Economics 8770 – second half

Topics in econometrics:

spatial econometrics, bootstrapping

Spring semester, 2003

Instructors: Kelly D. Edmiston and Mary Beth Walker

Contact Information:

For Edmiston, UL1217, 404-651-3519, edmiston@gsu.edu

For Walker, RCB 643, 404-651-3751, mbwalker@gsu.edu

Time and Location:

Tuesday, Thursday, 1:00 – 2:15, 203 Aderhold Learning Center

Office Hours:

Walker, Monday, 10:00 – 12:00 and Thursday, 2:00-4:00 or by appointment.

Textbooks and other materials:

For computer work, we will be using a commercial econometric package such as STATA or SAS for the straightforward applications and MatLab or GAUSS for the non-standard models that require programming.

Objective: The purpose of this course is to familiarize you with several important topics in applied econometrics. In the second half of the course, we will examine the spatial econometric model and its applications in urban and regional economics. The parametric first order spatial correlation specification as well as more recent advances in covariance matrix estimation will be covered. We will then take another look at semiparametric estimation. Finally, we will examine monte carlo simulation and bootstrapping as useful techniques in many applied situations.

Grading:

Your grade for the second half of the course will be based on a set of three homework assignments (30%) and a project (20%). There will be no exams.

Course Outline:

It is difficult to gauge the speed at which we will progress. We list the topics we hope to cover with the understanding that change may be necessary.

Week 8: The basic spatial model, the consistency of OLS estimator for conditional mean parameters and the inconsistency of the covariance matrix estimator.

Week 9: The first order spatial correlation model, ML and GMM estimation.

Week 10: Robust covariance matrix estimators.

Week 11: Testing for spatial correlation and applications of spatial models.

Week 12: Nonparametric and semiparametric estimation.

Week 13: Monte Carlo simulation, the bootstrap and bootstrap standard errors for OLS.

Week 14: The bootstrap for instrumental variable estimation and the bootstrap for use with panel data.

Readings for the second half

For spatial econometrics:

Anselin, Luc, Anil K. Bera, Raymond Florax and Mann J. Yoon (1996), “Simple Diagnostic Tests for Spatial Dependence,” Regional Science and Urban Economics, 26, 77-104.

Case, Anne C. (1991), “Spatial Patterns in Household Demand,” Econometrica, 59, 953-966.

Conley, Timothy G. (1999), “GMM estimation with cross sectional dependence,” Journal of Econometrics, 92, 1-45.

Driscoll, John C. and Aart C. Kraay (1998), “Consistent Covariance Matrix Estimation with Spatially Dependent Data,” The Review of Economics and Statistics, 80, 549-560.

Kelejian, Harry and Ingmar Prucha (1999), “A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model” International Economic Review, 40, 509-535.

For bootstrapping:

Brownstone, David and Robert Valletta, “The Bootstrap and Multiple Imputations: Harnessing Increased Computing Power for Improved Statistical Tests.” The Journal of Economic Perspectives, 15, 129-41.