Description: Stern School of Business

Econometric Analysis of Panel Data

Stern School of Business / B55.9912

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

Description: prev0.gifReturn to course home page.

Abstract: This is an intermediate level, Ph.D. course in the area of  Applied Econometrics dealing with Panel Data.  The range of topics covered in the course will span a large part of econometrics generally, though we are particularly interested in those techniques as they are adapted to the analysis of 'panel' or 'longitudinal' data sets.  Topics to be studied include specification, estimation, and inference in the context of models that include individual (firm, person, etc.) effects.  We will  begin with a development of the standard linear regression model, then extend it to panel data settings involving 'fixed' and 'random' effects.  The asymptotic distribution theory necessary for analysis of generalized linear and nonlinear models will be reviewed or developed as we proceed.. We will then turn to instrumental variables, maximum likelihood, generalized method of moments (GMM), and two step estimation methods.  The linear model will be extended to dynamic models and recently developed GMM and instrumental variables techniques.   The classical methods of maximum likelihood and GMM and Bayesian methods, especially MCMC techniques, are applied to models with individual effects.  The last third of the course will focus on nonlinear models.  Theoretical developments will focus on heterogeneity in models including random parameter variation, latent class (finite mixture) and 'mixed' and hierarchical models.  We will also visit the theory for  techniques for optimization in the setting of nonlinear models.  We will consider numerous applications from the literature, including static and dynamic regression models, heterogeneous parameters models, random parameter variation, and specific nonlinear models such as binary and multinomial choice and models for count data.

Prerequisites: Multivariate calculus, matrix algebra, probability and distribution theory, statistical inference, and a previous course in Econometrics at the level of Greene (7th ed, 2012) are assumed..

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

  • Midterm examination (25%)
  • Take home final exam (35%) 
  • 7 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 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 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, 2012.
  • 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 choice models:

Ordered Choice Models

Models for Count Data

Discrete Choices

  • Recommended 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 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 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, 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. SevestreThe 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).  The 14th 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, 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, R, Gauss, MatLab, RATS, TSP, 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 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.

Description: prev0.gifReturn to 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, 10]
    • Topics
      • Econometric Models,
      • Benefits and Limits of Panel Data
    •   References
      • 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]
    • 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)
      • Fixed and random effects: Mundlak’s approach: Mundlak (1978)
      • Chamberlain's Approach to Random Effects: Chamberlain (1984)
    • References
      • General Discussion of FEM and REM Hsiao (2004, Chapter 2, 3, 4 to Section 4.5)
      • More In Depth Treatment of General Issues in Panel DataChamberlain (1984)
  • III. Extensions: Heteroscedasticity, Autocorrelation, Robust Estimation: [B, Ch. 5,10.5], [G, Ch. 11], [W, Ch. 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 (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]
    • 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)
    • Other References
      • Hsiao (2004, Chapter 4)
  • V.  Parameter Variation (First Generation Models), Hierarchical Models, Two Step Estimation: [G, Chs. 11.11, 15.7, 15.8] 
    • Topics
      • Parameter Heterogeneity: Swamy and Tavlas (2001)
      • Cross Section Variation in Parameters:  Hsiao (2004)
      • 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 (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 (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: 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, 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]
    • 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: [G, Chs. 14.10, 15]
    • 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: [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)**, 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 (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: [G, Ch. 18.4], [W, Chs. 16, 2, 10]
    • 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:


                          FINAL EXAM


Description: prev0.gifReturn to 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)

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

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, 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)  (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.

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)

Heckman, J
., Statistical Models for Discrete Panel Data. Structural Analysis of Discrete Data with Econometric Applications, Manski, C. and McFadden D. (eds.). MIT Press: 1981b.

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., "Testing for Individual Effects in Autoregressive Models," Journal of Econometrics, 1988, pp. 297-307.

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.

Hsiao, C., "Formulation and Estimation of Dynamic Models Using Panel Data," Journal of Econometrics, 1982, pp. 47-82.

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, 4th ed., Springer, 2005.

Description: prev0.gifReturn to course home page.