Statistics
Econometrics IProfessor William GreeneStern School of BusinessDepartment of Economics
Part 1: Introduction1-#/221Econometrics IPart 1 - ParadigmPart 1: Introduction1-#/222Econometrics: ParadigmTheoretical foundationsMicroeconometrics and macroeconometricsBehavioral modeling: Optimization, labor supply, demand equations, etc.Statistical foundationsMathematical ElementsModel building the econometric modelMathematical elementsThe underlying truth is there one?Part 1: Introduction1-#/223Why Use This Framework?Understanding covariationUnderstanding the relationship:Estimation of quantities of interest such as elasticitiesPrediction of the outcome of interestThe search for causal effectsControlling future outcomes using knowledge of relationshipsPart 1: Introduction1-#/224Model Building in EconometricsRole of the assumptionsParameterizing the modelNonparametric analysisSemiparametric analysisParametric analysisSharpness of inferencesPart 1: Introduction1-#/225
Application: Is there a relationship between investment and capital stock? (10 firms, 20 years)
Part 1: Introduction1-#/226Nonparametric RegressionWhat are the assumptions? What are the conclusions?
Part 1: Introduction1-#/227Semiparametric RegressionInvestmenti,t = a + b*Capitali,t + ui,tMedian[ui,t | Capitali,t] = 0
Part 1: Introduction1-#/228Fully Parametric RegressionInvestmenti,t = a + b*Capitali,t + ui,tui,t | Capitalj,s ~ N[0,2] for all i,j,s,tIi,t|Ci,t ~ N[a+bCit,2]
Part 1: Introduction1-#/229Estimation PlatformsThe best use of a body of dataSample dataNonsample informationThe accretion of knowledgeModel basedKernels and smoothing methods (nonparametric)Moments and quantiles (semiparametric)Likelihood and M- estimators (parametric)Methodology based (?)Classical parametric and semiparametricBayesian strongly parametricPart 1: Introduction1-#/2210Classical Inference Population Measurement EconometricsCharacteristicsBehavior PatternsChoicesImprecise inference about the entire population sampling theory and asymptoticsPart 1: Introduction1-#/2211Bayesian Inference Population Measurement EconometricsCharacteristicsBehavior PatternsChoicesSharp, exact inference about only the sample the posterior density.Part 1: Introduction1-#/2212Data StructuresObservation mechanismsPassive, nonexperimentalActive, experimentalThe natural experimentData typesCross sectionPure time seriesPanel Longitudinal data NLSCountry macro data Penn W.T.Financial dataPart 1: Introduction1-#/2213Estimation Methods and ApplicationsLeast squares etc. OLS, GLS, LAD, quantileMaximum likelihoodFormal MLMaximum simulated likelihoodRobust and M- estimationInstrumental variables and GMMBayesian estimation Markov Chain Monte Carlo methodsPart 1: Introduction1-#/2214Trends in EconometricsSmall structural models vs. large scale multiple equation modelsNon- and semiparametric methods vs. parametric Robust methods GMM (paradigm shift)Unit roots, cointegration and macroeconometricsNonlinear modeling and the role of softwareBehavioral and structural modeling vs. reduced form, covariance analysis Pervasiveness of an econometrics paradigmIdentification and Causal effectsPart 1: Introduction1-#/2215Course Objective Develop the tools needed to read about with understanding and to do empirical research using the current body of techniques.Part 1: Introduction1-#/2216PrerequisitesA previous course that used regressionMathematical statisticsMatrix algebra
We will do some proofs and derivations We will also examine empirical applicationsPart 1: Introduction1-#/2217ReadingsMain text: Greene, W., Econometric Analysis, 7th Edition, Prentice Hall, 2012.A few articlesNotes and materials on the course website:
http://people.stern.nyu.edu/wgreene/Econometrics/Econometrics.htmPart 1: Introduction1-#/2218
http://people.stern.nyu.edu/wgreene/Econometrics/Econometrics.htmPart 1: Introduction1-#/22The Course OutlineNo class on: Thursday, September 25
Midterm: October 21Final: Take home due December 19
Part 1: Introduction1-#/2220Course ApplicationsSoftwareNLOGIT provided, supportedSAS, Stata, EViews optional, your choiceR, Matlab, Gauss, othersQuestions and review as requestedProblem Sets: (more details later)Part 1: Introduction1-#/2221Course RequirementsProblem sets: 5 (25%)Midterm, in class (30%)Final exam, take home (45%)EnthusiasmPart 1: Introduction1-#/2222
