Description: Stern School of Business

Econometric Analysis of Panel Data

Stern School of Business

Professor William. Greene
Department of Economics
Office:;MEC 7-90, Ph. 998-0876
Home page:

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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%) 
  • 5 problem sets and exercises involving theory and estimation (20%).
  • Term paper. This will involve an 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%)


Course Materials:

  • Texts: The recommended text for the course is: Baltagi, Econometric Analysis of Panel Data, 4th Edition, Wiley, 2008. 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:

Ordered Choice Models

Models for Count Data

Discrete Choices


  • Recommended References:  Both Baltagi (2013) 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 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
    • 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., Analysis 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 students.
    • Arellano, M., Panel Data Econometrics, Oxford University Press, 2003.  The most current theoretical research and thinking on the subject.
    • 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 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, 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.
    • Frees, E., Longitudinal and Panel Data: Analysis and Applications in the Social Sciences, Cambridge University Press, 2004.
    • Matyas, L., The Econometrics of Panel Data, 3rd Ed., Springer, 2008.
    • Matyas, L and P. SevestreThe 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, 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: 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, RATS, 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 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 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.

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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, 10]
    • Topics
      • Econometric Models,
      • Benefits and Limits of Panel Data
    •   References
      • General References on Panel Data: Chamberlain (1984)
      • Recent Developments in 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]
    • Topics
      • Fixed vs. Random Effects
      • Balanced and unbalanced panels, rotating panels
      • Exogeneity
      • 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 (2001)
      • Fixed and random effects: Mundlak's approach: Mundlak (1978)
      • Chamberlain's Approach to Random Effects
    • References
      • 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], [W, Ch. 10]
    • Topics
      • 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)
    • Reference 
      • 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]
    • Topics
      • 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, Chapter 12)
    • Other References
      • Hsiao (2014, Chapter 4)
  • V.  Parameter Variation (First Generation Models), Hierarchical Models, Two Step Estimation: [G, Chs. 11.11, 15.7, 15.8] 
    • Topics
      • 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), Saxonhouse (1976)
      • The Fama-Macbeth Model:  Fama and Macbeth (1973)


  • VI. Nonlinear Models: [G, Ch. 14, App. E], [W, Chs. 12, 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 (2004)**
      • Markov Chain Monte Carlo Methods:  Casella and George (1992)
    • References
      • 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, 16.8, 19.6]
    • Topics
      • 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), Chamberlain (1984)
  • VIII. Random Parameters and Latent Class Models: [G, Chs. 14.10, 15]
    • Topics
      • 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 (2000)
  • 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.
    • Topics
      • 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 (1999)
      • The Mixed Logit Model: McFadden and Train (2000)
      • Stochastic Frontiers: Greene (2004a)
  • X. Models for Data on Counts: [G, Ch. 18.4], [W, Chs. 16, 2, 10]
    • Topics
      • 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:

          FINAL EXAM


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 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.)

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.

Anselin, L., "Spatial Econometrics," in Baltagi, B., ed., A Companion to Theoretical Econometrics, pp. 310-330 Blackwell, 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 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.

Bai, 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.

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, 2002, pp.113-131.


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., 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.

Burda, 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)

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

Chen, M., "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.

Cornwell, 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)

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.

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)

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

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, 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)

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, 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, 2003)

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.

Hensher, 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)

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., "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.

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. 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.

Kripfganz, S., and C. Schwarz, "Estimation of Linear Dynamic Panel Data Models with Time Invariant Regressors," (December 16, 2013). Available at SSRN: or (download)

Lancaster, T. "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. 357-362.

McFadden, 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)

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Tamm, 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)

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