econometrics presentation

Aaron Smith

Chapter 1 - introduction to econometrics.

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• Define and describe the basics of econometrics
• Describe how to do an econometric study
• Do Good Teachers Produce Better Student Outcomes?
• Does the Law of Demand Hold for Electricity?
• Is it Possible to Forecast Stock Returns?

What We Learned

• Econometrics is about making sense of economic data.
• Three steps to conducting econometric analysis. (1) State the purpose of the analysis, (2) Formulate the research design and specify the econometric model, (3) Apply statistical theory.

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ECONOMETRICS Chapter # 1: Introduction Domodar N. Gujarati

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Econometrics I

Class Notes

Professor W. Greene Department of Economics Office: MEC 7-90, Ph. 998-0876 e-mail: [email protected] Home page: http://people.stern.nyu.edu/wgreene

Abstract: This is an intermediate level, Ph.D. course in Applied Econometrics . Topics to be studied include specification, estimation, and inference in the context of models that include then extend beyond the standard linear multiple regression framework. After a review of the linear model, we will develop the asymptotic distribution theory necessary for analysis of generalized linear and nonlinear models. We will then turn to instrumental variables, maximum likelihood, generalized method of moments (GMM), and two step estimation methods. Inference techniques used in the linear regression framework such as t and F tests will be extended to include Wald, Lagrange multiplier and likelihood ratio and tests for nonnested hypotheses such as the Hausman specification test. Specific modelling frameworks will include the linear regression model and extensions to models for panel data, multiple equation models, and models for discrete choice.

Notes: The following list points to the class discussion notes for Econometrics I. These are Power Point (.pptx) files and pdf documents (.pdf).

Econometrics I

Jan 17, 2013

240 likes | 506 Views

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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• multiple equation models
• data structures
• prerequisites
• semiparametric regression
• empirical applications

Presentation Transcript

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

Econometrics I Part 1 - 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? • 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 • 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 What are the assumptions? What are the conclusions?

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

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 • 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 Population Measurement Econometrics Characteristics Behavior Patterns Choices Imprecise inference about the entire population – sampling theory and asymptotics

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

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

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

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

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)

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

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