Indiana University-Purdue University Indianapolis (IUPUI) / In this dissertation, a potential outcomes (PO) based framework is developed for
causally interpretable treatment effect parameters in the multinomial dependent variable
regression framework. The specification of the relevant data generating process (DGP) is
also derived. This new framework simultaneously accounts for the potential endogeneity
of the treatment and loosens inter-category covariance restrictions on the multinomial
outcome model (e.g., the independence from irrelevant alternatives restriction).
Corresponding consistent estimators for the “deep parameters” of the DGP and the
treatment effect parameters are developed and implemented (in Stata). A novel approach
is proposed for assessing the inter-category covariance flexibility afforded by a particular
multinomial modeling specification [e.g. multinomial logit (MNL), multinomial probit
(MNP), and nested multinomial logit (NMNL)] in the context of our general framework.
This assessment technique can serve as a useful tool for model selection. The new
modeling/estimation approach developed in this dissertation is quite general. I focus here,
however, on the NMNL model because, among the three modeling specifications under
consideration (MNL, MNP and NMNL), it is the only one that is both computationally
feasible and is relatively unrestrictive with regard to inter-category covariance. Moreover,
as a logical starting point, I restrict my analyses to the simplest version of the model – the
trinomial (three-category) NMNL with an endogenous treatment (ET) variable conditioned
on individual-specific covariates only. To identify potential computational issues and to assess the statistical accuracy of my proposed NMNL-ET estimator and its implementation
(in Stata), I conducted a thorough simulation analysis. I found that conventional
optimization techniques are, in this context, generally fraught with convergence problems.
To overcome this, I implement a systematic line search algorithm that successfully resolves
this issue. The simulation results suggest that it is important to accommodate both
endogeneity and inter-category covariance simultaneously in model design and estimation.
As an illustration and as a basis for comparing alternative parametric specifications with
respect to ease of implementation, computational efficiency and statistical performance,
the proposed model and estimation method are used to analyze the impact of substance
abuse/dependence on the employment status using the National Epidemiologic Survey on
Alcohol and Related Conditions (NESARC) data.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/17715 |
| Date | 18 June 2018 |
| Creators | Tang, Shichao |
| Contributors | Terza, Joseph V., Carlin, Paul, Lin, Hsien-Chang, Morrison, Gwendolyn, Seo, Boyoung |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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