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From | "Karabulut, Yigitcan" <karabulut@finance.uni-frankfurt.de> |
To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
Subject | AW: Subject: st: How to derive Wooldridge and Orme approaches in xtprobit module ? |
Date | Sun, 21 Mar 2010 20:09:12 +0100 |
Thank you very much for your interest & very helpful answer, Prof. Jenkins. Best Regards, Yigitcan ________________________________________ Von: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] im Auftrag von Stephen P. Jenkins [stephenj@essex.ac.uk] Gesendet: Sonntag, 21. März 2010 19:31 An: statalist@hsphsun2.harvard.edu Betreff: Subject: st: How to derive Wooldridge and Orme approaches in xtprobit module ? +++++++++++++++++++++++ ------------------------------ Date: Fri, 19 Mar 2010 13:46:12 +0100 From: "Karabulut, Yigitcan" <karabulut@finance.uni-frankfurt.de> Subject: st: How to derive Wooldridge and Orme approaches in xtprobit module ? Dear Statlist, I am trying to estimate a dynamic random effects probit model that has the following reduced form: Y_it = beta*x_it + gamma*y_t-1+alpha_i +u_it ( where y_it is a binary variable, alpha_i represents the individual specific unobserved heterogeneity, u_it is the unobserved error, x_it is a vector of regressors) In order to account for unobserved heterogeneity and initial conditions problems, I have employed the Heckman approach (Heckman, 1981) using the program module redprob by Prof. Mark Stewart. However, also as stressed in the literature (e.g. Arulampan and Steward, 2009 or Capellari and Jenkins, 2008), the computation of Heckman estimator takes so long (for instance, Capellari and Jenkins ( 2008) notes that their estimation took about 15 hours). Since the other two approaches; Orme (1996) and Wooldridge (2002) do also provide similar results as the Heckman estimator (Arulampan and Steward, 2009), I am willing to employ these approaches since the estimation is less "expensive" (also as a robustness check). I read that I can derive the Orme and Wooldridge approaches from the program module xtprobit, however, I could not figure out how. I was wondering if anyone knows how to derive these approaches in xtprobit module in Stata? Thanks! Best, Yigitcan ++++++++++++++++++++++++ (1) As the Statalist FAQ states: you are enjoined to provide full bibliographic references to research that you cite. This is a multidisciplinary list. [Unfortunately the work of mine you cite is not yet known worldwide! :)) ] (2) You will also get better assistance if you show the Stata commands that you have tried in order to implement these estimators. We are not here to do your research for you. In both cases, you need your data in long form. For the Orme estimator, fit the initial condition probit, and then generate the generalized residual variable using the formula in his paper. Then you enter that variable as an additional regressor in the -xtprobit- specification. For the Wooldridge estimator, you need to first compute the longitudinally-averaged variables for each person (think e.g. -bys personid: egen-), then enter these and the initial binary outcome values as additional regressors in your -xtprobit- specification. References (which contain references to the papers by Stewart, and Arulampalam and Stewart): Cappellari, L and Jenkins, SP (2008) “The dynamics of social assistance receipt: measurement and modelling issues, with an application to Britain”, prepared under contract JA0004519, ELS/SPD Division, Organisation for Economic Cooperation and Development, September 2008, 70 pp. Report released as OECD Social, Employment and Migration Working Paper 67, http://www.oecd.org/dataoecd/30/42/41414013.pdf. Cappellari, L and Jenkins, SP (2009) “The dynamics of social assistance benefit receipt in Britain”, ISER Working Paper 2009-29, http://www.iser.essex.ac.uk/pubs/workpaps/pdf/2009-29.pdf. In: D. Besharov and K. Couch (eds.), Measuring Poverty, Income Inequality, and Social Exclusion. Lessons from Europe. Oxford University Press, 2010, forthcoming. Orme, Christopher D. (1997). ‘The initial conditions problem and two-step estimation in discrete panel data models’, Discussion Paper No. 9633, School of Social Sciences, University of Manchester. Revised version, June 2001, retitled as: ‘Two-Step inference in dynamic non-linear panel data models’, http://personalpages.manchester.ac.uk/staff/chris.orme/documents/Research%20Papers/initcondlast.pdf Wooldridge, Jeffery M. (2005), ‘Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity’, Journal of Applied Econometrics, 20: 39–54. Stephen ----------------------------------------- Professor Stephen P. Jenkins <stephenj@essex.ac.uk> Institute for Social and Economic Research University of Essex, Colchester CO4 3SQ, U.K. Tel: +44 1206 873374. Fax: +44 1206 873151. http://www.iser.essex.ac.uk Survival Analysis using Stata: http://www.iser.essex.ac.uk/survival-analysis Downloadable papers and software: http://ideas.repec.org/e/pje7.html * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/