**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

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