Econometric Analysis of Panel Data

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Econometric Analysis of Panel Data

• William Greene
• Department of Economics
• Stern School of Business

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Econometric Analysis of Panel Data

• Overview

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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 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.

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Why a Course on Panel Data?

• Microeconometrics and applications contemporary
  broad field in economics/econometrics
• Behavioral modeling
• Individual choice and response
• A platform for surveying econometric models and
  methods most of the field
• Various types
• Recent developments

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Prerequisites

• Econometrics I or equivalent Ph.D. level
  introduction to econometrics
• Mathematical statistics
• Matrix algebra
• We will do some proofs and derivations.
• We will examine many empirical applications.
• You will apply the tools developed in the course.

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Text Readings

• Main text Baltagi (2008) read chapters 1,2
• Recommended Greene (2012) read chapters 1,2,11
• Suggested Wooldridge (2002) read chapters
  1,2,10
• Very interesting Cameron and Trivedi,
  Microeconometrics (Cambridge University Press,
  2005.)

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Course Applications

• Problem sets
• Panel data sets See the course website
• Software NLOGIT Version 5.0
• Other packages SAS, Stata
• Programming environments Gauss, Matlab,
  Mathematica, R
• Lab work
• Problem sets
• Software
• Questions and review as requested

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Course Requirements

• Problem sets 7 (20) Due 1. Statistics and
  Regression Feb. 9 2. Fixed and Random
  Effects Feb. 23 3. Instrumental Variables,
  MDE, GMM March 8 4. Parameter Heterogeneity,
  RPM, HLM March 29 5. Nonlinear Models April
  12 6. Nonlinear Models for Panel Data April
  24 7. Simulation, Latent Class, Random
  Parameters May 3 (Note The last class
  is May 3. Problem 7 is due with the final.)
• Midterm, in class, (25) Thursday, March 22
• Final exam (35)
• Distributed Thursday, May 3, due Friday, May 11
• Please plan ahead
• Term paper/project Application of method(s)
  developed in class to a live data set. Details
  to be given in class. (20)
• Enthusiasm

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Dates
No class April 26 (Th) No class March 13 (T),
March 15(TH) spring break Midterm Exam Thursday
March 22 Final Exam Period May 3 May 11
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http//pages.stern.nyu.edu/wgreene/Econometrics/P
anelDataEconometrics.htm
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Course Outline
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Econometric Analysis of Panel Data

• 1. Methodology

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Econometrics Paradigm

• Theoretical foundations
• Microeconometrics and macroeconometrics
• Behavioral modeling
• Statistical foundations Econometric methods
• Mathematical elements the usual
• Model building the econometric model
• Mathematical elements
• The underlying truth is there one?

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Model Building in Econometrics

• Role of the assumptions
• Sharpness of inferences
• Parameterizing the model
• Nonparametric analysis
• Semiparametric analysis
• Parametric analysis

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Estimation Platforms

• Model based
• Kernels and smoothing methods (nonparametric)
• Moments and quantiles (semiparametric)
• Likelihood and M- estimators (parametric)
• Methodology based (?)
• Classical parametric and semiparametric
• Bayesian strongly parametric

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The Sample and Measurement

Population
Measurement
Theory
Characteristics Behavior Patterns Choices
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Classical Inference

Population
Measurement
Econometrics
Characteristics Behavior Patterns Choices
Imprecise inference about the entire population
sampling theory and asymptotics
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Bayesian Inference

Population
Measurement
Econometrics
Characteristics Behavior Patterns Choices
Sharp, exact inference about only the sample
the posterior density.
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Data Structures

• Observation mechanisms
• Passive, nonexperimental
• Active, experimental
• The natural experiment
• Data types
• Cross section
• Pure time series
• Panel longitudinal data
• Financial data

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Econometric Models

• Linear static and dynamic
• Discrete choice
• Censoring and truncation
• Structural models and demand systems

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Estimation Methods and Applications

• Least squares etc. OLS, GLS, LAD, quantile
• Maximum likelihood
• Formal ML
• Maximum simulated likelihood
• Robust and M- estimation
• Instrumental variables and GMM
• Simulation based estimation
• Bayesian estimation Markov Chain Monte Carlo
  methods
• Maximum simulated likelihood
• Semiparametric and nonparametric methods based on
  kernels and approximations

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Where Do We Go From Here?

• Review of familiar classical procedures
• Fundamental, familiar regression extensions
  common effects models
• Endogeneity, instrumental variables, GMM
  estimation
• Dynamic models
• Models of heterogeneity
• Nonlinear models that carry forward the features
  of the linear, static and dynamic common effects
  models
• Recent developments in non- and semiparametric
  approaches