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Applied Econometrics

Applied Econometrics. 1. The Paradigm of Econometrics. Econometrics: Paradigm. Theoretical foundationsMicroeconometrics and macroeconometricsBehavioral modeling: Optimization, labor supply, demand equations, etc.Statistical foundationsMathematical Elements?Model' building ? the econometric modelMathematical elementsThe underlying truth ? is there one?.

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Applied Econometrics

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    1. Applied Econometrics William Greene Department of Economics Stern School of Business

    2. Applied Econometrics 1. The Paradigm of Econometrics

    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 Controlling future outcomes using knowledge of relationships

    5. Measurement as Observation

    6. Inference

    7. Model Building in Econometrics Role of the assumptions Parameterizing the model Nonparametric analysis Semiparametric analysis Parametric analysis Sharpness of inferences Application: Is there a relationship between investment and capital stock? (10 firms, 20 years)

    9. Nonparametric Regression

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

    11. Parametric Regression Investmenti,t = a + b*Capitali,t + ui,t ui,t | Capitalj,s ~ N[0,?2] for all i,j,s,t

    12. Estimation Platforms The “best use” of a body of data 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

    13. Classical Inference

    14. Bayesian Inference

    15. Empirical Research Iterative search for information about the structure Specification searches Stepwise modeling, data mining, etc. Leamer on specification searching and significance levels Judge, et al. on sequential inference and pretesting Hendry on the encompassing principle – “general to specific” Classical estimation and inference

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

    17. Econometric Models Linear; static and dynamic Discrete choice Censoring and truncation Structural models and demand systems

    18. 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

    19. Trends in Econometrics Small structural models vs. large scale multiple equation models Parametric vs. non- and semiparametric methods Robust methods – GMM (paradigm shift?) Unit roots, cointegration and macroeconometrics Nonlinear modeling and the role of software Behavioral and structural modeling vs. “covariance analysis” – pervasiveness of the econometrics paradigm

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

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

    22. The Course Outline Topics Sequence Timing

    23. Readings Main text: Greene, W., Econometric Analysis, 7th Edition, Prentice Hall, 2011. A few articles Notes on the course website: http://www.stern.nyu.edu/~wgreene/Econometrics /Econometrics.htm

    24. Course Applications Software LIMDEP/NLOGIT provided, supported SAS, Stata optional, your choice Gauss, Matlab, others Problem sets Software Questions and review as requested Term Paper – Application (more details later)

    25. Course Requirements Problem sets: approximately 6 (15%) Midterm, in class (30%) Final exam (40%) Term paper/project: Application of method(s) developed in class to a ‘live’ data set. (15%) Enthusiasm

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