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Econometrics Q-Exam Syllabus

The 2013 exam will cover the same material as the 6190 and 6200 lectures. The corresponding syllabi are reproduced below.

Probability Theory and Statistics for Economists

Required textbook: Statistical Inference, 2nd Edition, George Casella and Roger Berger, 2002.

1 Introduction to Statistics and Econometrics

2 Foundation of Probability Theory

  • Random Experiments
  • Basic Concepts of Probability
  • Review of Set Theory
  • Fundamental Probability Laws
  • Methods of Counting
  • Conditional Probability and Independence

3 Random Variables and Univariate Probability Distributions

  • Random Variables and Distribution Functions
  • Discrete Random Variable
  • Continuous Random Variables
  • Functions of a Random Variable
  • Mathematical Expectations
  • Moment Generating Function
  • Characteristic Function

4 Important Parametric Distributions

  • Introduction
  • Discrete Distributions
  • Continuous Probability Distributions

5 Random Vectors and Multivariate Probability Distribution

  • Random vectors and Joint Probability Distributions
  • Marginal Distributions
  • Conditional Distributions
  • Independence
  • Empirical Applications
  • Bivariate Transformation
  • Expectations Under Multivariate Distributions
  • Implications of Independence
  • Conditional Expectations

6 Introduction to Sampling Theory and Statistics

  • Population and Random Sample
  • The Sampling Distribution of the Sample Mean
  • The Sampling Distribution of the Sample Variance
  • Student s t Distribution
  • Snedecor s F Distribution
  • Sufficient Statistics

7 Convergence Concepts and Limit Theories

  • Limits and Orders of Magnitude: A Review
  • Motivation for Convergence Concepts
  • Convergence in Quadratic Mean and Lp-convergence
  • Convergence in Probability
  • Almost Sure Convergence
  • Convergence in Distribution

8 Parameter Estimation and Evaluation

  • Population and Distribution Model
  • Maximum Likelihood Estimation
  • Method of Moments and Generalized Method of Moments
  • Mean Squared Error Criterion
  • Best Unbiased Estimators

9 Hypothesis Testing

  • Introduction to Hypothesis Testing
  • The Wald Test
  • The Lagrangian Multiplier Test
  • The Likelihood Ratio Test
  • A Simple Example

Principles of Estimation and Inference

Required textbook: Econometrics. Hayashi, F., Princeton University Press (2000)

1 Ordinary Least Squares

  • Definition and finite sample properties
  • Large sample properties in rather general setting.
  • Robust test statistics

2 Generalized Method of Moments

  • Overview, linear single-equation GMM
  • OLS and IV as special cases
  • Linear single equation
  • Hypothesis and specification tests
  • 2SLS as special case
  • Multiple-equation GMM.
  • SUR, FIVE, and RE as special cases
  • Panel data: a GMM perspective
  • FE, RE, first-difference as special cases

3 Extremum Estimators

  • Identification: A more formal approach
  • Extremum estimators. Overview, consistency, asymptotic normality
  • Hypothesis testing: the trinity
  • Maximum likelihood: some applications

4 The Bootstrap

  • What is the bootstrap?
  • When does it work?
  • When does it improve on asymptotic approximation?
  • Bootstrap confidence regions and bootstrap bias correction

[revised: February 2013]

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