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Econometrics I

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Econometrics I

Professor William Greene

Stern School of Business

Department of Economics

Econometrics I

Part 1 - 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?

- 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.htm

No class on: Thursday, September 5

Midterm: October 22

- 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)

- Problem sets: 5 (25%)
- Midterm, in class (30%)
- Final exam, take home (45%)
- Enthusiasm