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Econometrics I

Econometrics I. Professor William Greene Stern School of Business Department of Economics. Econometrics I. Part 1 - Paradigm. Econometrics: Paradigm. Theoretical foundations Microeconometrics and macroeconometrics

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Econometrics I

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  1. Econometrics I Professor William Greene Stern School of Business Department of Economics

  2. Econometrics I Part 1 - Paradigm

  3. Econometrics: Paradigm • Theoretical foundations • Microeconometrics and macroeconometrics • Behavioral modeling: Optimization, labor supply, demand equations, etc. • Statistical foundations • Mathematical Elements • ‘Model’ building – the econometric model • Mathematical elements • The underlying truth – is there one?

  4. Why Use This Framework? • Understanding covariation • Understanding the relationship: • Estimation of quantities of interest such as elasticities • Prediction of the outcome of interest • The search for “causal” effects • Controlling future outcomes using knowledge of relationships

  5. Model Building in Econometrics • Role of the assumptions • Parameterizing the model • Nonparametric analysis • Semiparametric analysis • Parametric analysis • Sharpness of inferences

  6. Application: Is there a relationship between investment and capital stock? (10 firms, 20 years)

  7. Nonparametric Regression What are the assumptions? What are the conclusions?

  8. Semiparametric Regression • Investmenti,t = a + b*Capitali,t + ui,t • Median[ui,t | Capitali,t] = 0

  9. Fully Parametric Regression • Investmenti,t = a + b*Capitali,t + ui,t • ui,t | Capitalj,s ~ N[0,2] for all i,j,s,t • Ii,t|Ci,t ~ N[a+bCit,2]

  10. Estimation Platforms • The “best use” of a body of data • Sample data • Nonsample information • The accretion of knowledge • 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

  11. Classical Inference Population Measurement Econometrics Characteristics Behavior Patterns Choices Imprecise inference about the entire population – sampling theory and asymptotics

  12. Bayesian Inference Population Measurement Econometrics Characteristics Behavior Patterns Choices Sharp, ‘exact’ inference about only the sample – the ‘posterior’ density.

  13. Data Structures • Observation mechanisms • Passive, nonexperimental • Active, experimental • The ‘natural experiment’ • Data types • Cross section • Pure time series • Panel – longitudinal data • Financial data

  14. 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 • Bayesian estimation – Markov Chain Monte Carlo methods

  15. Trends in Econometrics • Small structural models vs. large scale multiple equation models • Non- and semiparametric methods vs. parametric • Robust methods – GMM (paradigm shift) • Unit roots, cointegration and macroeconometrics • Nonlinear modeling and the role of software • Behavioral and structural modeling vs. “reduced form,” “covariance analysis” • Pervasiveness of an econometrics paradigm • Identification and “Causal” effects

  16. Course Objective Develop the tools needed to read about with understanding and to do empirical research using the current body of techniques.

  17. Prerequisites • A previous course that used regression • Mathematical statistics • Matrix algebra We will do some proofs and derivations We will also examine empirical applications

  18. Readings • Main text: Greene, W., Econometric Analysis, 7th Edition, Prentice Hall, 2012. • A few articles • Notes and materials on the course website: http://people.stern.nyu.edu/wgreene/Econometrics/Econometrics.htm

  19. http://people.stern.nyu.edu/wgreene/Econometrics/Econometrics.htmhttp://people.stern.nyu.edu/wgreene/Econometrics/Econometrics.htm

  20. The Course Outline No class on: Thursday, September 5 Midterm: October 22

  21. Course Applications • Software • LIMDEP/NLOGIT provided, supported • SAS, Stata, EViews optional, your choice • R, Matlab, Gauss, others • Questions and review as requested • Problem Sets: (more details later)

  22. Course Requirements • Problem sets: 5 (25%) • Midterm, in class (30%) • Final exam, take home (45%) • Enthusiasm

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