HS 409a. Advanced Econometrics

Fall 2014

HellerSchool for Social Policy and Management, BrandeisUniversity

Instructor:Dominic HodgkinTeaching Assistant: Tim Creedon

Phone:(781) 736-8551E-mail:

Office:Heller 264

E-mail:

Aims of course: To build on previous econometrics coursework to further develop students’ skills in using multivariate statistical techniques, particularly for time-series and longitudinal data. To help students recognize situations where standard ordinary least squares (OLS) models may not apply, and learn about alternative approaches. Based on examples from human-service and health care research.

Prerequisite: HS 405a (Applied Econometrics).

Teaching methods: (a) conceptual explanation of a given econometric technique, when/why it is used; (b) focused class discussion of a paper that uses the technique, drawn from health/human services literature (these papers are marked ‘E’ on the syllabus); (c) a homework assignment in the computer lab, where students apply the technique to a dataset provided to them; (d) ‘Study sections’, where we critically review designs of hypothetical studies that raise various econometric issues.

Requirements: The course grade will be determined by several homework assignments and a final exam. Students will be given feedback on their progress through written comments on homework, and through meeting with the instructor on request.

If you are a student with a documented disability on record at BrandeisUniversity and wish to have a reasonable accommodation made for you in this class, please see me immediately.

READING LIST

Text: Wooldridge JM. Introductory Econometrics: A Modern Approach. 5thedition. Cengage South-Western, 2013.

Other readings are available on the course website (access via Website access is automatic for registered students. Others should contact me to arrange access.

A separate sheet specifies the class schedule and the readings for each session. Make sure to check the sheet regularly.

A.Cross-sectional data

  1. Review of ordinary least squares (OLS)

Review Wooldridge, chapters 3 and 6.

Ramanathan R, Introductory Econometrics, pp. 211-213 [Coronary heart disease example – Reading Assignment 1]

  1. Skewed data

Diehr P, Yanez D, Ash A et al (1999). Methods for analyzing health care utilization and costs. Annual Review of Public Health 20: 125-144.

  1. Logit and odds ratios

Wooldridge, chapter 17.1.

Hillis SL, Woolson RF (1995). Analysis of categorized data. (Chapter 3 in TsuangMT, Tohen M and GEP Zahner (1995). Textbook in Psychiatric Epidemiology.)

Oropesa RS, LandaleNS (2000). From austerity to prosperity? Migration and child poverty among mainland and island Puerto Ricans.Demography Aug;37(3):323-38. (E)

Long JS, Freese J (2006). Regression models for categorical dependent variables using Stata, chapter 4.

  1. Power analysis

HinkleDE, Wiersma W, Jurs SG (1998). Determining power and sample size. Chapter 13, Applied statistics for the behavioral sciences. 4th edition.

Dennis ML, Lennox RD and Foss MA (1997). Practical power analysis for substance abuse health services research. In: Bryant KJ, Windle M (Ed) et al; The science of prevention: Methodological advances from alcohol and substance abuse research.

B.Models for multi-period and duration data

  1. Two-period pre-post analysis

Wooldridge, chapter 13.

Meyer, B. (1995). Natural and quasi-experiments in economics. Journal of Business and Economic Statistics. 13(2): 151-162.

Kaestner R, Korenman S, O'Neill J (2003). Has welfare reform changed teenage behaviors? Journal of Policy Analysis and Management 22(2): 225-48. (E).

Shadish, WR, Cook, TD, & Campbell, DT (2002). Experimental and quasi-experimental designs for generalized causal inference. Chapter 5.

  1. Interrupted time series

WagnerAK, Soumerai SB, Zhang F, Ross-Degnan D (2002). Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 27(4):299-309.

Libby AM, Brent DA, et al (2007). Decline in treatment of pediatric depression after FDA advisory on risk of suicidality with SSRIs. Am J Psychiatry. 2007 Jun;164(6):884-91.(E).

Shadish, WR, Cook, TD, & Campbell, DT (2002). Experimental and quasi-experimental designs for generalized causal inference. Chapter 6.

  1. Fixed effects

Wooldridge, chapter 14.1.

Johnson DR (1995). Alternative methods for the quantitative analysis of panel data in family research: pooled time-series models. Journal of Marriage and the Family 57: 1065-1077.

Dee TS (2001). Alcohol abuse and economic conditions: evidence from repeated cross-sections of individual-level data. Health Economics 10(3): 257-70. (E)

  1. Random effects

Wooldridge, chapter 14.2.

Powell, L. M. (2009). Fast food costs and adolescent body mass index: evidence from panel data. Journal of Health Economics, 28(5), 963-970. (E)

  1. Duration data/ Survival analysis

Luke, DA, Homan SM (1998). Time and change: Using survival analysis in clinical assessment and treatment evaluation. Psychological Assessment 10(4), Dec, 360-378.

Fisher DL, Lin DY (1999). Time-dependent covariates in the Cox proportional-hazards regression model. Annual Review of Public Health 20: 145-157.

Wells K, Guo S (2003). Mothers’ welfare and work income and reunification with children in foster care.Childrenand Youth Services Review 25(3): 203-224. (E)

Cleves M, Gould WW, et al. An Introduction to Survival Analysis Using Stata, chapters 8 and 9. 2008.

C.Other topics

  1. Instrumental variables

Wooldridge, chapter 15.

Newhouse JP ; McClellan M (1998). Econometrics in outcomes research: the use of instrumental variables. Annual Review Of Public Health 19: 17-34.

Sturm R (2000). Instrumental variables methods for effectiveness research. International Journal of Methods in Psychiatric Research 7(1): 17-26.

Knox VW (1996). The effects of child support payments on developmental outcomes for elementary school-age children. Journal of Human Resources31(4): 816-840. (E)

  1. Multilevel modeling

Luke DA (2004). Multilevel modeling. Sage Publications, chapters 1-2.

Diez-Roux AV (2000). Multilevel analysis in public health research. Annual Review Of Public Health 21: 171-92.

Singer JD (1998). Using SAS Proc Mixed to fit multilevel models, hierarchical models and individual growth models. Journal of Educational and Behavioral Statistics 24(4): 323-355.

Hogan MJ, Campbell JS(2005). Contrasting Juvenile and Program-Level Impacts on Diversion Service Provision: A Hierarchical Linear Analysis. Youth Violence and Juvenile Justice 3(1): 41-58. (E)

7/25/2014