Journal of Economic Perspectives
Vol. 15, No. 4, Fall 2001
Contents
Symposium on Econometric Tools
Alan B. Krueger 3-10
Nonparametric Density and Regression Estimation
John DiNardo and Justin L. Tobias 11-28
Semiparametric Censored Regression Models
Kenneth Y. Chay and James L. Powell 29-42
Binary Response Models: Logits, Probits and Semiparametrics
Joel L. Horowitz and N.E. Savin 43-56
Mismeasured Variables in Econometric Analysis: Problems
from the Right and Problems from the Left
Jerry Hausman 57-68
Instrumental Variables and the Search for Identification:
From Supply and Demand to Natural Experiments
Joshua D. Angrist and Alan B. Krueger 69-87
Applications of Generalized Method of Moments Estimation
Jeffrey M. Wooldridge 87-100
Vector Autoregressions
James H. Stock and Mark W. Watson 101-116
The New Econometrics of Structural Change: Dating Breaks
in U.S. Labor Productivity
Bruce E. Hansen 117-128
The Bootstrap and Multiple Imputations: Harnessing
Increased Computing Power for Improved Statistical Tests
David Brownstone and Robert Valletta 129-142
Quantile Regression
Roger Koenker and Kevin F. Hallock 143-156
GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics
Robert Engle 157-168
Teaching Statistics and Econometrics to Undergraduates
William E. Becker and William H. Greene 169-182
Free Labor for Costly Journals?
Theodore C. Bergstrom 183-198
Retrospectives: Cost-Benefit Analysis and the Classical
Creed
Joseph Persky 199-208
Features:
Recommendations for Further Reading 209-218
Comments: Russell S. Sobel, Alberto Alesina, Kurt W. Rothschild, J.E.
King,
and Mark Blaug 219-222
Notes 223-230
Symposium on Econometric Tools
Alan B. Krueger
No abstract available.
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Nonparametric Density and Regression Estimation
John DiNardo and Justin 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.
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Semiparametric Censored Regression Models
Kenneth Y. Chay and James 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.
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Binary Response Models: Logits, Probits and Semiparametrics
Joel L. Horowitz and N.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.
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Mismeasured Variables in Econometric Analysis: Problems from the Right
and Problems from the Left
Jerry Hausman
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.
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Instrumental Variables and the Search for Identification: From Supply
and Demand to Natural Experiments
Joshua D. Angrist and Alan 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."
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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.
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Vector Autoregressions
James H. Stock and Mark 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 conludes 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.
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The New Econometrics of Structural Change: Dating Breaks in U.S. Labor
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.
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The Bootstrap and Multiple Imputations: Harnessing Increased Computing
Power for Improved Statistical Tests
David Brownstone and Robert Valletta
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.
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Quantile Regression
Roger Koenker and Kevin 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 whch minimizes a sume 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.
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GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics
Robert Engle
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.
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Teaching Statistics and Econometrics to Undergraduates
William E. Becker and William 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.
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Free Labor for Costly Journals?
Theodore C. Bergstrom
No abstract available.
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Retrospectives: Cost-Benefit Analysis and the Classical Creed
Joseph Persky
No abstract available.
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Features (view in pdf format):
Recommendations
for Further Reading (AEA members only)
Comments: Russell
S. Sobel, Alberto Alesina, Kurt W. Rothschild, J.E. King,
and Mark Blaug (AEA Members only)
Notes
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