Analysis of Panel Data
School of Business
Professor William. Greene
Department of Economics
Office:;MEC 7-90, Ph. 998-0876
Home page: http://people.stern.nyu.edu/wgreene
Return to course home page.
Abstract: This is
an intermediate level, Ph.D. course in the area of Applied
Econometrics dealing with Panel Data. The range of topics
covered in the course will span a large part of econometrics generally, though
we are particularly interested in those techniques as they are adapted to the
analysis of 'panel' or 'longitudinal' data sets. Topics to be studied
include specification, estimation, and inference in the context of models that
include individual (firm, person, etc.) effects. We will begin with
a development of the standard linear regression model, then extend it to panel
data settings involving 'fixed' and 'random' effects. The asymptotic
distribution theory necessary for analysis of generalized linear and nonlinear
models will be reviewed or developed as we proceed.. We will then turn to
instrumental variables, maximum likelihood, generalized method of moments
(GMM), and two step estimation methods. The linear model will be extended
to dynamic models and recently developed GMM and instrumental variables
techniques. The classical methods of maximum likelihood and GMM and
Bayesian methods, especially MCMC
techniques, are applied to models with individual effects. The last third
of the course will focus on nonlinear models. Theoretical developments
will focus on heterogeneity in models including random parameter variation,
latent class (finite mixture) and 'mixed' and hierarchical models. We
will also visit the theory for techniques for optimization in the setting
of nonlinear models. We will consider numerous applications from the
literature, including static and dynamic regression models, heterogeneous
parameters models, random parameter variation, and specific nonlinear models
such as binary and multinomial choice and models for count data.
Prerequisites: Multivariate calculus, matrix algebra, probability and
distribution theory, statistical inference, and a previous course in
Econometrics at the level of Greene (7th ed, 2012) are assumed..
Course Requirements: Grades for the course will be based on:
- Take home final exam (40%)
- 5 problem sets and exercises involving theory and estimation (40%).
or extension. Using the data
from any published study that is based on panel data, attempt to replicate
some of the author's computations. (Note, it might not be possible.) Or,
use their data to do some panel data modeling of your own. Report your
results in the form of a short paper. Note, some such data sets are
provided on the home page for this course. The data archive for the Journal of Applied Econometrics is
a great source for data sets for econometric analysis. Alternatively, present
a study of your own, again, using methods and models developed in this
- Texts: The recommended text for the course is: Baltagi,
Econometric Analysis of Panel Data,
5th Edition, Wiley, 2013. Also recommended, but not essential,
is Wooldridge, J., Econometric Analysis of Cross Section and
Panel Data, MIT Press, 2nd
Ed, 2010. Strongly recommended for background is Greene, Econometric Analysis, 7th
Edition, Prentice Hall, 2012.
- Other Recommended
Texts: You should have a reference text for basic
concepts in econometrics used in the course. We will not be
reworking in detail material developed in Econometrics I. For a backup
reference, I suggest (of course) Greene, W., Econometric Analysis, 7th
Ed., Prentice Hall, 2012. Copies of a few relevant chapters of Greene
7/e will be distributed, but you might want to have a copy of the book for
general reference. Wooldridge also contains much of this material, but at
a somewhat higher level. Wooldridge's book is also somewhat shorter
than Greene's on applications and examples, which many find preferable.
Unfortunately, the number of general graduate level econometrics texts is
fairly small. One that is very good that I might recommend for a
general course, Hayashi's, Econometrics (Princeton,
2000) is, unfortunately, not particularly useful for a course in panel
data. An excellent single volume for a survey of many topics is
Baltagi, B. ed., A Companion to Theoretical Econometrics,
Blackwell, 2001. There are also many other lower level econometrics texts
available, and you should be able to use any of them (such as Gujarati or
Wooldridge's undergraduate text) for a rudimentary introduction to
econometric ideas. Note, though, this course will be considerably
more advanced than those undergraduate texts.
- Greene, W., Econometric Analysis. You can find
several chapters from this text on this Microeconometrics
website Some Survey papers
on discrete choice models:
for Count Data
Baltagi (2013) and Hsiao (2014) are excellent background references.
Baltagi is somewhat more terse and his algebra is heavier than
Hsiao's, but either book would be an excellent resource. You might want to
obtain one or both of them (both paperback). Note that I have made Baltagi
required for the course.
- Bibliography: The reference list below lists a sampler
of the literature in this area, including some of the most important
articles. This is a small slice of the literature. Baltagi notes
that as of 2002 or so he could find nearly 3,000 published articles listed
that mention panel data in the title or abstract. That would not
include at least as many unpublished working papers and the last several
years of development - at least as much research has been done since.
Suffice to say, this literature is enormous.
- Supplementary Books: The literature on panel data is vast - it is one
of the most active areas of research in econometrics. For the
interested student who wishes to pursue the subject, in addition to the
received journal articles, the following are the current crop of books on
the subject of panel data, listed (more or less), in the order in which
they are most likely to appeal to the applied researcher
- Arellano, M., Panel Data Econometrics, Oxford University
Press, 2003. Current theoretical research and thinking on the
- Baltagi, B., Econometric
Analysis of Panel Data, 5th Edition,
Wiley, 2013. This is the canonical reference for
researchers Algebraically terse and dense, but definitely complete
on analysis of the linear model.
- Hsiao, C.,
of Panel Data, Cambridge University
Press, 3rd ed., 2014.
- Baltagi, ed., Panel Data: Theory and Applications, Physica Verlag,
2004. A collection of articles from the journal Empirical Economics, including in
full, a recent special issue of the journal on panel data.
- Baltagi, B. ed., A Companion to Theoretical Econometrics, Blackwell,
2001. Not specifically about panel data, but a very useful book for
- Baltagi, B., ed., The Oxford Handbook
of Panel Data, 2014. A collection of articles on models and methods.
- Baltagi, B., Ed., Recent Developments in the Econometrics of
Panel Data, 2004.
- Cameron, A and Trivedi, P., Microeconometrics, Cambridge University Press,
2005. Superb reference work on the field.
- Diggle, P., K. Liang and S. Zeger, Analysis of Longitudinal Data, Oxford University Press, 1994.
Analysis from the "statistical" point of view (not econometric)
with a focus on "generalized linear models," and GEE
- Frees, E., Longitudinal
and Panel Data: Analysis and Applications in the Social Sciences, Cambridge University Press, 2004.
- Griliches, Z. and M. Intriligator, Handbook of Econometrics, Volume 2, Chapter
22, Panel Data (By Gary Chamberlain), North Holland, 1984. An
important early summary of panel data theory.
- Heckman, J.. and E. Leamer, Handbook of Econometrics, Volume 5, Chapter
53, Panel Data Models: Some Recent Developments (by Manuel Arellano and Bo Honore), North
Holland, 2001. Summary of new results, focused on GMM and
- Heckman, J. and B. Singer, Longitudinal Analysis of Labor Market Data,
Cambridge University Press, 1985. A
collection of studies from the area of labor economics, the source of a
large proportion of the research in microeconometrics.
- Hsiao, C., K. Lahiri, L. Lee and M. Pesaran, Analysis of Panels and Limited Dependent
Variable Models In Honor of G. S. Maddala, Cambridge University Press, 1999. A collection of
articles and applications.
- Lee, M.,
Panel Data Econometrics: Method of Moments and Limited Dependent
Variables, Academic Press, 2002. Somewhat esoteric, and not for
the applications oriented type, but an in depth look at frontier methods
in semiparametric analysis for LDV models with panel data.
- Matyas, L., The
Econometrics of Panel Data, 3rd
Ed., Springer, 2008.
- Matyas, L and P. Sevestre. The Econometrics of Panel Data, 3rd
ed., Kluwer Academic, 2008. Large, extremely interesting
collection of essays on many topics.
- Nerlove, M., Essays in Panel Data Econometrics, Cambridge University
Press, 2002. A look back at the evolution of the subject from one
of the poineers. The original Balestra-Nerlove study described at
length in this book, is one of the cornerstones of the literature.
- Maddala, G.S., The
Econometrics of Panel Data: Volumes I and II, Edward Elgar, 1993.
These two volumes are collections of papers, most of them highly
technical (e.g., from Econometrica).
- The Journal of Econometrics published three panel data conference volumes:
- Journal of Econometrics, 18, 1, Econometrics of Longitudinal Data,
- Carraro, C., Peracchi, F. and Weber, G., eds.,
The Econometrics of Panels and Pseudo Panels, JE, 59, 1/2, 1993.
- Baltagi, B., ed., Panel Data, JE, 68, 1, 1995.
- Software: Some of the outside work for this course will
involve using a computer. All of the major commercial econometrics
packages (SAS, Stata, LIMDEP,
NLOGIT, R, Gauss, Matlab,
contain programs for analysis of panel data. Some are more complete than
others. The two among these that will contain the widest range of
techniques, will most closely match our course, and are likely to be the
most accessible to students are LIMDEP (or NLOGIT) and Stata.
Stern has a site license for Stata for those who wish to
use it. Many researchers are using R to develop their own
applications. I will use
Version 5.0 of NLOGIT for this course. I will distribute a
version of NLOGIT for students
in this course who wish to use it for their empirical work. Further
details on software will be provided on the first day of class.
- Data Sets: There are many data sources available on
the internet. The archives of the Journal of Applied Econometrics
is a particularly rich source. I will also make available a number
of panel data sets for students to use in this course. These can be
accessed from the course home page - go to the link for "Panel Data
- Readings: Articles on the various subtopics in the panel
data arena are listed below. These are offered as background and as
a gateway into the literature for the interested student. We will
discuss a few of these specifically in class. Note, in a few cases,
the list below contains links to these articles on the web. Some of
these are publicly available manuscripts. Others will require access
to JSTOR or some other archive. The
NYU server provides access to most journals.
- Supplement: Website
for a short course in microeconometrics that contains some references and
data sets as well as some lecture notes.
Return to course home page.
Reading suggestions with the section topic are from Baltagi (B), Greene (G) and Wooldridge (W).
Underlined references appear above with the supplementary texts.
Remaining references are listed below. The articles listed in the
references are for background. We will (obviously) not be able to discuss all
of them in class. Assigned articles are marked with '**'
- I. Econometric Models and Panel Data: [B, Ch. 1], [G, Ch.
11], [W, Chs. 1, 2,
- Econometric Models,
- Benefits and Limits of Panel Data
- General References on Panel Data: Chamberlain (1984)
- Theory and Application: Arellano and Honore (2001)
- II. Fixed and Random Effects: [B,
Ch. 2-4,9], [G, Ch. 11,
esp. 11.4, 11.5, ], [W, Chs. 7, 10, 11]
- Fixed vs. Random Effects
- Balanced and unbalanced panels, rotating panels
- Estimation methods: OLS, GLS, FGLS, MLE:
- Specification tests, LM, Hausman: Hausman
(1978)**, Breusch and Pagan (1979**, 1980)
- Alternative specifications: Nested random
effects, one and two way effects models, clustering: Wooldridge (2003), Antwiler
- Fixed and random effects: Mundlak's approach:
- Chamberlain's and Mundlak's Approach to Random
in differences estimation
- General Discussion of FEM and REM: Baltagi (2013, Ch. 2,3)
- III. Extensions: Heteroscedasticity,
Autocorrelation, Robust Estimation: [B, Ch. 5,10.5], [G, Ch. 11],
- Clustering and Robust Estimation: Wooldridge
- Time and Individual Effects: Bai(2009),
Cornwell, Schmidt and Sickles (1990), Chen (2014)
- Heteroscedasticity: White (1980)
- Covariance Structure Models and Cross Country
Models: Beck and Katz (1995)
- Autocorrelation, Newey and West (1987)**
- Spatial Autocorrelation: Anselin (2001)
- Measurement Error: Griliches and Hausman (1986)
- General discussion of nonspherical
disturbances: Baltagi (2013, Chapter 5, 10.5)
- IV. Instrumental Variables and GMM Estimation,
Dynamic Models, Time Series Application: [B, Ch. 7, 8, 12], [G, Ch.
8, 10, 13], [W, Chs. 5, 8, 11.3, 14]
- Endogeneity, Exogeneity, and Instrumental
Variables: Hausman and Taylor
(1981)**, Cornwell and Rupert (1988),
- Dynamic Models: Anderson and Hsiao (1981),
Balestra and Nerlove (1966), Dahlberg and Johansson (2000), Blundell and
Bond (1998), Arellano and Bond (1991), Arellano and Bover (1995),
Kripfganz and Schwarz (2013)
- The GMM Estimator
- The Arellano, Bond, and Bover Model - Dynamic
Panel Data Models: Arellano and Bover (1995)**, Arellano and Bond
(1991), Arellano and Bover (1995), Bond (2002), Gong et al. (2003)
- Unit Roots in Panel Data: Baltagi (2013,
- Other References
- V. Parameter Variation (First Generation
Models), Hierarchical Models, Two Step Estimation: [G, Chs. 11.11,
- Parameter Heterogeneity
- Cross Section Variation in Parameters
- GLS and FGLS Estimation
- Hierarchical Models - Random Parameters with
Heterogeneous Means: Craig, Douglas, Greene (2004)
- Two Step Estimation: Passmore (2004),
- The Fama-Macbeth Model: Fama and Macbeth
- VI. Nonlinear Models: [G, Ch. 14,
App. E], [W, Chs. 12,
- Nonlinear econometric models
- Optimization: maximum likelihood estimation.the
EM algorithm: Dempster et al. (1977)
- Semiparametric Estimation
- Estimation of models with individual effects:
Quadrature and simulation: Butler
and Moffitt (1982)**, Greene (2004)**
- Markov Chain Monte Carlo
Methods: Casella and George (1992)
- General references on optimization: Judd (1998). These are books on the subject
of optimization as done by social scientists. There is a vast
library on the subject of numerical optimization, in many fields. We are
interested in a broad look at the techniques at this point, since
contemporary microeconometrics is a heavy user of nonlinear techniques:
Murphy and Topel (2002), Newey (1984).
- Simulation based
estimation: Train (2009).
- Econometric software (Stata, SAS, NLOGIT, LIMDEP, etc.) comes with
embedded optimization code. The documentation for all programs
contain details on 'how it's done.' You might be interested in
learning about nonlinear optimization methods.
- VII. Classical and Bayesian Estimation of Models
with Individual Effects: [B, Ch. 1], [G, Chs. 16 ], [W, Chs. 15.8, 16.3.4, 18.7]
- Fixed and random effects in nonlinear models,
Greene (2005), Greene (2004), Honore (2002)**
- The incidental parameters problem,
Abrevaya (1997), Lancaster (2000)**, Nickell (1981)
- Bias reduction, Fernandez-Val (2009)
- Bayesian estimation of the fixed effects model,
Koop et al. (1997)
- Bayesian estimation in random effects,
hierarchical models, Allenby and Rossi (1999)**
- References: Casella and George (1992),
- VIII. Random Parameters and Latent Class Models: [G, Chs. 14.10,
- Random Parameter Models: Hensher and Greene
(2001), Revelt and Train (1998)
- Finite Mixture (Latent Class) Models: Deb
and Trivedi (1997)
- References: Train (2009), McLachlan and Peel
- IX. Discrete Choice Models, Limited
Dependent Variables, Sample Selection Models: [B, Ch.
11], [G, Ch. 17-19], [W, Chs. 15.8, 16, 17, 18.7, 19.6-19.9].
Also, Greene and Hensher, Ordered Choice Survey, Chapters 1,2,5.
- Discrete Choice: Probit and Logit.
Abrevaya (1997)**, Butler and Moffitt (1982), Chamberlain (1980), Honore
and Kyriazidou (2000), Manski (1987), Papke and Wooldridge (2008)
- Ordered Choice: Greene and Hensher (2009)
- Multinomial Choice: Berry et al. (1995)**
- Semiparametric Estimation: Honore and
Kyriazidou (2000), Honore (1992), Manski (1987), Kyriazidou (1997)
- Sample Selection: Nijman and Verbeek (1992),
Verbeek (1990), Verbeek and Nijman (1992), Wooldridge (1995), Zabel
(1992), Kyriazidou (2001), Jensen et al. (2001)
- Censoring and Truncation: Vella and Verbeek
- The Mixed Logit Model: McFadden and Train
- Stochastic Frontiers: Greene (2004a)
- X. Models for Data on Counts: [G,
Ch. 18.4], [W, Chs. 16, 2,
- Models for Counts: Greene (2012, Section 18.4),
Cameron and Trivedi (1986), Wedel et al. (1993), Wang et al. (1998)
- Fixed and Random Effects Models: Hausman et al.
(1984)**, Montalvo (1997)**
- General References on Count Data Models:
Return to course home page.
Reading List: Articles and Other Sources
"The Equivalence of Two Estimators of the Fixed Effects Logit Model".
Economics Letters 1997, 55 (1), 41-44. (Proves the famous
twice beta rule for fixed effects logit with T = 2.)
Allenby, G. and P. Rossi, "Marketing Models of Consumer
Heterogeneity," Journal of Econometrics, 89, 1999, pp. 57-78.
Anderson, T. and C. Hsiao, Estimation of Dynamic Models with Error
Components," Journal of the American Statistical Association, 76,
1981, pp. 598-606.
L., "Spatial Econometrics,"
in Baltagi, B., ed., A Companion to Theoretical Econometrics, pp. 310-330
Antweiler, W., "Nested Random Effects Estimation in
Unbalanced Panel Data," Journal of Econometrics, 101, 2001, pp.
295-312; Comment by Greene, W. (minor algebraic observation).
M. and S. Bond, "Some Tests of
Specification for Panel Data: Monte Carlo Evidence and an Application to
Employment Equations," Review of
Economic Studies, 58, 1991, pp. 277-297. (download)
Arellano, M. and O. Bover "Another Look at the Instrumental Variable
Estimation of Error-Components Models," Journal of Econometrics,
68, 1995, pp. 29-51.
J., "Panel data models with
interactive fixed effects," Econometrica,
77, 1229-1279, 2009. (download)
Balestra, P. and M. Nerlove, "Pooling Cross Section and Time Series Data in
the Estimation of a Dynamic Model: The Demand for Natural Gas," Econometrica,
1966, pp. 585-612.
Baltagi B., J.
Griffin and W. Xiong, "To Pool or Not to Pool: Homogeneous versus Heterogeneous
Estimators Applied to Cigarette Demand," Review of Economics and
Statistics, 82(1), 2000, pp.117-26.
Baltagi, B., "Estimating an Economic Model of Crime Using Panel Data from
North Carolina," Journal of Applied
Econometrics, 21, 2006, --. 543-547.
Beck, N. and Katz, J., "What To Do (and Not to Do) with Time-Series
Cross-Section Data in Comparative Politics," American Political Science
Review, 89, 1995, pp. 634-647.
Berry, S., J. Levinsohn
and A. Pakes, "Automobile Prices
in market Equilibrium," Econometrica, 63, 4, 1995, pp. 841-890.
Bloom, N., M. Schankerman and J. Van
Reenen, "Identifying Technology
Spillovers and Product Market Rivalry," Econometrica, 81, 2013, pp. 1347-1393.
Blundell R., R. Griffith and F. Windmeijer, "Individual effects and dynamics in count data models," Journal of Econometrics 108,
Blundell, R. and S. Bond, "Initial Conditions and Moment Restrictions in
Dynamic Panel Data Models," Journal of Econometrics, 1998, pp.
Bond, S., "Dynamic Panel Data Models: A Guide to Micro Data Methods and
Practice," CEMMAP Working Paper CWP-09/02,
2002. (download pdf)
Breusch, T., and Pagan, A., "The LM Test and Its Applications to Model
Specification in Econometrics," Review of Economic Studies, 47,
1980, pp. 239-254.
Breusch, T., and Pagan, A., "A Simple Test for Heteroscedasticity and
Random Coefficients Variation," Econometrica, 47, 1979, pp.
M. and M. Harding, "Panel Probit
with Flexible Correlated Effects: Quantifying Technology Spillovers in the
Presence of Latent Heterogeneity," Journal
of Applied Econometrics, 28, 2013, pp. 956-981.
Butler, J. and R. Moffitt, "A Computationally Efficient Quadrature
Procedure for the One Factor Multinomial Probit Model," Econometrica,
50, 1982, pp. 761-764.
Cameron, C. and P. Trivedi, "Econometric Models Based on Count Data:
Comparisons and Applications of Some Estimators and Tests," Journal
of Applied Econometrics, 1, 1986, pp. 29-54.
Cameron and Trivedi, Regression Analysis of Count Data, Cambridge University Press, 1998.
Casella, G. and E. George, "Explaining the Gibbs Sampler," The
American Statistician, 46, 3, 1992, pp. 167-174.
Chamberlain, G., "Panel Data," Handbook of
Econometrics, Volume 2, Chapter 22, 1984. (download pdf)
G., "Analysis of Covariance with
Qualitative Data," Review of Economic Studies, 47,1980, pp.
"Estimation of Nonlinear Panel Models with Multiple Unobserved
Effects," Unpublished Manuscript, Boston University, Department of
Economics, 2014. (download)
Cornwell, C. and P. Rupert, "Efficient Estimation with Panel Data: An
Empirical Comparison of Instrumental Variable Estimators," Journal
of Applied Econometrics, 3, 1988, pp. 149-155.
C., P. Schmidt and R. Sickles,
"Production Frontiers with Cross Section and Time Series Variation in
Efficiency Levels," Journal of
Econometrics, 46, 1990, pp. 185-200. (download)
C. and W. Trumbull, "Estimating
the Economic Model of Crime with Panel Data," Review of Economics and Statistics, 76, 1994, pp. 360-366.
Craig, S., S. Douglas and W. Greene:
"Culture Matters: A Hierarchical Linear Random Parameters Model for Predicting
Success of US Films in Foreign Markets," Department of Marketing, Stern School of Business, NYU. (download)
Dahlberg, M. and E. Johansson, "An Examination of the Dynamic Behavior of
Local Governments Using GMM Bootstrapping Methods," Journal of Applied
Econometrics, 15, 2000, pp. 401-416.
P. and P. Trivedi, "Demand for
Medical Care by the Elderly: A Finite Mixture Approach," Journal of
Applied Econometrics, 12, 3, 1997, pp. 313-336.
A., N. Laird and D. Rubin,
"Maximum Likelihood From Incomplete Data via the E.M. Algorithm," Journal
of the Royal Statistical Society, Series B, 39, 1, 1977, pp. 1-38.
Fama, E., and J. MacBeth, "Risk, Return and Equilibrium: Empirical
Tests," Journal of Political Economy, 81, 3, 1973, pp. 607-636. (download pdf)
Fernandez Val, I., "Fixed Effects Estimation of
Structural Parameters and Marginal Effects in Panel Probit Models," Journal of Economertrics, 150, 2009, pp. 71-85.
Gannon, B., "A Dynamic Analysis of
Disability and Labour Force Participation in Ireland 1995-2000," Health Economics, 14, 5005,
pp. 925-938 (download)
Goett A., Hudson K. and Train K., "Customer Choice Among Retail Energy Suppliers: The
Willingness-to-Pay for Service Attributes," Energy Journal, 2002,.21,
pp. 1-28. (download)
Gong, X., A. van Soest and E. Villagomez, "Mobility in the Urban Labor Market: A Panel
Data Analysis for Mexico,"
IZA, Discussion paper 213, 2003. (download pdf)
Greene, W. " Fixed and Random Effects in Nonlinear
Models," Working Paper 01-01, Stern
School of Business, Department
of Economics, New York
University. You can
download this from the web at http://people.stern.nyu.edu/wgreene/panel.doc
Greene, W., "Fixed and Random Effects in Nonlinear
Models," Stern, NYU, Economics, Working Paper 01-10, 2001. (download pdf)
Fixed and Random Effects in Stochastic
Frontier Models, Journal of Productivity Analysis, 23, 1, 2005, pp. 7-32 (download pdf)
Greene, W., "Distinguishing Between Heterogeneity and Inefficiency:
Stochastic Frontier Analysis of the World Health Organizations Panel Data on
National Health Care Systems" Stern Department of Economics, 2004, Health
Economics. (download pdf)
Greene, W., "The
Behavior of the Fixed Effects Estimator in Nonlinear Models," The
Econometrics Journal , 7, 1, 2004, pp. 98-119.)
Greene, W., "Convenient Estimators for the Panel Probit
Model," Empirical Economics, 29, 2004, pp. 21-48. (Also in Baltagi,
2004) (download pdf)
"Interpreting Estimated Parameters and Measuring Individual Heterogeneity
in Random Coefficient Models," NYU/Stern Economics, Working Paper 04-08,
May, 2004 . (download pdf)
Greene, W. and D.
Hensher, Modeling Ordered Choices,
Cambridge University Press, 2009 . (download pdf)
Greene, W. and D. Hensher, "A Latent Class Model for Discrete Choice
Analysis: Contrasts with Mixed Logit," University of Sydney,
Institute for Transport Studies, 2002. (Appeared in Transport Research B,
Griliches, Z., and J. Hausman, "Errors in Variables in Panel Data," Journal
of Econometrics, 31, 1986, pp. 93-118.
Hausman, J., "Specification Tests in Econometrics," Econometrica,
46, 1978, pp. 1251-1271. Develops the "Hausman Test," a now widely
used specification test that gets around the need for nested models imposed by
the conventional likelihood, Neyman-Pearson based tests.
Hausman, J., Hall, B., and Griliches, Z., "Econometric Models for Count Data
with an Application to the Patents-R&D Relationship," Econometrica,
52, 1984, pp. 909-938. The first major reference on count data models for economists.
Includes extensions for panel data. Interesting for proposing an entire class
of models for a nonlinear regression setting.
Hausman, J., and Taylor, W., "Panel Data and Unobservable Individual
Effects," Econometrica, 49, 1981, pp. 1377-1398. Extends the
familiar fixed and random effects models to some more involved cases. For
example, how to deal with fixed effects in models in which group effects are
fixed over time.
D. and W. Greene, "The Mixed Logit Model The State of Practice," University
of Sydney, Institute for
Transport Studies, 2001. (Appeared in Transportation Research, B,
2003). (download pdf)
B., "Trimmed LAD and Least Squares Estimation of Truncated and
Censored Regression Models with Fixed Effects," Econometrica, 60,
1992, pp. 533-565.
B., "Non-Linear Models with
Panel Data," CEMMAP, Working paper 13-02, 2002. (download pdf)
B and E. Kyriazidou, "Panel Data
Discrete Choice Models with Lagged Dependent Variables," Econometrica,
68, 2000, pp. 839-874.
Jensen, P., M. Rosholm and M. Verner, "A Comparison of Different Estimators for Panel
Data Sample Selection Models," CIM, CLS,
Aarhus, 2001. (download pdf)
Judd, K., Numerical Methods in Econometrics, MIT Press, 1998.
Kyriazidou, E., "Estimation of a Panel Data Sample Selection
Model," Econometrica, 65, 1997, pp. 1335-1364.
Kyriazidou, E., "Estimation of Dynamic Panel Data Sample
Selection Models," Review of Economic Studies, 68, 2001, pp.
Koop, G., J. Osiewalski and M. Steel, Bayesian efficiency analysis through individual
effects: hospital cost frontiers, Journal of Econometrics 76, 1997, pp.
S., and C. Schwarz, "Estimation
of Linear Dynamic Panel Data Models with Time Invariant Regressors," (December 16, 2013). Available at
SSRN: http://ssrn.com/abstract=2386572 or http://dx.doi.org/10.2139/ssrn.2386572. (download)
"The Incidental Parameters Problem Since 1948." Journal of
Econometrics, 2000, 95, 391-414.
Manski, C., "Semiparametric Analysis of Random Effects
Linear Models from Binary Panel Data," Econometrica, 55, 1987, pp.
D. and K. Train, "Mixed MNL
Models for Discrete Response," Journal of Applied Econometrics, 15,
2000, pp. 447-470.
McLachlan, G. and D. Peel, Finite Mixture Models, Wiley, 2000.
Montalvo, J., "GMM Estimation of Count-Panel-Data Models
with Fixed Effects and Predetermined Instruments," Journal of Business
and Economic Statistics, 15, 1997, pp. 82-89. Current application that
shows the use of the GMM estimation method. Straightforward reading, accessible
to students in this course.
Mundlak, Y., "On the Pooling of Time Series and Cross
Section Data," Econometrica, 1978, pp. 1251-1271.
Murphy, K., and Topel, R., "Estimation and Inference in Two Step
Econometric Models," Journal of Business and Economic Statistics,
3, 2002, pp. 370-379. Lays out the computations needed for handling two step
maximum likelihood or least squares estimation.
Newey, W., "A Method of Moments Interpretation of
Sequential Estimators," Economics Letters, 14, 1984, pp. 201-206.
Similar to Murphy and Topel. Develops a similar set of results for GMM
estimation - M&T is for ML and least squares (though it can be extended to
some GMM estimators).
Newey, W., and West, K., "A Simple, Positive Semi-definite, Heteroscedasticity
and Autocorrelation Consistent Covariance Matrix," Econometrica,
55, 1987, pp. 703-708. The canonical presentation of one of the most important
tools in the applied econometricians toolkit. Generalizes White's estimator,
and makes feasible, many GMM estimators in time series settings.
Nickell, S., "Biases in Dynamic Models with Fixed
Effects," Econometrica, 49, 6, 1981, pp. 1417-1426.
Nijman, T. and M. Verbeek, "Nonresponse in Panel Data: The Impact on
Estimates of Life Cycle Consumption Function," Journal of Applied
Econometrics, 7, 1992, pp. 243-257.
Papke, L. and J. Wooldridge, "Panel Data methods for Fractional Response
Variables with an Application to Test Pass Rates," Journal of Econometrics, 145, 2008, pp. 121-133. (download)
Passmore, W., "The GSE
Implicit Subsity and Value of Government Ambiguity," Board of Governors,
Federal Reserve System, Manuscript, 2004. (download pdf)
Rendon, S., "Fixed and Random Effects in Classical and
Bayesian Regression," Oxford Bulletin of Economics and Statistics,
75, 2013, pp. 460-476. (download pdf)
Revelt, D. and K. Train, "Mixed Logit with Repeated Choices: Households'
Choices of Appliance Efficiency Level," Review of Economics and Statistics,
1998, 80, , pp. 1-11.
Saxonhouse, G, "Estimated Parameters as Dependent
Variables," American Economic Review, 46(1), March 1976, 178-183.
M., H. Tauchmann, J. Wasem and S. Gress,
"Elasticities of market Shares and Social Health Insurance Choice in
Germany: A Dynamic Panel Data Approach," Health Economics, 16, 2007, pp. 243-256. (download)
Train, K., Discrete Choice Methods with Simulation,
Cambridge University Press, 2009.
Train, K., "A Comparison of Heirarchical Bayes and Maximum Simulated
Likelihood for Mixed Logit," Economics, Berkeley, 2003. (download pdf)
F. and M. Verbeek, "Two Step
Estimation of Panel Data Models with Censored Endogenous Variables and
Selection Bias," Journal of Econometrics, 90, 1999, pp. 239-263.
M., "On the Estimation of a Fixed
Effects Model with Selectivity Bias," Economics Letters, 34, 1990,
M. and T. Nijman, "Testing for
Selectivity Bias in Panel Data Models," International Economic Review,
33, 3, 1992, pp. 681-703.
Wang, P., I. Cockburn and M. Puterman, "Analysis of Panel Data: A Mixed Poisson
Regression Model Approach," Journal
of Business and Economic Statistics, 16, 1998, pp. 27-41.
M., W. DeSarbo, J. Bult, and V. Ramaswamy, "A Latent Class Poisson Regression Model for Heterogeneous Count Data,"
Journal of Applied Econometrics, 8, 1993, pp. 397-411.
Heteroscedasticity-Consistent Covariance Matrix Estimator and Direct Test for
Heteroscedasticity," Econometrica, 48, 1980, 817-838. The White
estimator for unknown heteroscedasticity.
J., "Selection Corrections for
Panel Data Models Under Conditional Mean Independence Assumptions," Journal
of Econometrics, 68, 1995, pp. 115-132.
Wooldridge, J.M., "Cluster-Sample Methods in
Applied Econometrics," American Economic Review, 93, 2003, 133-138.
J., "Estimating Fixed and Random
Effects Models with Selectivity," Economics Letters, 40, 1992, pp.
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