Applied Econometrics
February 23, 2005
Jin-Tan Liu
Department of Economics,
National Taiwan University and NBER
Email：liujt@ntu.edu.tw
Telephone：02-23519641 ext. 520
1
Introduction
Kennedy, Peter (2003), “Chapter 21: Applied Econometrics,”
A Guide to Econometrics, Blackwell Publishing.
2
Introduction
1. Economic Theory, Interesting Topics:
 Wooldridge, Jeffrey M. (2003), Introductory Econometrics:
A Modern Approach, South-Western. Chapter 19,
“Carrying out an Empirical Project”.
 Policy Evaluation: Natural Experiment
 NBER web site: www.nber.org
 Taiwan Study:
 1968 Taiwan 9-years education extension program
 1995 Taiwan National Health Insurance
Happiness Research in Economics
3
Topics

Plug and Vijberberg (2003), “Schooling, Family Background,
and Adoption: Is It Nature or Is it Nurture?,” JPE, 111(3), 611641.

Plug (2004), “Estimating the Effect of Mother’s Schooling on
Children’s Schooling Using a Sample of Adoptees,” AER,
94(1), 358-368.

Persico, Postiewaite, and Silverman (2004), “The Effect of
Adolescent Experience on Labor Market Outcomes: The Case
of Height,” JPE, 112(5), 1019-1053.
4
Introduction
2. Data:
 “Econometrics is much easier without data.” (Verbeek,
2000)

“At least 80 percent of the material in most of the existing
textbooks in econometrics focuses purely on econometric
techniques. By contrast, practicing econometricians typical
spend 20 percent or less of their time and effort on
econometric techniques per se; the remainder is spent on
other aspects of the study, particularly on the construction
of a relevant econometric model and the development of
appropriate data before estimation and the interpretation of
results after estimation.” (Intriligator, Bodkin, and Hsiao,
1996).
5
Introduction
2. Data:
 Griliches, Zvi (1986), “Economic Data Issues,” Handbook
of Econometrics, Volume III, Elsevier Science Publishers.

Deaton, Angus (1995), “Data and Econometric Tools for
Development Analysis,” Handbook of Development
Economics, Volume III, Elsevier Science Publishers.

Deaton, Angus (1997), “Econometric Issues for Survey
Data,” The Analysis of Household Surveys: A Microeconometric Approach to Development Policy, Johns
Hopkins University Press.
6
Introduction
2. Data:
 Hamermesh, Daniel S. (1999), “LEEping into the future of
Labor Economics: the Research Potential of Linking
Employer and Employee Data,” Labor Economics, 6, 25-41.
 Why bother with linking employer-employee data?
 New directions for research and policy.


Hamermesh, Daniel S. (2002), “International Labor
Economics,” NBER working paper 8757.
NBER, Journal of Economic Data. (A forthcoming Journal)
7
Introduction
3. Econometric Method:
 “the current disconnect between economics and
econometrics” … “in the past two decades, the gap between
econometric theory and empirical practice has grown,”…
“command of statistical methods is only a part and
sometimes a very small part of what is required to do firstclass empirical work,” (Heckman, 2001).

“ it is not what you know about something which is
important but rather how you use it.” (Pagan, 1999, p. 374).
8
Introduction
3. Econometric Method:
 Hamermesh, Daniel S. (1999), “The Art of Labormetrics,”
NBER working paper 6927.

Jones, Andrew M. (2000), “Health Econometrics,”
Handbook of Health Economics, Volume 1, Elsevier
Science Publishers.
9
The Ten Commandments
of Applied econometrics
Rule 1: Use common sense and economic theory

The role of theory extends beyond the development of
the specification; it is crucial to the interpretation of the
results and to identification of predictions from the
empirical results that should be test.
10
The Ten Commandments
of Applied econometrics
Rule 2: Avoid type III errors

A type III error occurs when a researcher produces the
right answer to the wrong question.
11
The Ten Commandments
of Applied econometrics
Rule 3: Know the context

Do not try to model without understanding the nonstatistical aspects of the real-life system you are trying to
subject to statistical analysis. (Belsley and Welch, 1988).

History, institutions, operating constraints, measurement
peculiarities, cultural customs.

How were the data gathered?
12
The Ten Commandments
of Applied econometrics
Rule 4: Inspect the data

Economists are accused of never looking at their data.

Economists are unique among social scientists in that they
are trained only to analyze, not to collect data.

Data generation is a dirty, time-consuming, expensive and
non-glorious job.

Environmental Economics: Benefit Estimation, Contingent
Valuation Method.

Summary Statistics, Graphs, and Data Cleaning (any
observations impossible, unrealistic or suspicious?)
13
The Ten Commandments
of Applied econometrics
Rule 5: Keep it sensibly simple

Econometricians employ the latest, most sophisticated
econometric techniques, often because such techniques are
novel and available, not because they are appropriate.

Think first why you are doing before attacking the problem
with all the technical arsenal you have and churning out a
paper that may be mathematically imposing but of limited
practical use. (Maddala, 1999).
14
The Ten Commandments
of Applied econometrics
Rule 6: Test the estimation

to check that the results make sense.

The signs of coefficients as expected? Important
variables statistically significant? Are coefficient
magnitudes reasonable? Are the results consistent with
theory?
15
The Ten Commandments
of Applied econometrics
Rule 7: Be prepared to compromise

Econometric theory courses students are taught standard
solutions to standard problem, but in practice there are no
standard problems, only standard solutions.
16
The Ten Commandments
of Applied econometrics
Rule 9: Do not confuse statistical significance with
meaningful magnitude

Very large samples can give rise to estimated coefficients
with very small standard errors.

Look at the magnitude of coefficient estimates as well as
their significance.
17
The Ten Commandments
of Applied econometrics
Rule 10: Report a sensitivity analysis

Are the results sensitive to the sample period, the
functional form, the set of explanatory variables, or
measurement of proxies for the variables?

Are robust estimation results markedly different?
18