertoAlesina
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
221222
Revolutions in Economic Thought
Kurt W.Rothschild
http://www.aeaweb.org/jep/contents/Fall2001.html
American Economic Association
Nashville, Tennessee
0895-3309
Journal of Economic Perspectives
15
4
Fall 2001
222222
No History of Ideas, Please, We're Economists: Response
MarkBlaug
http://www.aeaweb.org/jep/contents/Fall2001.html