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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

Econometrics I

Professor William Greene

Stern School of Business

Department of Economics


Econometrics i1

Econometrics I

Part 1 - Paradigm


Econometrics paradigm
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?


Why use this framework
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


Model building in econometrics
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)


Nonparametric regression
Nonparametric Regression

What are the assumptions? What are the conclusions?


Semiparametric regression
Semiparametric Regression

  • Investmenti,t = a + b*Capitali,t + ui,t

  • Median[ui,t | Capitali,t] = 0


Fully parametric regression
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]


Estimation platforms
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


Classical inference
Classical Inference

Population

Measurement

Econometrics

Characteristics

Behavior Patterns

Choices

Imprecise inference about the entire population – sampling theory and asymptotics


Bayesian inference
Bayesian Inference

Population

Measurement

Econometrics

Characteristics

Behavior Patterns

Choices

Sharp, ‘exact’ inference about only the sample – the ‘posterior’ density.


Data structures
Data Structures

  • Observation mechanisms

    • Passive, nonexperimental

    • Active, experimental

    • The ‘natural experiment’

  • Data types

    • Cross section

    • Pure time series

    • Panel – longitudinal data

    • Financial data


Estimation methods and applications
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


Trends in econometrics
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


Course objective
Course Objective

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


Prerequisites
Prerequisites

  • A previous course that used regression

  • Mathematical statistics

  • Matrix algebra

    We will do some proofs and derivations

    We will also examine empirical applications


Readings
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


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


The course outline
The Course Outlinehttp://people.stern.nyu.edu/wgreene/Econometrics/Econometrics.htm

No class on: Thursday, September 5

Midterm: October 22


Course applications
Course Applicationshttp://people.stern.nyu.edu/wgreene/Econometrics/Econometrics.htm

  • 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)


Course requirements
Course Requirementshttp://people.stern.nyu.edu/wgreene/Econometrics/Econometrics.htm

  • Problem sets: 5 (25%)

  • Midterm, in class (30%)

  • Final exam, take home (45%)

  • Enthusiasm


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