Econometric Analysis of Panel Data
of Business / B55.9912
Professor William. Greene
Department of Economics
Office:;MEC 7-90, Ph. 998-0876
Home page: http://people.stern.nyu.edu/wgreene
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:
- Midterm examination (25%)
- Take home final exam (35%)
- 7 problem sets and small exercises involving theory and estimation
- Term paper. This will
involve a serious application of the techniques developed in the course
applied to a 'real' data set, either from
the student's own research or obtained from one of the many publicly
available archives on the internet. Details of the assignment wil be
discussed in class. (20%)
- Texts: The required text for the course is: Baltagi’s Econometric Analysis of Panel Data, 4th
Edition, Wiley, 2008. Recommended but not essential is Wooldridge, J.,
Econometric Analysis of Cross Section and Panel Data, MIT
Press, 2nd Ed, 2010. More
strongly recommended for background is Greene, Econometric Analysis, 7th Edition, Prentice Hall,
- 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 the author’s Discrete
Choice Modeling website
Some Survey papers on discrete
for Count Data
References: Both Baltagi (2008)
and Hsiao (2004) 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 the writing of his text (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 five years of development. 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
- Baltagi, B., Econometric Analysis of Panel Data, 4th Edition, Wiley,
2008. This is the canonical reference for researchers
Algebraically terse and dense, but definitely complete on analysis of the
- Hsiao, C.,
Analysis of Panel Data, Cambridge
1st ed., 1986, 2nd ed., 2004. A classic.
- 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
- Baltagi, B. ed., A Companion to Theoretical Econometrics, Blackwell,
2001. Not specifically about panel data, but a very useful book for
- Arellano, M., Panel Data Econometrics, Oxford University
Press, 2003. The most current theoretical research and thinking on
- Baltagi, B., Ed., Recent Developments in the Econometrics of
Panel Data, 2004.
- 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
- 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 semiparametric methods.
- 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, Cambridege 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.
- Frees, E., Longitudinal
and Panel Data: Analysis and Applications in the Social Sciences, Cambridge University Press, 2004.
- Matyas, L and P. Sevestre. The Econometrics of Panel Data, 3rd
ed., Kluwer Academic, 1996. 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).
- Conferences: In addition to these books and monographs, we
note, there have been at least thirteen worldwide conferences
specifically on the subject of panel data. The most recent were
held at Cambridge University (UK) in July, 2006. (Panel Data Conference).
was at Xiamen University in China in July, 2007. The 18th international panel data conference will be held at
the Bank of France in Paris in July, 2012.
- The Journal of Econometrics also publishes conference volumes related to
some of these. Two particular issues that contain a number of
influential papers are:
- 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: Most 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,
EViews) 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
also 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
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. Many of these are offered as background
and as a gateway into the literature for the interested student. A
few are marked as required. We will discuss 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 an actual trip
into the pages of a journal.
course home page.
Course Outline and Schedule:
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), Hsiao
(2004, Chapter 1)
- Recent Developments in Theory and Application: Arellano and Honore (2001), Kitazawa (2000)
- 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: Baltagi and Griffin (1983)
- Balanced and unbalanced panels, rotating panels
- Exogeneity: Mundlak (1978), Lillard and Willis
- Estimation methods: OLS, GLS, FGLS, MLE: Swamy
and Arora (1972), Wallace and Hussain (1969), Balestra and Nerlove
(1980), Wansbeek and Kapteyn (1978), Lillard and Weiss (1979), Berry,
Gottschalk, and Wissokur (1988), Maddala (1971)
- Specification tests, LM, Hausman: Hausman
(1978)**, Breusch and Pagan (1979**, 1980), Arellano (1993), Ahn
and Low (1996)
- Alternative specifications: Nested random effects,
one and two way effects models, clustering: Antweiler (2001)
- Fixed and random effects: Mundlak’s approach:
- Chamberlain's Approach to Random Effects:
- General Discussion of FEM and REM Hsiao (2004, Chapter 2, 3, 4 to Section
- More In Depth Treatment of General Issues in
Panel Data: Chamberlain
- III. Extensions: Heteroscedasticity,
Autocorrelation, Robust Estimation: [B, Ch. 5,10.5], [G, Ch. 11],
- Heteroscedasticity: Kiefer (1980), Mazodier and
Trognon (1978), White (1980)
- Covariance Structure Models and Cross Country
Models: Beck and Katz (1995)
- Autocorrelation, Newey and West (1987)**,
Baltagi et al. (2003), Baltagi and Li (1995)
- Spatial Autocorrelation: Anselin (2001)
- Measurement Error: Griliches and Hausman (1986)
- General discussion of nonspherical
disturbances: Baltagi (2008, 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)**, Amemiya and MaCurdy (1986), Cornwell and Rupert (1988),
Breusch, Mizon and Schmidt (1989), Baltagi and Khanti-Akom (1990), Keane
and Runkle (1992)
- Dynamic Models: Anderson and Hsiao (1981),
Hsiao (1982), Balestra and Nerlove (1966), Bhargava and Sargan, (1983),
Bhargava (1987), Dahlberg and Johansson (2000), Blundell and Bond (1998)
- The GMM Estimator, Ahn and Schmidt (1995)**,
Im, Ahn, Schmidt and Wooldridge (1999),Ahn and Schmidt (1999),
Holtz-Eakin (1988), Holtz-Eakin, Newey, and Rosen (1988)
- The Arellano, Bond, and Bover Model - Dynamic
Panel Data Models: Arellano and Bover (1995)**, Arellano and Bond (1991),
Bond (2002), Judson and Owen (1996)
- Unit Roots in Panel Data: Baltagi (2001,
Chapter 12), Kao (1999)
- Other References
- V. Parameter Variation (First Generation
Models), Hierarchical Models, Two Step Estimation: [G, Chs. 11.11,
- Parameter Heterogeneity: Swamy and Tavlas
- Cross Section Variation in Parameters:
- GLS and FGLS Estimation, Hsiao (2004), Hildreth
and Houck (1968)
- Hierarchical Models - Random Parameters with
Heterogeneous Means: Craig, Douglas, Greene (2004a)
- Two Step Estimation: Passmore (2004),
Saxonhouse (1976), Milcent (2003)
- 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 (2001)**, Gourieroux and Monfort (1996),
- Markov Chain Monte Carlo
Methods: Casella and George (1992), Chib and Carlin (1999)
- General references on optimization: Altman et al (2004), 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
- Simulation based estimation: Robert and Casella (1999), Train (2003).
- Econometric software (Stata, SAS, NLOGIT, LIMDEP, etc.) comes with embedded
optimization code. The documentation for all programs contains
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, 16.8, 19.6]
- Fixed and random effects in nonlinear models,
Greene (2001)**,Greene (2002a), Greene (2004),Rendon (2003), Honore
(2002)**, Heckman (1981a), Heckman (1981b)
- The incidental parameters problem, Neyman and
Scott (1948), Abrevaya (1997), Lancaster (2000)**, Lancaster (2002),
Nickell (1981), Hsiao (1996, repeated in 2004)
- Bias reduction, Hahn and Kuersteiner (2003),
Hahn and Newey (2004)
- Bayesian estimation of the fixed effects model,
Koop et al. (1997)
- Bayesian estimation in random effects,
hierarchical models, Rossi and Allenby (2003), Allenby and Rossi
(1999)**, Train and Huber (2001)
- References: Casella and George (1992),
- VIII. Random Parameters and Latent Class Models: [G, Chs. 14.10,
- Random Parameter Models: Swamy and Tavlas
(1991), Hensher and Greene (2001)
- Finite Mixture (Latent Class) Models: Deb
and Trivedi (1997), Kamakura,
Kim and Lee (1996), Greene and Hensher (2002)
- References: Hsiao (2004, Chapter 6), Train
(2003), McLachlan and Peel (2000), Greene (2001), Greene (2004)
- IX. Discrete Choice Models, Limited
Dependent Variables, Sample Selection Models: [B, Ch.
11], [G, Ch. 17-19], [W, Chs. 15.8, 16.8, 17.7, 18.5]. Also, Greene and Hensher, Ordered Choice
Survey, Chapters 1,2,5.
- Discrete Choice: Probit and Logit. Abrevaya
(1997)**, Akin et al. (1979), Albert and Chib (1993), Butler and Moffitt (1982), Chamberlain
(1980), Guilkey and Murphy (1993), Sepanski (2000), Honore and
Kyriazidou (2000), Manski (1987)
- 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: Kyriazidou (1997), 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
(1999), Honore (1992), Honore (1993), Honore and Lu (2004)
- The Mixed Logit Model: McFadden and Train
- Stochastic Frontiers: Greene (2004a)
- Books (see supplementary list): Lee (2002), Hsiao et al. (1999), Hsiao (2004,
- Articles and other sources: Heckman (1981a,b), Greene (2004),
Abrevaya (1997), Han, Schmidt, Greene (2004), Gong et al, (2000),
- X. Models for Data on Counts: [G,
Ch. 18.4], [W, Chs. 16, 2,
- Models for Counts: Greene (2003, Section 21.9),
Cameron and Trivedi (1986), Wedel et al. (1993), Wang et al. (1998)
- Fixed and Random Effects Models: Hausman et al.
(1984)**, Montalvo (1997)**, Allison and Waterman (2000), Greene (2003,
Chapter 21.9), Chib et al. (1998)
- General References on Count Data Models:
course home page.
Reading List: Articles and Other Sources
Abrevaya, J. "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.)
Abrevaya, J., "Leapfrog Estimation of a Fixed
Effects Model with Unknown Transformation of the Dependent Variable," Journal
of Econometrics, 93, 2, 1999, pp. 203-228.
Ahn, S. and S. Low, "A Reformulation of the Hausman Test
for Regression Models with Pooled Cross Section Time Series Data," Journal
of Econometrics, 71, 1996, pp. 291-307.
Ahn, S. and P. Schmidt, "Efficient Estimation of Models for
Dynamic Panel Data," Journal of Econometrics, 68, 1995, pp. 5-27.
Ahn, S. and P. Schmidt, "Modified Generalized Instrumental
Variables Estimation of Panel Data Models with Strictly Exogenous Instrumental
Variables," in Hsiao et al. (1999)
D. Guilkey and R. Sickles,
A Random Coefficient Probit Model with an Application to a Study of
Migration," Journal of Econometrics, 11, 1979, pp. 233-246.
Albert, J. and S. Chib, "Bayesian Analysis of Binary and
Polytomous data," Journal of The American Statistical Association,
88, 1993, pp. 669-679.
Allenby, G. and P. Rossi, "Marketing Models of Consumer
Heterogeneity," Journal of Econometrics, 89, 1999, pp. 57-78.
Allison, P. and Waterman, R. "Fixed Effects Negative Binomial
Regression Models," Department of Sociology, University of Pennsylvania,
Altman, M., J. Gill and M.
Issues in Statistical Computing for the Social Scientist, Wiley, 2004.
Amemiya, T. and T. MaCurdy, "Instrumaental Variable Estimation
of an Error Components Model," Econometrica, 1986, pp. 869-880.
Anderson, T. and C. Hsiao, Estimation of Dynamic Models with Error
Components," Journal of the American Statistical Association, 76,
1981, pp. 598-606.
Anselin, L., "Spatial Econometrics",
Chapter 14 in Baltagi, 2001.
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).
Arellano, M. and S. Bond, "Some Tests of Specification for
Panel Data: Monte Carlo Evidence and
Application to Employment Equations," Review of Economic Studies,
1991, pp. 277-297.
Arellano, M. and B Honore, "Panel Data Models: Some Recent Developments,"
Handbook of Econometrics, Volume 5, Chapter 53, 2001. (download pdf)
Arellano, M., "On the Testing of Correlated
Effects with Panel Data," Journal of Econometrics, 59, 1993, pp.
Arellano, M. and O. Bover "Another Look at the
Instrumental Variable Estimation of Error-Components Models," Journal
of Econometrics, 68, 1995, pp. 29-51.
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. and J. Griffin, "Gasoline Demand in the OECD: An
Application of Pooling and Testing Procedures," European Economic
Review, 22, 1983, pp. 117-137.
Baltagi, B. and S. Khanti-Akom, "On Efficient Estimation with Panel
Data: An Empirical Comparison of Instrumental Variable Estimators," Journal
of Applied Econometrics, 5, 1990, pp. 401-406.
Baltagi, B. and Q. Li, "Testing AR(1) against MA(1)
Disturbances in an Error Components Model," Journal of Econometrics,
68, 1995, pp. 133-151.
Baltagi, B. , S. Song, W. Koh
and B. Jung,
"Testing for Serial Correlation, Spatial Autocorrelation and Random
Effects Using Panel Data," Texas
A&M, Economics (2003). (download
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.
Berndt, E., Hall, B., Hall,
R., and Hausman, J.,
"Estimation and Inference in Nonlinear Structural Models," Annals
of Economic and Social Measurement, 3/4, 1974, pp. 653-665.
Landmark paper which presents the BHHH, BH3, or outer product of
gradients (OPG, lately called the "sandwich") estimator for the
asymptotic covariance matrix of the MLE.
Berry, S., P. Gottschalk, and D. Wissokur, "An Error Components Model of the
Impact of Plant Closings on Earnings," Review of Economics and
Statistics, 70, 1988, pp. 701-707.
Berry, S., J.
Levinsohn and A. Pakes,
"Automobile Prices in market Equilibrium," Econometrica, 63,
4, 1995, pp. 841-890.
Bhargava, A., "Wald Tests and Systems of
Stochastic Equations," International Economic Review, 1987, pp.
Bhargava, A., and J. Sargan, "Estimating Dynamic Random Effects
Models from Panel Data Covering Short Time Periods," Econometrica,
1983, pp. 695-701.
Blundell, R. and S. Bond, "Initial Conditions and Moment
Restrictions in Dynamic Panel Data Models," Journal of Econometrics,
1998, pp. 115-143.
Bond, S., "Dynamic Panel Data Models: A
Guide to Micro Data Methods and Practice," CEMMAP Working Paper CWP-09/02, 2002. (download
Breusch, T., G. Mizon and P.
Estimation Using Panel Data," Econometrica, 1989, pp. 695-701.
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. 1287-1294.
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
"Analysis of Covariance with Qualitative Data," Review of Economic
Studies, 47,1980, pp. 225-238.
and B. Carlin, "On MCMC Sampling in Hierarchical Longitudinal
Models," Statistics and Computing, 9, 1999, pp. 17-26.
Chib, S., E. Greenberg and R. Winkelmann, "Posterior Simulation and Bayes
factor in Panel Count Data Models," Journal of Econometrics, 86,
1998, pp. 33-54.
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.
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,
Dahlberg, M. and
E. Johansson, "An
Examination of the Dynamic Behavior of Local Governments Using GMM
Bootstrapping Methods," Journal of Applied Econometrics, 15, 2000,
Deb, P. and P. Trivedi, "Demand for Medical Care by the Elderly: A Finite Mixture
Approach," Journal of Applied Econometrics, 12, 3, 1997, pp.
Dempster, 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
Gong, X., A. van Soest and E.
"Mobility in the Urban Labor Market: A Panel Data Analysis for Mexico,"
IZA, Discussion paper 213, 2003. (download
C. and A. Monfort, Simulation
Based Econometrics, Oxford
University Press, New York, 1996.
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://www.stern.nyu.edu/~wgreene/panel.doc
Greene, W., "Fixed and Random Effects in
Nonlinear Models," Stern, NYU, Economics, Working Paper 01-10, 2001. (download
Greene, W., "Fixed and Random Effects in
Stochastic Frontier Models," (Forthcoming Journal of Econometrics
in 2004), Stern Department of Economics, 2002. (download
Greene, W., "Distinguishing
Between Heterogeneity and Inefficiency: Stochastic Frontier Analysis of the
World Health Organization’s Panel Data on National Health Care Systems"
Stern Department of Economics, 2004a, forthcoming, Health Economics. (download
Greene, W., The Behavior of the Fixed Effects
Estimator in Nonlinear Models, Working Paper EC-02-05, Stern School of
Business, NYU (Forthcoming, Econometric Reviews, 2004) (download
Greene, W., "Convenient Estimators for the Panel Probit
Model," Empirical Economics, 29, 2004, pp. 21-48. (Also in Baltagi,
Greene, W., "Interpreting Estimated Parameters
and Measuring Individual Heterogeneity in Random Coefficient Models,"
NYU/Stern Economics, Working Paper 04-08, May, 2004 . (download
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.
and J. Murphy,
"Estimation and Testing in the Random Effects Probit Model," Journal
of Econometrics, 59, 1993, pp. 301-317.
Hahn, J. and G. Kuersteiner, "Bias Reduction for Dynamic Nonlinear
Panel Models with Fixed Effects," MIT, Economics, 2003. (download pdf)
Hahn, J. and W. Newey, "Jackknife and Analytical Bias
Reduction for Nonlinear Panel Data Models," Manuscript, Department of
Economics, MIT, 2002. (CEMMAP,
download Working Paper 1703)
Hansen, L., "Large Sample Properties of
Generalized Method of Moments Estimators," Econometrica, 50, 1982,
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
"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.
Heckman, J. The
Incidental Parameters Problem and the Problem of Initial Conditions in
Estimating a Discrete Time - Discrete Data Stochastic Process. Structural
Analysis of Discrete Data with Econometric Applications, Manski,
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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)
Heckman, J., Statistical Models for Discrete Panel Data. Structural
Analysis of Discrete Data with Econometric Applications, Manski,
C. and McFadden D. (eds.). MIT Press:
Hildreth, C. and J. Houck, "Some Estimators for a Linear Model with Random
Coefficients," Journal of the American Statistical Association, 63,
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D., "Testing for
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1988, pp. 297-307.
Holtz-Eakin, D., W. Newey and
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1988, pp. 1371-1396.
Honore, B., "Trimmed LAD
and Least Squares Estimation of Truncated and Censored Regression Models with
Fixed Effects," Econometrica, 60, 1992, pp. 533-565.
Honore, B., "Trimmed LAD
and Least Squares Estimation of Truncated and Censored Regression Models with
Fixed Effects," Econometrica, 60, 1992, pp. 533-565.
Honore, B., "Orthogonality Conditions for Tobit
Models with Fixed Effects and Lagged Dependent Variables" Journal of
Econometrics, 59, 1993, pp. 35-61.
"Non-Linear Models with Panel Data," CEMMAP, Working paper 13-02,
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Honore, B and L. Hu. "Estimation of Cross Sectional and
Panel Data Censored Regression Models with Endogeneity" Journal of
Econometrics, 2004, forthcoming.
"Logit and Probit Models," in Matyas, L. and Sevestre, P., eds., The
Econometrics of Panel Data: Handbook of Theory and Applications, Second Revised
Edition, Kluwer Academic Publishers, Dordrecht, 1996 pp. 410-447.
Hsiao, C., "Formulation and Estimation of
Dynamic Models Using Panel Data," Journal of Econometrics, 1982,
Im, K., S.
Ahn, P. Schmidt, and J. Wooldridge, "Efficient Estimation of Panel Data
Models with Strictly Exogenous Explanatory Variables," Journal of
Econometrics, 93, 1999, pp. 177-201.
Jensen, P., M. Rosholm and M.
Comparison of Different Estimators for Panel Data Sample Selection
Models," CIM, CLS, Aarhus,
Judd, K., Numerical Methods in Econometrics,
MIT Press, 1998.
Judson, R., and A. Owen, "Estimating Dynamic Panel Data
Models: A Practical Guide for Macroeconomists," Federal Reserve
Board, 1996. (download
Kamakura, Kim and Lee, "Modeling Preference and Structural
Heterogeneity in Consumer Choice," Marketing Science, 15, 2, 1996,
Kao, C., "Spurious Regression and Residual
Based Tests for Cointegration in Panel Data," Journal of Econometrics,
90, 1999, pp. 1-44.
Keane, M. and D. Runkle, "On the Estimation of Panel Data
Models with Serial Correlation when Instruments are not Strictly Exogenous, Journal
of Business and Economic Statistics, 10, 1992, pp. 1-29. (And commentary)
Kiefer, N., "Estimation of Fixed Effects Models
for Time Series of Cross Sections with Arbitrary Intertemporal
Covariances," Journal of Econometrics, 1980, pp. 195-202.
Kitazawa, Y., "Recent Developments in Panel Data
Econometrics," Manuscript, Economics, Kyushu Sangyo
University (Japan), (download
Kyriazidou, E., "Estimation of a Panel Data Sample
Selection Model," Econometrica, 65, 1997, pp. 1335-1364.
Kyriazidou, E., "Estimation of Dynamic Panel Data
Sample Selectioin Models," Review of Economic Studies, 68, 2001,
Koop, G., J. Osiewalski and M.
efficiency analysis through individual effects: hospital cost frontiers, Journal
of Econometrics 76, 1997, pp. 77-105.
Lancaster, T. "The Incidental Parameters Problem Since 1948." Journal
of Econometrics, 2000, 95, 391-414.
Lancaster, T., "Orthogonal Parameters and Panel Data," Review of
Economic Studies, 69, 2002, pp. 647-666.
Lillard, L. and Weiss, Y., "Components of Variation in Panel
Earnings Data: American Scientists, 1960-1970," Econometrica, 47,
1979, pp. 437-454.
Lillard, L. and R. Willis, "Dynamic Aspects of Earnings
Mobility," Econometrica, 46, 1978, pp. 985-1012.
Maddala, G., "The Use of Variance Components in
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1971, pp. 341-358.
Maddala, G., "Limited Dependent Variable Models
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Manski, C., "Semiparametric Analysis of Random
Effectss Linear Models from Binary Panel Data," Econometrica, 55,
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Mazodier, P. and A. Trognon, "Heteroskedasticity and
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McFadden, D. and K. Train, "Mixed MNL Models for Discrete Response," Journal of
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Milcent, C., "Ownership, System of
Reimbursement and Mortality Relationships," Presented at the 12th European
Workshop on Econometrics and Health Economics, Menorca,
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, 1985, 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.
Newey, W., and West, K., "Hypothesis Testing with Efficient
Method of Moments Estimation," International Economic Review, 28,
1987, pp. 777-787. Useful summary of a set of estimation techniques. Shows how
the LM and Wald tests can be mimiced in the GMM class of estimators.
Neyman, J. and Scott, E. "Consistent Estimates Based on
Partially Consistent Observations." Econometrica 1948, 16,
Nickell, S., "Biases in Dynamic Models with
Fixed Effects," Econometrica, 49, 6, 1981, pp. 1417-1426.
and M. Verbeek,
"Nonresponse in Panel Data: The Impact on Estimates of Life Cycle
Consumption Function," Journal of Applied Econometrics, 7, 1992,
Passmore, W., "The GSE
Implicit Subsity and Value of Government Ambiguity," Board of Governors,
Federal Reserve System, Manuscript. (download
Rendon, S., "Fixed and Random Effects in
Classical and Bayesian Regression," University of Western Ontario,
2003. (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.
Robert, C. and G. Casella, Monte Carlo
Statistical Methods, Springer, 1999.
Rossi, P. and G. Allenby, "Bayesian Statistics and
Marketing," Marketing Science, 22, 3, 2003, pp. 304-328.
Saxonhouse, G, "Estimated Parameters as
Dependent Variables," American Economic Review, 46(1), March 1976,
Sepanski, J., "On a Random Coefficient Probit
Model," Communications in Statistics - Theory and methods, 29, 11,
2000, pp. 2493-2505.
Swamy, P. and S. Arora, "The Exact Finite Sample Properties of the Estimators of
Coefficients in the Error Components Regression Model," Econometrica,
40, 1972, pp. 261-275.
Swamy, P. and G. Tavlas, "Random Coefficient Models,"
Chapter 19 in Baltagi, 2001.
Taylor, W., "Small Sample Considerations in Estimation from Panel
Data," Journal of Econometrics, 13, 1980, pp. 203-223.
Train, K., Discrete Choice Methods with
Simulation, Cambridge University Press, 2003.
Train, K., "A Comparison of Heirarchical
Bayes and Maximum Simulated Likelihood for Mixed Logit," Economics,
Berkeley, 2003. (download pdf)
Train, K. and J. Huber, "On the Similarity of Classical and Bayesian Estimates
of Individual Mean Partworths, Marketing Letters, Vol. 12, No. 3, pp.259-269, August
Vella, F. and M. Verbeek, "Two Step Estimation of Panel Data Models with Censored Endogenous
Variables and Selection Bias," Journal of Econometrics, 90, 1999,
"On the Estimation of a Fixed Effects Model with Selectivity Bias," Economics
Letters, 34, 1990, pp. 267-270.
Verbeek, M. and T. Nijman, "Testing for Selectivity Bias in Panel Data Models," International
Economic Review, 33, 3, 1992, pp. 681-703.
Wallace, T. and A. Hussain, "The Use of Error Components Models
in Combining Cross Section and Time Series Data," Econometrica, 37,
Wang, P., I. Cockburn, and M. Puterman, "Analysis of Patent Data - A Mixed
Poisson Regression Model Approach," Journal of Business and Economic
Statistics, 16, 1, 1998, pp. 27-41.
Wedel, 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.
"Selection Corrections for Panel Data Models Under Conditional Mean Independence Assumptions," Journal of Econometrics, 68,
1995, pp. 115-132.
"Estimating Fixed and Random Effects Models with Selectivity," Economics
Letters, 40, 1992, pp. 269-272.
Wansbeek and Kapteyn, "The Separation of Individual
Variation and Systematic Change in the Analysis of Panel Data," Annales
de l'INSEE, 30, 1978, pp. 659-680.
White, H., "A Heteroscedasticity-Consistent
Covariance Matrix Estimator and Direct Test for Heteroscedasticity," Econometrica,
48, 1980, 817-838. The White estimator for unknown heteroscedasticity.
Winkelmann, R., Econometric Analysis of Count Data,
4th ed., Springer, 2005.
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