Econometric Analysis of Panel Data

Stern School of Business / B55.9912

Professor William. Greene Department of Economics Office:;MEC 7-78, Ph. 998-0876, Fax. 995-4218 e-mail: wgreene@stern.nyu.edu Home page: http://www.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, expecially 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 (e.g., Fama-Macbeth), 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(5th ed, 2003) are assumed..

Course Requirements: Grades for the course will be based on:

• Midterm examination (25%)
• Take home final exam (35%)
• Several problem sets and small exercises involving theory and estimation (20%).
• Term paper. This will involve a serious application of the techniques developed in the course applied to a 'real' data set, either from the studen's own research or obtained from one of the many publicly available archives on the internet. (20%)

• Text: The required text for the course is: Wooldridge, J., Econometric Analysis of Cross Section and Panel Data, MIT Press, 1999.
• 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, 5th Ed., Prentice Hall, 2003. Copies of a few relevant chapters of Greene 5/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. A very useful volume for a quick introduction to 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 texts.
• Recommended References: Both Baltagi (2001) 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).
• 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 two 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 book 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, 2nd Edition, Wiley, 2001. This is the canonical reference for researchers Algebraically terse and dense, but definitely complete on analysis of the linear model.
  • Hsiao, C., Analysis of Panel Data, Cambridge University Press, 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 panel data.
  • Baltagi, B. ed., A Companion to Theoretical Econometrics, Blackwell, 2001. Not specifically about panel data, but a very useful book for students.
  • Arellano, M., Panel Data Econometrics, Oxford University Press, 2003. The most current research and thinking on the subject.
  • Baltagi, B., Ed., Recent Developments in the Econometrics of Panel Data, 2004, http://econweb.tamu.edu/baltagi/recent_developments.htm.
  • 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 estimation.
  • 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.
  • Matyas, L and P. Sevestre. The Econometrics of Panel Data, Revised 2nd ed., Kluwer Academic, 1996. Large, extremely interesting collection of essays on many topics. A new edition to be published any day now.
  • 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.
  • Conferences: In addition to these books and monographs, we note, there have been ten worldwide conferences specifically on the subject of panel data. The most recent was held at Texas A&M University on June 4-6, 2004. (Panel Data Conference).
  • 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, 1982
    • 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, Gauss, MatLab, RATS, TSP, EViews) contain packages of routines for analysis of panel data. Some are more complete than others. Students may use any computer software that they are familiar with for their course work. 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. Ph.D. students who wish to use LIMDEP and do not already have access to it should contact me early in the semester. Stern has a site license for Stata for those who prefer to use it. Finally, for the serious analyst who is facile with Gauss, Manuel Arellano has released a package of Gauss routines for dynamic panel data modeling (DPD). Many authors have picked these up and positioned them alone or with other software (such as OX). These routines are distributed free on the web, and are easy to find with, e.g., Google. A warning, they are not simple to use. I will not be presenting instruction on software usage during the semester. From time to time, however, we will meet in separate lab sessions to discuss applications and methods. We can discuss some aspects of the specific programs in those meetings.
• 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 Sets."
• 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 Outline and Schedule:

Assignments with the section topic are from Wooldridge. 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: Wooldridge, Chapters 1, 2, 10
  • Topics
    • Econometric Models,
    • Benefits and Limits of Panel Data
  • References
    • General References on Panel Data: Baltagi (2001, Chapter 1), Chamberlain (1984), Hsiao (2004, Chapter 1), Greene (2003, Chapter 13)
    • Recent Developments in Theory and Application: Arellano and Honore (2001), Kitazawa (2000)
• II. Fixed and Random Effects: Wooldridge, Chapters 7, 10 and 11
  • Topics
    • Fixed vs. Random Effects: Baltagi and Griffin (1983)
    • Balanced and unbalanced panels, rotating panels
    • Exogeneity: Mundlak (1978), Lillard and Willis (1978)
    • Estimation methods: OLS, GLS, FGLS, MLE: Swamy and Arora (1972), Wallace and Hussain (1969), Balestra and Nerlove (1966), Taylor (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)
    • Chamberlain's Approach to Random Effects: Chamberlain (1984)
  • References
    • General Discussion of FEM and REM Baltagi (2001,Chapters 2-4), Hsiao (2004, Chapter 2, 3, 4 to Section 4.5), Greene (2003, Chapter 13)
    • More In Depth Treatment of General Issues in Panel Data: Chamberlain (1984)
• III. Extensions: Heteroscedasticity, Autocorrelation, Robust Estimation: Wooldridge, Chapter 10
  • Topics
    • 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)
  • Reference
    • General discussion of nonspherical disturbances: Baltagi (2001, Chapter 5, 10.5)
• IV. Instrumental Variables and GMM Estimation, Dynamic Models, Time Series Application: Wooldridge, Chapters 5, 8, 11.3, 14
  • Topics
    • 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)
  • References
    • Baltagi (2001, Chapter 8 and 12), Greene (2003,Chapter 16), Hsiao (2004, Chapter 4)
• V. Parameter Variation (First Generation Models), Hierarchical Models, Two Step Estimation
  • Topics
    • Parameter Heterogeneity: Swamy and Tavlas (2001)
    • Cross Section Variation in Parameters: Hsiao (2004)
    • GLS and FGLS Estimation, Hsiao (2004), Greene (2003), Hildreth and Houck (1968)
    • Hierarchical Models - Random Parameters with Heterogeneous Means: Craig, Douglas, Greene (2003), Greene (2004a)
    • Two Step Estimation: Passmore (2004), Saxonhouse (1976), Milcent (2003)
    • The Fama-Macbeth Model: Fama and Macbeth (1973)

• VI. Nonlinear Models: Wooldridge, Chapters 12 and 13.
  • Topics
    • 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), Abrevaya (1999)
    • Markov Chain Monte Carlo Methods: Casella and George (1992), Chib and Carlin (1999)
  • References
    • General references on optimization: Greene (2003, Chapter 16), 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 techniques.
    • Simulation based estimation: Robert and Casella (1999), Train (2003).
    • Econometric software (Stata, SAS, 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: Wooldridge, Chapter 15.8, 16.8, 19.6
  • Topics
    • 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), Chamberlain (1984)
• VIII. Random Parameters and Latent Class Models
  • Topics
    • 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: Wooldridge, Chapter 15.8, 16.8, 17.7, 18.5
  • Topics
    • 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)
    • 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 (2000)
    • Stochastic Frontiers: Greene (2004a)
  • References
    • Books (see supplementary list): Lee (2002), Hsiao et al. (1999), Hsiao (2004, Chapters 7,8)
    • Articles and other sources: Heckman (1981a,b), Greene (2004), Abrevaya (1997), Han, Schmidt, Greene (2004), Gong et al, (2000), Maddala (1987)
• X. Models for Data on Counts: Wooldridge Chapter 19.6
  • Topics
    • 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:
    • Winkelmann (2000), Cameron and Trivedi (1998)
    • Useful website: http://www.econ.ucdavis.edu/faculty/cameron/racd/count.html

Return to course home page.

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)

Akin, J., 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, 2000. (download pdf)

Altman, M., J. Gill and M. McDonald, Numerical 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. 87-97.

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 pdf)

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. 789-808.

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 pdf)

Breusch, T., G. Mizon and P. Schmidt, "Efficient 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 pdf)

Chamberlain, G., "Analysis of Covariance with Qualitative Data," Review of Economic Studies, 47,1980, pp. 225-238.

Chib, S. 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, NYU. (download doc file)

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.

Deb, 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.

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 pdf)

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)

Gourieroux, 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 pdf)

Greene, W., "Fixed and Random Effects in Stochastic Frontier Models," (Forthcoming Journal of Econometrics in 2004), Stern Department of Economics, 2002. (download pdf)

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 pdf)

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 pdf)

Greene, W., "Convenient Estimators for the Panel Probit Model," Empirical Economics, 29, 2004, pp. 21-48. (Also in Baltagi, 2004) (download pdf)

Greene, W., "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, "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, 2003) (download pdf)

Griliches, Z., and J. Hausman, "Errors in Variables in Panel Data," Journal of Econometrics, 31, 1986, pp. 93-118.

Guilkey, D., 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, pp. 1029-1054.

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.

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, C. and McFadden D. (eds.). MIT Press: 1981a.

Hildreth, C. and J. Houck, "Some Estimators for a Linear Model with Random Coefficients," Journal of the American Statistical Association, 63, 1968, pp. 584-595.

Holtz-Eakin, D., W. Newey and H. Rosen, "Estimating Vector Autoregressions with Panel Data," Econometrica, 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.

Honore, B., "Non-Linear Models with Panel Data," CEMMAP, Working paper 13-02, 2002. (download pdf)

Honore, B and E. Kyriazidou, "Panel Data Discrete Choice MOdels with Lagged Dependent Variables," Econometrica, 68, 2000, pp. 839-874.

Honore, B and L. Hu. "Estimation of Cross Sectional and Panel Data Censored Regression Models with Endogeneity" Journal of Econometrics, 2004, forthcoming.

Hsiao, C, "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.

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. 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.

Judson, R., and A. Owen, "Estimating Dynamic Panel Data Models: A Practical Guide for Macroeconomists," Federal Reserve Board, 1996. (download pdf)

Kamakura, Kim and Lee, "Modeling Preference and Structural Heterogeneity in Consumer Choice," Marketing Science, 15, 2, 1996, pp. 152-172.

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 pdf)

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, pp. 543-572.

Koop, G., J. Osiewalski and M. Steel, Bayesian 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 Pooling Cross Section and Time Series Data," Econometrica, 39, 1971, pp. 341-358.

Maddala, G., "Limited Dependent Variable Models Using Panel Data," Journal of Human Resources, 22, 3, 1987, pp. 307-338.

Manski, C., "Semiparametric Analysis of Random Effectss Linear Models from Binary Panel Data," Econometrica, 55, 1987, pp. 357-362.

Mazodier, P. and A. Trognon, "Heteroskedasticity and Stratification in Error Components Models," Annales de l'INSEE, 30, 1978), pp. 451-482.

McFadden, D. and K. Train, "Mixed MNL Models for Discrete Response," Journal of Applied Econometrics, 15, 2000, pp. 447-470.

Milcent, C., "Ownership, System of Reimbursement and Mortality Relationships," Presented at the 12th European Workshop on Econometrics and Health Economics, Menorca, Sept., 2003.

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, 1-32.

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.

Passmore, W., "The GSE Implicit Subsity and Value of Government Ambiguity," Board of Governors, Federal Reserve System, Manuscript. (download pdf)

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, 178-183.

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 2001.

Vella, 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.

Verbeek, M., "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, pp. 55-72.

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.

Woolridge, J., "Selection Corrections for Panel Data Models Under Conditional Mean Independence Assumptions," Journal of Econometrics, 68, 1995, pp. 115-132.

Zabel, J., "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, 3rd ed., Springer, 2000.