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Estimating Treatment Effects for Ordered Outcomes Using Maximum Simulated Likelihood

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  • Christian Gregory

    (Economic Research Service, USDA)

Abstract

This presentation introduces 4 new modules: -treatoprobit-, -switchoprobit-, -treatoprobitsim-, and -switchoprobitsim-. Each of these routines estimates a model in which a binary endogeneous variable is affects an ordered outcome. -treatoprobit- and -switchoprobit- estimate treatment and outcome under the assumption that the error terms in the selection and outcome process are distributed as bivariate normal. -treatoprobitsim- and -switchoprobitsim- allow researchers to relax this assumption by estimating models in which a latent factor with a potentially non-normal distribution accounts for the correlation between treatment and outcome. -treatoprobit- and -treatoprobitsim- operate under the assumption of a single outcome regime for treated and untreated groups; -switchoprobit- and -switchoprobitsim- work under (and test) the assumption that outcome processes for treated and untreated ought to be handled as distinct. The presentation will introduce the modules, show Monte Carlo evidence regarding their performance, and offer an example of their use. This presentation is based on an article that is currently under review at the Stata Journal.

Suggested Citation

  • Christian Gregory, 2015. "Estimating Treatment Effects for Ordered Outcomes Using Maximum Simulated Likelihood," 2015 Stata Conference 2, Stata Users Group.
    • Handle: RePEc:boc:scon15:2
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    Cited by:

    1. Bilgel, Fırat & Karahasan, Burhan Can, 2018. "Self-rated health and endogenous selection into primary care," Social Science & Medicine, Elsevier, vol. 197(C), pages 168-182.
    2. Jan Willem Nijenhuis, 2021. "Estimation of ordered probit model with endogenous switching between two latent regimes," 2021 Stata Conference 22, Stata Users Group.
    3. Huang, Bihong & Lian, Yujun & Li, Wensu, 2016. "How far is Chinese left-behind parents' health left behind?," China Economic Review, Elsevier, vol. 37(C), pages 15-26.
    4. Debucquet, G. & Guillotreau, P. & Lazuech, G. & Salladarré, F. & Troiville, J., 2020. "Sense of belonging and commitment to a community-supported fishery. The case of Yeu Island, France," Review of Agricultural, Food and Environmental Studies, Institut National de la Recherche Agronomique (INRA), vol. 101(4), January.
    5. Wubneshe Dessalegn Biru & Manfred Zeller & Tim K. Loos, 2020. "The Impact of Agricultural Technologies on Poverty and Vulnerability of Smallholders in Ethiopia: A Panel Data Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 517-544, January.
    6. Jie Dong & Kuan Zhang & Xiguo Yin & Houjian Li & Mansoor Ahmed Koondhar, 2021. "Does piped water improve adolescent health? Empirical evidence from rural China," Review of Development Economics, Wiley Blackwell, vol. 25(3), pages 1601-1628, August.
    7. Samuel Sekyi & Benjamin Musah Abu & Paul Kwame Nkegbe, 2020. "Effects of farm credit access on agricultural commercialization in Ghana: Empirical evidence from the northern Savannah ecological zone," African Development Review, African Development Bank, vol. 32(2), pages 150-162, June.
    8. Firat Bilgel & Burhan Can Karahasan, 2017. "Self-Rated Health and Primary Care Utilization: Is Selection into Healthcare Endogenously Determined?," Working Papers 1079, Economic Research Forum, revised 04 Jun 2017.
    9. Abiodun A. Ogundeji, 2022. "Adaptation to Climate Change and Impact on Smallholder Farmers’ Food Security in South Africa," Agriculture, MDPI, vol. 12(5), pages 1-16, April.
    10. Zi-qing Yuan & Xian Zheng & Eddie C. M. Hui, 2021. "Happiness Under One Roof? The Intergenerational Co-residence and Subjective Well-Being of Elders in China," Journal of Happiness Studies, Springer, vol. 22(2), pages 727-765, February.

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