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# Econometrics I - PowerPoint PPT Presentation

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

Professor William Greene

Department of Economics

### Econometrics I

• 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?

• 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

• 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)

What are the assumptions? What are the conclusions?

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

• Median[ui,t | Capitali,t] = 0

• 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]

• 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

Population

Measurement

Econometrics

Characteristics

Behavior Patterns

Choices

Imprecise inference about the entire population – sampling theory and asymptotics

Population

Measurement

Econometrics

Characteristics

Behavior Patterns

Choices

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

• Observation mechanisms

• Passive, nonexperimental

• Active, experimental

• The ‘natural experiment’

• Data types

• Cross section

• Pure time series

• Panel – longitudinal data

• Financial data

• 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

• 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

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

• A previous course that used regression

• Mathematical statistics

• Matrix algebra

We will do some proofs and derivations

We will also examine empirical applications

• 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 Outlinehttp://people.stern.nyu.edu/wgreene/Econometrics/Econometrics.htm

No class on: Thursday, September 5

Midterm: October 22

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

• Problem sets: 5 (25%)

• Midterm, in class (30%)

• Final exam, take home (45%)

• Enthusiasm