Advanced Applied Econometrics
I taught
this course twice at the OECD economics
department, once in November and December 2010, and once more in January
2011.
Background and course objectives
The OECD
economics department identified a training need in econometrics. There was
demand for training at an advanced level, but focused on researchers doing
applied work, illustrating tools with recent, well published applied studies in
the literature. In terms of topics, there is strong interest in
micro-econometric techniques, like difference-in-differences and (dynamic)
panel data models. In terms of teaching methods, there is demand for a
combination of lectures, interactive tutorials and some guidance to the use of
statistical software, over a longer period of time (possibly even multiple
courses).
This
course was designed with these training needs in mind, and is loosely is
modeled after an advanced Ph.D. level course in applied
econometrics, which I designed, coordinated and co-taught at the graduate
program in economics of the Universitat
Pompeu Fabra. It is an
advanced course, meaning that I assume that participants have already followed
standard graduate or advanced undergraduate courses in econometrics, and have a
working knowledge of statistical software. The course is taught at high speed,
starting from the basics, but advancing quickly to the research frontier. At
the same time, this is an applied course, geared towards researchers doing
applied work on a regular basis. The course’s aim is not to give a survey
of econometric theory. Rather, we discuss a range of techniques and empirical
approaches, which are regularly and successfully used in applied work, and
discuss applications, practical implementation, and potential problems and pitfalls.
Course content and teaching philosophy
The course
includes six topics that I consider as essential for almost all applied
researchers using micro-econometric methods. In this first part of the course,
participants receive training in the modern way of thinking about applied
micro-econometrics, the so called experimental approach, which borrows a lot of
terminology and its way of thinking about identification from laboratory
experiments. In addition, we discuss particular issues that arise in panel datasets,
in particular the estimation of dynamic panel data models. Finally, we discuss
cases, in which standard errors do not give an accurate reflection of the
uncertainty associated with the point estimates, and ways to correct for these
problems. If further, more specialized, needs arise or are revealed during the
first part of the course, additional topics may be added as a second part.
To ensure
effective learning, each topic consists of a lecture and an interactive
workshop. The lectures start with an application of the technique under study.
Using this application, typically either a classic or a recent contribution to
the literature, we discuss what the methodological problem is, and why it is
important in practice. Then, I explain how to use the method in practice, how
to compare it to other methods (e.g. OLS), how to use statistical software to
implement it, how to best present the results, and what problems to be aware
of. If necessary, we discuss additional applications to illustrate particular strengths
or weaknesses of the method. The workshops offer the opportunity to practice
these skills using guided exercises (problem sets) or replicating existing
studies. I am available to answer questions and offer suggestions related to
the exercise.
In addition
to lectures and workshops, I have individual meetings with the participants.
These meetings are meant to offer OECD researchers the opportunity to consult
with me on issues or problems related to their own work, whether related to the
course material or not. Individual meetings serve a dual purpose. First, they
offer direct feedback for the participants on their work for the OECD. Second,
they give me a better insight into what methods and techniques are of interest
to OECD researchers, allowing me to better focus the content of the lectures,
particularly possible additional topics in part II.
As a
statistical software package, Stata has become the
standard for academic research in economics. Other packages, like eViews and SAS, are good alternatives, but since I am much
less familiar with these, I can offer less support. If necessary for the
workshops, I make code available in Stata format.
Data are provided as text files, which are readable in all packages.
Outline
1. Regression Analysis and the Experimental
Approach
·
Introduction
to the course
·
Review
of regression analysis
·
Regression
and causality
·
Experimental
approach
·
The
‘basement’ of applied research
Joshua Angrist and Jörn-Steffen Pischke (2009), Mostly Harmless Econometrics:
An Empiricist’s Companion, chapters 1-3
Joshua D. Angrist and Alan B. Krueger (1999). Empirical strategies in
labor economics, in: O. Ashenfelter & D. Card
(ed.), Handbook of Labor Economics,
edition 1, volume 3, chapter 23, pages 1277-1366
Peter E. Kennedy
(2002). Sinning
in the Basement: What Are the Rules? The Ten Commandments of Applied
Econometrics, Journal of Economic
Surveys, 16(4)
Journal of Economic Perspectives, 24(2), Spring 2010
Joshua
D. Angrist and Steve Pischke
(2010).
The
Credibility Revolution in Empirical Economics: How Better Research Design is
Taking the Con out of Econometrics.
Edward Leamer. Tantalus on the Road to Asymptopia.
Michael Keane. A Structural Perspective on the Experimentalist School.
Christopher Sims. But Economics
Is Not an Experimental Science.
Aviv
Nevo and Michael Whinston. Taking the
Dogma out of Econometrics: Structural Modeling and Credible Inference.
James Stock. The Other
Transformation in Econometric Practice: Robust Tools for Inference.
Journal of Economic Literature, 48(2), June 2010
Angus Deaton. Instruments,
Randomization, and Learning about Development.
James J. Heckman. Building Bridges between Structural and Program Evaluation
Approaches to Evaluating Policy.
Guido W. Imbens. Better LATE
Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009).
Journal of Economic Perspectives, 25(3), Summer 2011
(Symposium: Field Experiments)
John A. List. Why Economists
Should Conduct Field Experiments and 14 Tips for Pulling One Off.
Jens
Ludwig, Jeffrey R. Kling and Sendhil Mullainathan. Mechanism Experiments and Policy Evaluations.
David
Card, Stefano DellaVigna and Ulrike Malmendier. The Role of Theory in Field Experiments.
Oriana Bandiera, Iwan Barankay and Imran Rasul. Field Experiments with Firms.
2. Difference-in-Differences
·
Effects
of the minimum wage on employment
·
Difference-in-Differences
(DD)
·
DD
and regression
·
DD
with multiple groups and fixed effects (FE)
·
Review
of FE regression
·
Presenting
results
·
What
is the source of identifying variation?
David
Card and Alan B. Krueger (1994). Minimum Wages
and Employment: A Case Study of the Fast-Food Industry in New Jersey and
Pennsylvania, American Economic
Review, 84(4), pp.772-93
David Card (1992).
Using Regional
Variation to Measure the Effect of the Federal Minimum Wage, Industrial and Labor Relations Review,
46, pp.22-37
Joshua Angrist and Jörn-Steffen Pischke (2009), Mostly Harmless Econometrics:
An Empiricist’s Companion, section
5.1-5.2
Guido
W. Imbens and Jeffrey M. Wooldridge (2009). Recent
Developments in the Econometrics of Program Evaluation, Journal of Economic Literature, 47(1),
pp.5-86
Workshop #1 on DD - data
3. Experiments in Economics and
Instrumental Variables
·
A
typical field experiment in development economics: cost sharing
·
A
field experiment in the US: labor market discrimination
·
An
experiment in macroeconomics: fiscal stimulus
·
Natural
experiments
·
Review
of instrumental variables (IV) regression
Jessica
Cohen and Pascaline Dupas
(2010).
Free Distribution or Cost-Sharing? Evidence
from a Randomized Malaria Prevention Experiment.
Quarterly Journal of Economics, 125
(1), pp.1-45
Marianne
Bertrand and Sendhil Mullainathan
(2004).
Are
Emily and Greg More Employable than Lakisha and
Jamal? A Field Experiment on Labor Market Discrimination. American Economic
Review, 94(4), pp.991-1013
Jonathan
A. Parker, Nicholas S. Souleles, David S. Johnson,
and Robert McClelland (2010). Consumer
Spending and the Economic Stimulus Payments of 2008, working paper
Northwestern University
Joshua Angrist and Jörn-Steffen Pischke (2009), Mostly Harmless Econometrics:
An Empiricist’s Companion, chapter 4
4. Regression Discontinuity Design
·
Effect
class size on achievement
·
Sharp
RDD
·
Choosing
the bandwidth
·
Choosing
the control function
·
Fuzzy
RDD
·
Checklist
for implementation
·
Creating
variation by using the cross-section
Joshua Angrist and Jörn-Steffen Pischke (2009), Mostly Harmless Econometrics:
An Empiricist’s Companion, chapter 6
David
S. Lee and Thomas Lemieux (2010). Regression
Discontinuity Designs in Economics, Journal
of Economic Literature, 48,
pp.281-355
Guido
Imbens and Thomas Lemieux (2008). Regression
Discontinuity Designs: A Guide to Practice, Journal of Econometrics, 142(2), pp.615-635
Joshua
D. Angrist and Victor Lavy
(1999).
Using
Maimonides’ Rule to
Estimate the Effect of Class Size on Scholastic Achievement, Quarterly Journal of Economics, 114(2),
pp.533-775
Workshop #2 on RDD - data
5. Dynamic Panel Data Models
·
Examples:
income and democracy, convergence, education and growth
·
Dynamic
models with fixed effects
·
Lagged
dependent variable
·
Bounding
the true coefficient
·
Using
lags as instruments
·
GMM
estimators (Arellano-Bond, Blundell-Bond system GMM)
·
Implementation
in Stata
·
Endogenous
explanatory variables
·
Serially
correlated errors
·
Dynamics
in the effect of interest
Joshua Angrist and Jörn-Steffen Pischke (2009), Mostly Harmless Econometrics:
An Empiricist’s Companion, section
5.3-5.4
Daron Acemoglu, Simon Johnson, James A. Robinson and Pierre Yared (2008). Income and Democracy,
American Economic Review, 98(3),
pp.808-842
Francesco Caselli, Gerardo
Esquivel, and Fernando Lefort (1996). Reopening the
Convergence Debate: A New Look at Cross-Country Growth
Empirics, Journal of Economic Growth,
1(3), pp.363-389
Stephen
R. Bond, Anke Hoeffler, and
Jonathan Temple (2001). GMM
Estimation of Empirical Growth Models, CEPR Discussion Papers 3048
Coen Teulings and Thijs van Rens (2008). Education,
Growth, and Income Inequality, Review
of Economics and Statistics, 90(1), pp.89-104
6. Standard errors
·
Inference
problems
·
Standard
error of the OLS estimator
·
Homoskedasticity
·
Generalized
Least Squares (GLS)
·
Robust
standard error estimation
·
Cluster-robust
standard errors
·
How
much does clustering matter?
·
Clustering
as double-counting
·
Autocorrelation
in panels
Joshua Angrist and Jörn-Steffen Pischke (2009), Mostly Harmless Econometrics:
An Empiricist’s Companion, chapter 8
Kurt Schmidheiny (2010). Clustering in the Linear Model, Short Guides to Microeconometrics
Brent R. Moulton
(1986). Random
Group Effects and the Precision of Regression Estimates, Journal of Econometrics, 32(3),
pp.385-397
Jeffrey M.
Wooldridge (2003). Cluster-Sample
Methods in Applied Econometrics, American
Economic Review (P&P), 93(2), pp.133-138
Marianne
Bertrand, Esther Duflo and Sendhil
Mullainathan (2004). How
Much Should We Trust
Differences-in-Differences Estimates?, Quarterly Journal of Economics, 119(1), pp.249-275
Two-way clustering made easy (thanks to Jean-Marc Fournier
for pointing out these references)
Christopher
F Baum, Austin Nichols and Mark E Schaffer (2010). Evaluating
one-way and two-way cluster-robust covariance matrix estimates, presentation
at the BOS’10 Stata Conference, July 2010
Samuel B. Thompson
(2009). Simple
Formulas for Standard Errors that Cluster by Both Firm and Time, working
paper.
Workshop #3 on DPD and clustering - data
Specialized topics
·
Matching
and the propensity score
·
Heterogeneity
and Local Average Treatment Effect
·
Weak
instruments
·
Discrete
choice models (logit, probit,
tobit)
·
Quantile regression
·
Non-parametric
estimation
·
Synthetic
panel data (repeated cross-sections)
·
Bootstrapping
Thijs van Rens | CREI
| Department of Economics and Business
| Universitat Pompeu Fabra