ESTIMATE - Early Summer Tutorial in Modern Applied Tools of Econometrics
Course Announcement

http://econ.msu.edu/estimate

Presented by the Department of Economics at Michigan State University

June 9-11, 2017 on the campus of MSU, East Lansing, MI

Instructors:
Jeffrey Wooldridge (http://www.econ.msu.edu/faculty/wooldridge/index.php)
Timothy Vogelsang (http://www.econ.msu.edu/faculty/vogelsang/index.php)

OBJECTIVES: This is a short course in econometrics aimed at applied researchers wanting to use state-of-the-art econometrics in their empirical research. The presentation will focus on understanding when various models and estimation methods are appropriate as well as how to conduct proper inference in a variety of settings. The methods will be illustrated using several empirical examples using the econometrics package Stata. It will be assumed that participants in ÊSTIMATE have an econometrics background comparable to a first-year PhD econometrics sequence.

TOPICS:
1. Linear Models with Cross-Sectional Data
- a. Review of Ordinary Least Squares
- b. Best Linear Approximation
- c. Special Considerations with OLS: Multicollinearity, Interactions, Policy Analysis
2. Two Stage Least Squares with Cross-Sectional Data
- a. Review of Two Stage Least Squares
- b. Specification Tests
- c. Nonlinearities in Endogenous Explanatory Variables
- d. Weak Instruments
3. Introduction to Regression with Time Series Data
- a. Basics of Time Series Regression in Stationary/Weak Dependent Settings
- b. Strict and Weak Exogeneity
- c. Time Trends
- d. Serial Correlation Robust Standard Errors (aka Newey-West)
- e. Fixed-b Asymptotics
4. Finite Populations, Stratified Sampling, Cluster Sampling
- a. Finite Population Inference
- b. Standard Stratified and Variable Probability Sampling
- c. Inference with Cluster Samples
5. Difference-in-Differences Estimation
- a. Counterfactual Framework for Causal Inference
- b. The Basic Difference-in-Differences Setup
- c. General Policy Interventions
6. Linear Panel Data Models with Microeconomic Data
- a. Random Effects, Fixed Effects, Differencing. Robust Inference
- b. Correlated Random Effects. Hausman Tests
7. Linear Panel Data Models with Many Time Periods
- a. Large-T Asymptotics
- b. Robust Inference: Clustering and Driscoll-Kraay Standard Errors
- c. Link Between Individual/Time Periods Dummies and Exogeneity Assumptions
- d. Perils of Two-Way Clustering in a Panel Setting
8. Instrumental Variables Estimation with Panel Data. Unbalanced Panels
- a. FE 2SLS, RE 2SLS
- b. Dynamic Models
- c. Unbalanced Panels
9. Nonlinear Models for Cross-Sectional Data
- a. Binary Response, Fractional Response
- b. Count Data and Other Nonnegative Responses
10.Nonlinear Models with Panel Data
- a. Panel Data: Fixed Effects, Conditional MLE, Correlated Random Effects
- b. Binary Response Models
- c. Dynamic Models with Unobserved Heterogeneity

REGISTRATION: For registration and additional information please visit http://www.econ.msu.edu/estimate

Posted: 26 Jan 2017