Part 1 introduction bayesian inference population

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Part 1: Introduction Bayesian Inference     Population    Measurement   Econometric s Characteristics Behavior Patterns Choices Sharp, ‘exact’ inference about  only the sample – the ‘posterior’  density. ™    11/21
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Part 1: Introduction Data Structures p Observation mechanisms n Passive, nonexperimental n Active, experimental n The ‘natural experiment’ p Data types n Cross section n Pure time series n Panel – longitudinal data n Financial data ™    12/21
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Part 1: Introduction Estimation Methods and Applications p Least squares etc. – OLS, GLS, LAD, quantile p Maximum likelihood n Formal ML n Maximum simulated likelihood n Robust and M- estimation p Instrumental variables and GMM p Bayesian estimation – Markov Chain Monte Carlo methods ™    13/21
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Part 1: Introduction Trends in Econometrics p Small structural models vs. large scale multiple equation  models p Non- and semiparametric methods vs. parametric  p Robust methods – GMM (paradigm shift?) p Unit roots, cointegration and macroeconometrics p Nonlinear modeling and the role of software p Behavioral and structural modeling vs. “reduced form,”  “covariance analysis”  p Pervasiveness of an econometrics paradigm p Identification and “Causal” effects ™    14/21
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Part 1: Introduction Course Objective          Develop the tools needed to read about  with understanding and to do empirical  research using the current body of  techniques.
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