3. Empirical Methods for Microeconomic Applications to Health

Transcription

1 3. Empirical Methods for Microeconomic Applications to Health Presentation: The aim of this course is to provide the students with the necessary analytical tools toformulate appropriate econometric models,estimate themand draw proper inference using micro level data related to health issues, for instance data from health surveys. The key features of such data sets are their qualitative nature and/or panel type structure. Hence it is important to know and be able to apply the models and methods specially designed for incorporating the particular characteristics of these data sets. Objectives: At the end of the course the student would know how to: (1) Specify a suitable econometric model for investigating the problem under study; (2) Compare the theoretical model with empirical observations; and (3) Draw proper conclusions based on the results. Course Structure: This course will be presented in two modules, discrete choice models (by Professor William Greene of New York University) and panel data (longitudinal) data methods (by Professor Patrick Gagliardini of the University of Lugano). The contents of the two modules will be (roughly) (1) Discrete Choice Models - Dichotomous model (Logit, Probit) - Polytomous models (multinomial Logit, ordered models) - Count data models (2) Panel or Longitudinal Data - Fixed effects - Random effects - Dynamic models and instrumental variable estimation - Qualitative variables - Endogenous sample selection Valuation The course credits are obtained by writing a paper that applies the theory presented in the course to a data set. Place: University of Lugano Switzerland (Room to be confirmed) Date: June 22 to 26, 2015 Course Administrator: Ms. Marisa Clemenz, marisa.clemenz@usi.ch (Information, registration and accommodation)

2 Course Module Outlines: I. Discrete Choice Models Course Outline and Agenda The course will be presented over two days plus a morning session on a third day. Each day will consist of three background sessions in the morning and early afternoon, then two afternoon lab/practical sessions in which we will apply what we have discussed in the morning. Specifically: Day 1: Monday, June 22 9:00-10:15 1A Descriptive tools, regression, cross section and panel data, binary choice 10:30-12:00 1B Binary choice models, estimation and analysis, partial effects, Fixed and random effects 13:45-15:00 1C Ordered choice, extensions, endogeneity, control functions, robust estimation and inference, bootstrapping, models for counts 15:15-16:15 1D Panel data models. Fixed effects, random effects, clustering, dynamic models, Mundlak approach 16:15-16:30 Break 16:30-17:30 Lab 1 Estimation of basic models, description, kernel estimation, binary choice models, partial effects, model simulation, panel data estimation, endogeneity, dynamic models, bootstrap, delta method, Krinsky/Robb, nonlinear functions. Day 2: Tuesday, June 23 9:00-10:15 2A Two part models, censoring and truncation, inflation models, panel data models 10:30-12:00 2B Multinomial choice models, latent class models, mixed models, modeling aggregate market shares 13:45-15:00 2C Advanced models for heterogeneity, stated choice methods 15:15-16:15 2D Mixed modeling, latent class modeling, panel data methods 16:15-16:30 Break 16:30-17:30 Lab2 Multinomial choice modeling, stated preferences, Day 3: Wednesday, June 24 9:00-10:15 3A Censoring, truncation, sample selection, topics and discussion to be determined. 10:30 Begin of the Panel Data Module with Professor Gagliardini Lab sessions will use NLOGIT. Copies of the program and all data sets and materials will be provided to all students in the class to install on their own computers.

3 Data for Lab Sessions and Practicals Data sets for exercises will be provided with the program. Students may wish to work on their own projects as well. If you are not already using NLOGIT (or LIMDEP), you can import your data into NLOGIT by obtaining or converting your data file in standard CSV format. (If you have a data file that is in an Excel spreadsheet, xls or xlsx, format, just read it back into Excel and use Save As to save it as a CSV file.) References and Applications Major References Cameron, A. and Trivedi, P., (2005) Microeconometrics, Cambridge University Press Greene, W., (2012) Econometric Analysis, Prentice Hall. Wooldridge, J. (2010) Econometric analysis of cross-section and panel data, 2 nd. Ed., MIT Press Background References Terza, J., Basu, A. and Rathouz, P. (2008): Two-Stage Residual Inclusion Estimation: Addressing Endogeneity in Health Econometric Modeling, Journal of Health Economics, 27, (Terza_J-Basu_A-Rathouz_p-ResidualInclusion.pdf) Simple Solutions to the Initial Conditions Problem for Dynamic, Nonlinear Panel Data Models with Unobserved Heterogeneity, Journal of Applied Econometrics 20, 39-54, January (Wooldridge-InitialConditions.pdf) Cluster-Sample Methods in Applied Econometrics, American Economic Review 93, , May (Wooldridge-Cluster.pdf) Greene, W., Testing hypotheses about interaction terms in nonlinear model, Economics Letters, 107 (2010) (Greene-InteractionTerms-ECOLET.pdf) Greene, W. and Hensher, D. (2010) Modeling Ordered Choices, Cambridge University Press (OrderedChoiceModeling.pdf) Applications: Lagarde, M., Investigating attribute non-attendance and its consequences in choice experiments with latent class models, Health Economics, 5, 2013, pp Scott, Schurer, Jensen and Sivey, The Effects of an Incentive Program on Quality of Care in Diabetes Management, Health Economics, 18, 9, 2009, pp Johnston, D., Schurer, S., Shields, M., Maternal Gender Role Attitudes, Human Capital Investment, and Labor Supply of Sons and Daughters, IZA Discussion paper 6656, June, (Johnston-Schurer-Shields-REM.pdf) Jones, A. and S. Schurer, How does Heterogeneity shape the Socioeconomic Gradient in Health Satisfaction, Journal of Applied Econometrics, 26, 4, 2011, pp (Jones- Schurer-2011.pdf) Bago d Uva, T. and A. Jones, Health care utilisation in Europe: New evidence from the ECHP, Journal of Health Economics, 28, 2009, pp (Bago_d Uva-Jones -LatentClass.pdf) Contoyannis, P., Jones, A. and N. Rice, The Dynamics of Health in the British Household Panel Survey, Journal of Applied Econometrics, 19, 2004, pp (Jones-et-al- HealthSatisfaction.pdf) Riphann, R., Wambach, A. and Million, A., Incentive Effects in the Demand for Health Care: A Bivariate Panel Count Data Estimation, Journal of Applied Econometrics, 18, 2003, pp (RWM-MixedModel.pdf) Winkelmann, R., Health Rare Reform and the Number of Doctor Visits An Econometric Analysis, Journal of Applied Econometrics, 19, 2004, pp (Winkelmann- DoctorVisits.pds)

4 II. Panel Data Methods Course Outline and Agenda The part of the course concerning panel data methods will be presented in the second half of the week. The structure is similar as for the part on discrete choice models, with background sessions in the morning and early afternoon, followed by lab/practical sessions. Specifically: Day 3: Wednesday, June 24 10:30-12:00 3B The linear panel data model. Fixed effects. The Least Squares Dummy Variable (LSDV) estimator. 13:45-15:00 3C Random effects estimation. Fixed effects vs. random effects. Strict exogeneity and Hausman test. 15:15-17:30 Lab 3 Fixed effects and random effects estimation in linear panel models. Day 4: Thursday, June 25 9:00-10:15 4A The linear dynamic panel model. Inconsistency of the LSDV estimator. 10:30-12:00 4B Instrumental variable estimation. The Anderson-Hsiao, Arellano- Bond and Arellano-Bover estimators. 13:45-15:00 4C Discrete choice panel models with random effects. 15:15-17:30 Lab 4 Instrumental variable estimation in dynamic panel models. Day 5: Friday, June 26 9:00-10:15 5A Discrete choice models with fixed effects. The Conditional Maximum Likelihood (CML) estimator in the panel logit model. 10:30-12:00 5B Sample selection in panel models. Tobit panel models. Heckman-type estimators. Fixed effects estimators. Outlook. References and Applications The lectures are based on slides that will be made available to students before the course starts. Additional references are listed below: Main References (books) Arellano, M. (2003) Panel data econometrics, Oxford University Press Baltagi, B. H. (2001) Econometric analysis of panel data, John Wiley & Sons; 2nd edition Hsiao, C. (2003) Analysis of panel data, Cambridge University Press; 2nd edition Matyas, L. and Sevestre, P. (1996) The econometrics of panel data: A handbook of the theory with applications, Kluwer Print on Demand; 2nd edition Maddala, G. S. (1983) Limited-dependent and qualitative variables in econometrics, Cambridge University Press

5 Background Theoretical References Arellano, M., and B., Honore (2001): Panel Data Models: Some Recent Developments, in Handbook of Econometrics, Vol. 5, J. Heckman and E. Leamer (eds), Elsevier, Amsterdam. Anderson, T. W., and C., Hsiao (1982): Formulation and Estimation of Dynamic Models Using Panel Data, Journal of Econometrics, 18, Arellano, M., and S., Bond (1991): Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations, Review of Economic Studies, 58, Balestra, P., and M., Nerlove (1966): Pooling Cross-Section and Time Series Data in the Estimation of a Dynamic Model: The Demand for Natural Gas, Econometrica, 34, Chamberlain, G. (1980): Analysis of Covariance with Qualitative Data, Review of Economic Studies, 47, Gourieroux, C., and A. Monfort (1993): Simulation Based Inference: A Survey with Special Reference to Panel Data Models, Journal of Econometrics, 59, Honore, B. (1992): Trimmed LAD and Least Squares Estimation of Truncated and Censored Regression Models with Fixed Effects, Econometrica, 60, Kyriazidou, E. (1997): Estimation of a Panel Data Sample Selection Model, Econometrica, 65, Wooldridge, J. (1995): Selection Correction for Panel Data Models under Conditional Mean Independence Assumptions, Journal of Econometrics, 68, Papers with health economics applications: Askildsen, J., Baltagi, B., and T., Holmas (2003): Wage Policy in the Health Care Sector: A Panel Data Analysis of Nurses Labour Supply, Health Economics, 12, Bishai, D. (1996): Quality Time: How Parents Schooling Affects Child Health Through Its Interaction with Childcare Time in Bangladesh, Health Economics, 5, Bjorklund, A. (1985): Unemployment and Mental Health: Some Evidence from Panel Data, Journal of Human Resources, 20, Brown, T., Coffman, J., Quinn, B., Scheffer, R., and D., Schwalm (2005): Do Physicians Always Flee from HMO s? New Results Using Dynamic Panel Estimation Methods, Health Services Research, 40, Contoyannis, P., Jones, A., and N., Rice (2004): The Dynamics of Health in the British Household Panel Survey, Journal of Applied Econometrics, 19, Gannon, B. (2005): A Dynamic Analysis of Disability and Labour Force Participation in Ireland , Health Economics, 14, Jones, A. (2000): Health Econometrics, in Handbook of Health Economics, A. Culyer and J. Newhouse (eds.), North-Holland. Jones, A. (2007): Panel Data Methods and Applications to Health Economics, in The Palgrave Handbook of Econometrics, Volume 2: Applied Econometrics, T. Mills and K. Patterson (eds.). Kerkhofs, M., and M., Lindeboom (1997): Age Related Health Dynamics and Changes in Labour Market Status, Health Economics, 6, Lindeboom, M., Portrait, F., and G., van den Berg (2002): An Econometric Analysis of the Mental-Health Effects of Major Events in the Life of Older Individuals, Health Economics, 11, Tamm, M., Tauchmann, H., Wasem, J., and S., Gress (2007): Elasticities of Market Shares and Social Health Insurance Choice in Germany: A Dynamic Panel Data Approach, Health Economics, 16, Patrick