1、Econometric Analysis of Panel Data,William GreeneDepartment of EconomicsStern School of Business,Econometric Analysis of Panel Data,Overview,Panel Data Econometrics,This is an intermediate level, Ph.D. course in the area of Applied Econometrics dealing with Panel Data. The range of topics covered in
2、 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
3、individual (firm, person, etc.) effects.,Why a Course on Panel Data?,Microeconometrics and applications contemporary broad field in economics/econometricsBehavioral modelingIndividual choice and responseA platform for surveying econometric models and methods most of the fieldVarious typesRecent deve
4、lopments,Prerequisites,Econometrics I or equivalent Ph.D. level introduction to econometricsMathematical statisticsMatrix algebraWe will do some proofs and derivations.We will examine many empirical applications.You will apply the tools developed in the course.,Text Readings,Main text: Baltagi (2008
5、); read chapters 1,2Recommended: Greene (2008); read chapters 1,2,9Suggested: Wooldridge (2002); read chapters 1,2,10Very interesting: Cameron and Trivedi, Microeconometrics (Cambridge University Press, 2005.),Course Applications,Problem sets Panel data sets: See the course websiteSoftware: NLOGIT V
6、ersion 4.0Other packages SAS, StataProgramming environments: Gauss, Matlab, Mathematica, RLab workProblem setsSoftwareQuestions and review as requested,Course Requirements,Problem sets: 7 (20%)Due1. Statistics and RegressionFeb. 42. Fixed and Random EffectsFeb. 18 3. Instrumental Variables, MDE, GMM
7、March 44. Parameter Heterogeneity, RPM, HLMMarch 25 5. Nonlinear ModelsApril 86. Nonlinear Models for Panel DataApril 297. Simulation, Latent Class, Random ParametersMay 7 (Note: The last class is April 29. Problem 7 is due with the final.)Midterm, in class, (25%) Thursday, March 11Final exam (35%)D
8、istributed Thursday, April 29, due Friday, May 7Please plan aheadTerm paper/project: Application of method(s) developed in class to a live data set. Details to be given in class. (20%)Enthusiasm,Dates,No class: February 25(Th)No class: March 16(T), March 18(TH) spring breakMidterm Exam: Thursday Mar
9、ch 11Final Exam Period: April 29 May 7,http:/pages.stern.nyu.edu/wgreene/Econometrics/PanelDataEconometrics.htm,Course Outline,Econometric Analysis of Panel Data,1. Methodology,Econometrics: Paradigm,Theoretical foundationsMicroeconometrics and macroeconometricsBehavioral modelingStatistical foundat
10、ions: Econometric methodsMathematical elements: the usualModel building the econometric modelMathematical elementsThe underlying truth is there one?,Model Building in Econometrics,Role of the assumptionsSharpness of inferencesParameterizing the modelNonparametric analysisSemiparametric analysisParam
11、etric analysis,Estimation Platforms,Model basedKernels and smoothing methods (nonparametric)Moments and quantiles (semiparametric)Likelihood and M- estimators (parametric)Methodology based (?)Classical parametric and semiparametricBayesian strongly parametric,The Sample and Measurement,Population,Me
12、asurement,Theory,CharacteristicsBehavior PatternsChoices,Classical Inference,Population,Measurement,Econometrics,CharacteristicsBehavior PatternsChoices,Imprecise inference about the entire population sampling theory and asymptotics,Bayesian Inference,Population,Measurement,Econometrics,Characterist
13、icsBehavior PatternsChoices,Sharp, exact inference about only the sample the posterior density.,Data Structures,Observation mechanismsPassive, nonexperimentalActive, experimentalThe natural experimentData typesCross sectionPure time seriesPanel longitudinal dataFinancial data,Econometric Models,Line
14、ar; static and dynamicDiscrete choiceCensoring and truncationStructural models and demand systems,Estimation Methods and Applications,Least squares etc. OLS, GLS, LAD, quantileMaximum likelihoodFormal MLMaximum simulated likelihoodRobust and M- estimationInstrumental variables and GMMSimulation base
15、d estimationBayesian estimation Markov Chain Monte Carlo methodsMaximum simulated likelihoodSemiparametric and nonparametric methods based on kernels and approximations,Where Do We Go From Here?,Review of familiar classical proceduresFundamental, familiar regression extensions; common effects modelsEndogeneity, instrumental variables, GMM estimationDynamic modelsModels of heterogeneityNonlinear models that carry forward the features of the linear, static and dynamic common effects modelsRecent developments in non- and semiparametric approaches,
