Maximum simulated likelihood: don't stop believin'?

Schrey, Christopher

doi:10.15439/2021F113

Artykuł - szczegóły

Tytuł artykułu

Maximum simulated likelihood: don't stop believin'?

Autorzy

Schrey Christopher

Wybrane pełne teksty z tego czasopisma

http://annals-csis.org

Identyfikatory

DOI

10.15439/2021F113

Warianty tytułu

Konferencja

Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)

Języki publikacji

Abstrakty

Unobserved heterogeneity may complicate model estimation in econometrics. To integrate out the effect of unobserved heterogeneity via maximum simulated likelihood (MSL) estimation, assumptions regarding the underlying distribution need to be made. Researchers seldomly discuss these assumptions. This raises the question, to what extent estimation results in the MSL-context are robust to potential distributional mismatch. This work-in-progress derives the research question from the literature. A simulation study is conducted that underpins the relevance of this matter, where results imply that mismatch may introduce significant bias. Intended future work to properly address and answer this question is defined and discussed.

Słowa kluczowe

heterogeneity likelihood estimation robustness estimation

niejednorodność estymacja prawdopodobieństwa odporność estymacja

Wydawca

Polskie Towarzystwo Informatyczne

Czasopismo

Annals of Computer Science and Information Systems

Rocznik

2021

Tom

Vol. 26

Strony

175--180

Opis fizyczny

Bibliogr. 24 poz., wykr., tab.

Twórcy

autor

Schrey Christopher

hristopher.schrey@outlook.de

Lipsiusstrasse 44, 04317 Leipzig, Germany

Bibliografia

1. P. Deb, C. Li, P. K. Trivedi, and D. M. Zimmer. “The effect of managed care on use of health care services: results from two contemporaneous household surveys”. In: Health economics 15.7 2006, pp. 743-760. http://dx.doi.org/http://dx.doi.org/10.1002/hec.1096.
2. P. Deb and P. K. Trivedi. “Specification and simulated likelihood estimation of a non-normal treatment-outcome model with selection: Application to health care utilization”. In: The Econometrics Journal 9.2 2006, pp. 307-331. http://dx.doi.org/http://dx.doi.org/10.1111/j.1368-423X.2006.00187.x.
3. D. Shane and P. K. Trivedi. “What drives differences in health care eemand? The role of health insurance and selection bias”. In: Health, Econometrics and Data Group (HEDG) Working Papers 2012. URL: https://www.york.ac.uk/media/economics/documents/herc/wp/12_09.pdf.
4. D. McFadden and K. Train. “Mixed MNL models for discrete response”. In: Journal of Applied Econometrics 15.5 2000, pp. 447-470.
5. D. Revelt and K. Train. “Mixed logit with repeated choices: households’ choices of appliance efficiency level”. In: The Review of Economics and Statistics 80.4 1998, pp. 647-657. http://dx.doi.org/http://dx.doi.org/10.1162/003465398557735. URL : https://direct.mit.edu/rest/article/80/4/647/57083/Mixed-Logit-with-Repeated-Choices-Households.
6. D. Munger, P. L’Ecuyer, F. Bastin, C. Cirillo, and B. Tuffin. “Estimation of the mixed logit likelihood function by randomized quasi-Monte Carlo”. In: Trans-portation Research Part B: Methodological 46.2 2012, pp. 305-320. http://dx.doi.org/http://dx.doi.org/10.1016/j.trb.2011.10.005.
7. K. E. Train. Discrete choice methods with simulation. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/http://dx.doi.org/10.1017/CBO9780511805271.
8. A. Cameron and P. K. Trivedi. Microeconometrics: Methods and applications. New York, NY: Cambridge University Press, 2005.
9. C. R. Bhat. “Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model”. In: Transportation Research Part B: Methodological 35.7 2001, pp. 677-693. DOI : http://dx.doi.org/10.1016/S0191-2615(00)00014-X.
10. L. Chiou and J. L. Walker. “Masking identification of discrete choice models under simulation methods”. In: Journal of Econometrics 141.2 2007, pp. 683-703. http://dx.doi.org/http://dx.doi.org/10.1016/j.jeconom.2006.10.012.
11. L. M. Andersen. “Obtaining reliable likelihood ratio tests from simulated likelihood functions”. In: PloS one 9.10 2014, e106136. http://dx.doi.org/http://dx.doi.org/10.1371/journal.pone.0106136.
12. M. Bratti and A. Miranda. “Endogenous treatment effects for count data models with endogenous participation or sample selection”. In: Health economics 20.9 2011, pp. 1090-1109. DOI : http://dx.doi.org/10.1002/hec.1764.
13. M. B. Buntin, C. H. Colla, P. Deb, N. Sood, and J. J. Escarce. “Medicare spending and outcomes after postacute care for stroke and hip fracture”. In: Medical care 48.9 2010, pp. 776-784. http://dx.doi.org/http://dx.doi.org/10.1097/MLR.0b013e3181e359df.
14. M. M. Garrido, P. Deb, J. F. Burgess, and J. D. Penrod. “Choosing models for health care cost analyses: issues of nonlinearity and endogeneity”. In: Health services research 47.6 2012, pp. 2377-2397. http://dx.doi.org/http://dx.doi.org/10.1111/j.1475-6773.2012.01414.x.
15. Z. Sándor and K. Train. “Quasi-random simulation of discrete choice models”. In: Transportation Research Part B: Methodological 38.4 2004, pp. 313-327. http://dx.doi.org/http://dx.doi.org/10.1016/S0191-2615(03)00014-6.
16. D. Brunner, F. Heiss, A. Romahn, and C. Weiser. Reliable estimation of random coefficient logit demand models: DICE Discussion Papers. 2017. URL: https://EconPapers.repec.org/RePEc:zbw:dicedp:267.
17. C. Gouriéroux and A. Monfort. Simulation-based econometric methods. Oxford University Press, 1997. http://dx.doi.org/http://dx.doi.org/10.1093/0198774753.001.0001.
18. M. Czajkowski and W. Budziski. “Simulation error in maximum likelihood estimation of discrete choice models”. In: Journal of Choice Modelling 31 2019, pp. 73-85. http://dx.doi.org/http://dx.doi.org/10.1016/j.jocm.2019.04.003.
19. C. Schrey, T. Schäffer, C. Militzer-Horstmann, and N. Kossack. “Maximum Simulated Likelihood: Don’t stop ’til you get enough?” In: Position Papers of the 2019 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems. PTI, 2019, pp. 79-82. http://dx.doi.org/http://dx.doi.org/10.15439/2019F354.
20. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria, 2020. URL: https://www.R-project.org/.
21. Hadley Wickham. ggplot2: elegant graphics for data analysis. Springer-Verlag New York, 2016. URL : https://ggplot2.tidyverse.org.
22. Lionel Henry and Hadley Wickham. purrr: functional programming tools. 2020. URL: https://CRAN.R-project.org/package=purrr.
23. M. Griebel, F. Heiss, J. Oettershagen, and C. Weiser. “Maximum approximated likelihood estimation”. In: INS Preprint No. 1905 2019. URL : https://ins.uni-bonn.de/media/public/publication-media/INSPreprint1905.pdf?pk=1424.
24. P. Deb and P. K. Trivedi. “Maximum simulated likelihood estimation of a negative binomial regression model with multinomial endogenous treatment”. In: Stata Journal 6.2 2006, pp. 246-255.

Uwagi

1. Track 3: Advances in Information Systems and Technology

2. Session: 3rd Special Session on Data Science in Health, Ecology and Commerce

3. Position papers

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-af63185f-4f0a-4952-8d94-382f75df7405