Part 1: Introduction 1-1/22 Econometrics I Professor William Greene Stern School of Business Department of Economics

Part 1: Introduction 1-2/22 Econometrics I Part 1 - Paradigm

Part 1: Introduction 1-3/22 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?

Part 1: Introduction 1-4/22 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

Part 1: Introduction 1-5/22 Model Building in Econometrics  Role of the assumptions  Parameterizing the model Nonparametric analysis Semiparametric analysis Parametric analysis  Sharpness of inferences

Part 1: Introduction 1-6/22 Application: Is there a relationship between investment and capital stock? (10 firms, 20 years)

Part 1: Introduction 1-7/22 Nonparametric Regression What are the assumptions? What are the conclusions?

Part 1: Introduction 1-8/22 Semiparametric Regression  Investment i,t = a + b*Capital i,t + u i,t  Median[u i,t | Capital i,t ] = 0

Part 1: Introduction 1-9/22 Fully Parametric Regression  Investment i,t = a + b*Capital i,t + u i,t  u i,t | Capital j,s ~ N[0,  2 ] for all i,j,s,t  I i,t |C i,t ~ N[a+bC it,  2 ]

Part 1: Introduction 1-10/22 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

Part 1: Introduction 1-11/22 Classical Inference Population Measurement Econometrics Characteristics Behavior Patterns Choices Imprecise inference about the entire population – sampling theory and asymptotics

Part 1: Introduction 1-12/22 Bayesian Inference Population Measurement Econometrics Characteristics Behavior Patterns Choices Sharp, ‘exact’ inference about only the sample – the ‘posterior’ density.

Part 1: Introduction 1-13/22 Data Structures  Observation mechanisms Passive, nonexperimental Active, experimental The ‘natural experiment’  Data types Cross section Pure time series Panel –  Longitudinal data – NLS  Country macro data – Penn W.T. Financial data

Part 1: Introduction 1-14/22 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

Part 1: Introduction 1-15/22 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

Part 1: Introduction 1-16/22 Course Objective Develop the tools needed to read about with understanding and to do empirical research using the current body of techniques.

Part 1: Introduction 1-17/22 Prerequisites  A previous course that used regression  Mathematical statistics  Matrix algebra We will do some proofs and derivations We will also examine empirical applications

Part 1: Introduction 1-18/22 Readings  Main text: Greene, W., Econometric Analysis, 7 th Edition, Prentice Hall,  A few articles  Notes and materials on the course website:

Part 1: Introduction 1-19/22

Part 1: Introduction 1-20/22 The Course Outline No class on: Thursday, September 25 Midterm: October 21 Final: Take home due December 19

Part 1: Introduction 1-21/22 Course Applications  Software NLOGIT provided, supported SAS, Stata, EViews optional, your choice R, Matlab, Gauss, others Questions and review as requested  Problem Sets: (more details later)

Part 1: Introduction 1-22/22 Course Requirements  Problem sets: 5 (25%)  Midterm, in class (30%)  Final exam, take home (45%)  Enthusiasm