Econometrics

General Information

PREREQUISITES

ECON 2020: Principles of Microeconomics
ECON 2030: Principles of Macroeconomics
BUSN 2750: Introduction to Statistics

READINGS

J. Wooldridge (2017). Introductory Econometric.

COURSE POLICIES / ADDITIONAL INFORMATION

Attendance is recorded. Unexcused absence lowers the grade accordingly.
It is the students' responsibility to check Canvas and their Webster email regularly.
For every hour spent in class it is expected of the students to invest 3 hours per week outside of class in homework, reviewing material, assignments, etc.

UNIVERSITY POLICIES

Students are required to inform themselves of WUV academic policies.

A full list of these policies is available on the WUV website: http://webster.ac.at/academic-policies

Materials, Assignments and R-Code

Principles of Statistics [Slides][RCode]

The Simple Regression Model [Slides][RCode]

Multiple Regression Analysis: Estimation [Slides][RCode]

Multiple Regression Analysis: Inference [Slides][RCode]

Multiple Regression Analysis: OLS Asymptotics [Slides][RCode]

Maximum Likelihood Estimation [Slides]

Multiple Regression Analysis: Further Issues [Slides][RCode]

Binary Response Analysis [Slides][RCode]

Heteroskedasticity [Slides][RCode]

More on Specification and Data Issues [Slides][RCode]

Difference-in-Differences Estimation [RCode]

Instrumental Variable Estimation [RCode]

Weekly Schedule

Week 1

Principles of Linear Algebra

· Types of Matrices

· Operations

· Properties

· Decompositions

Principles of Statistics

· Statistical Moments

· Probability Distributions

· Law Of Large Numbers

· Central Limit Theorem

Week 2

The Simple Regression Model

· Definition of the Simple Regression Model

· Deriving the Ordinary Least Squares Estimates

· Properties of OLS on Any Sample of Data

· Expected Values and Variances of the OLS Estimators

The Simple Regression Model

· Regression through the Origin and Regression on a Constant

Multiple Regression Analysis: Estimation

· Mechanics of Ordinary Least Squares

· The Expected Value of the OLS Estimators

Week 3

Multiple Regression Analysis: Estimation

· The Variance of the OLS Estimators

· Efficiency of OLS. The Gauss-Markov Theorem

Multiple Regression Analysis: Inference

· Sampling Distributions of the OLS Estimators

· Testing Hypotheses about a Single Population Parameter

· Confidence Intervals

Week 4

Multiple Regression Analysis: Inference

· Testing Hypotheses about Multiple Parameters

· Testing Multiple Linear Restrictions: The F Test

· Reporting Regression Results

Multiple Regression Analysis: OLS Asymptotics

· Consistency

· Asymptotic Normality and Large Sample Inference

· Asymptotic Efficiency of OLS

Week 5

Mid-Term Exam

Maximum Likelihood Estimation

· Likelihood Function

· Maximum Likelihood Estimation

· Test Statistics

· Model Selection Criteria

Week 6

Multiple Regression Analysis: Further Issues

· Effects of Data Scaling on OLS Statistics

· More on Functional Form

· More on Goodness-of-Fit and Selection of Regressors

· Prediction and Residual Analysis

Multiple Regression Analysis with Qualitative Information: Binary

· A Single Dummy Independent Variable

· Using Dummy Variables for Multiple Categories

· Interactions Involving Dummy Variables

Week 7

Multiple Regression Analysis with Qualitative Information: Binary

· Binary Dependent Variable: The Linear Probability Model

· Probit

· Logit

Heteroskedasticity

· Consequences of Heteroskedasticity

· Heteroskedasticity-Robust Inference after OLS Estimation

· Testing for Heteroskedasticity

· Weighted Least Square Estimation

Week 8

More on Specification and Data Issues

· Functional Form Misspecification

· Using Proxy Variables for Unobserved Explanatory Variables

· Properties of OLS under Measurement Error

· Least Absolute Deviations Estimation

Final Exam

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