Identification in a generalization of bivariate probit models with dummy endogenous regressors

Han, Sukjin; Vytlacil, Edward

doi:10.1016/j.jeconom.2017.04.001

Cited by 88 publications

(79 citation statements)

References 30 publications

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“…However, as shown for instance in Han and Vytlacil (2014), Marra and Radice (2011a) and Wilde (2000), the presence of this restriction may not be necessary.…”

Section: Linear Predictor Specificationmentioning

confidence: 99%

Copula regression spline models for binary outcomes

2015

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We introduce a framework for estimating the effect that a binary treatment has on a binary outcome in the presence of unobserved confounding. The methodology is applied to a case study which uses data from the Medical Expenditure Panel Survey and whose aim is to estimate the effect of private health insurance on health care utilization. Unobserved confounding arises when variables which are associated with both treatment and outcome are not available (in economics this issue is known as endogeneity). Also, treatment and outcome may exhibit a dependence which cannot be modeled using a linear measure of association, and observed confounders may have a non-linear impact on the treatment and outcome variables.The problem of unobserved confounding is addressed using a two-equation structural latent variable framework, where one equation essentially describes a binary outcome as a function of a binary treatment whereas the other equation determines whether the treatment is received.Non-linear dependence between treatment and outcome is dealt with by using copula functions, whereas covariate-response relationships are flexibly modeled using a spline approach.Related model fitting and inferential procedures are developed, and asymptotic arguments presented.

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“…However, as shown for instance in Han and Vytlacil (2014), Marra and Radice (2011a) and Wilde (2000), the presence of this restriction may not be necessary.…”

Section: Linear Predictor Specificationmentioning

confidence: 99%

Copula regression spline models for binary outcomes

2015

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“…The correlation parameter achieves identification through functional form. Han and Vytlacil (2017) prove that identification is achievable in bivariate models without exclusion restrictions (i.e., instruments) if there are common exogenous regressors in both equations. They also show that having an exclusion restriction is necessary and sufficient for identification in these models without common exogenous variables but is sufficient only in models with common exogenous covariates.…”

Section: Identification Strategymentioning

confidence: 88%

Industry shutdown rates and permanent layoffs: evidence from firm-worker matched data

et al. 2017

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Firm shutdown creates a turbulent situation for workers as it leads directly to layoffs for its workers. An additional consideration is whether a firm's shutdown within an industry creates turbulence for workers at other continuing firms. Using data drawn from the Longitudinal Worker File, a Canadian firm-worker matched employment database, we investigate the impact of industry shutdown rates on workers at continuing firm. This paper exploits variation in shutdown rates across industries and within an industry over time to explain the rate of permanent layoffs and the growth of workers' earnings. We find an increase in industry shutdown rates increases the probability of permanent layoffs and decreases earnings growth for workers at continuing firms. JEL Classification: J24, J31, J63, C35

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“…, which is positive for any p 0 2 >p 0 2 and is negative for any p 0 2 <p 0 2 under Assumptions D.1(iii) and D.3 (see Lemma 4.1 of Han and Vytlacil, 2017). This implies that J G (ϑ w 1 ) is of full rank when p 0 2 =p 0 2 .…”

Section: Assumption D2mentioning

confidence: 90%

“…This implies that J G (ϑ w 1 ) is of full rank when p 0 2 =p 0 2 . Hence, the same arguments as in the proof of Theorem 5.1 in Han and Vytlacil (2017) lead to the identification of ϑ w 1 under the assumption that {(w 1 γ 0 + η(w 1 γ 1 ), ρ) : θ ∈ Θ} is open and simply connected. Moreover, the strict monotonicity of F ε j and η implies that w 1 γ 1 = η −1 (F −1 ε 1 (p 1 1 ) − w 1 γ 0 ) and thus that γ 1 is identified from p 1 1 and γ 0 under Assumption D.2(ii).…”

Section: Assumption D2mentioning

confidence: 90%

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Treatment Eﬀect Models with Strategic Interaction in Treatment Decisions

Hoshino

Yanagi

2018

SSRN Journal

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We develop identification and estimation methods for treatment effect models with strategic interaction in the treatment decisions. We consider models where one's treatment choice and outcome can be endogenously affected by others' treatment choices. We formulate the interaction of the treatment decisions as a two-player complete information game with potential multiple equilibria. For this model, using a latent index framework and the assumption of a stochastic equilibrium selection rule, we prove that the marginal treatment effect from one's own treatment and that from his/her partner's can be separately point-identified with potential over-identifiability. Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the marginal treatment effects using series approximation. The proposed estimator is shown to be uniformly consistent and asymptotically normal. As an empirical illustration, we investigate the impacts of risky behaviors on adolescents' academic performance.1 To the best of our knowledge, few studies address treatment evaluation in the presence of strategic interaction, where the treatment decisions are explicitly modeled as games. One important exception is Balat and Han (2018), who mainly investigate the partial identification of average treatment effects (ATE). Thus, our main parameters of interest (MTE) are different from theirs (ATE). In addition, the key identification assumptions in Balat and Han (2018) are shape restrictions on the conditional mean outcome functions, while we mainly rely on a stochastic equilibrium selection rule, as stated below.2 As another related work, Mogstad et al. (2019) introduce partial monotonicity to deal with issues due to multiple instruments. However, partial monotonicity is not designed for settings with multivalued or multidimensional treatments such as our situation.

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Identification in a generalization of bivariate probit models with dummy endogenous regressors

Cited by 88 publications

References 30 publications

Copula regression spline models for binary outcomes

Copula regression spline models for binary outcomes

Industry shutdown rates and permanent layoffs: evidence from firm-worker matched data

Treatment Eﬀect Models with Strategic Interaction in Treatment Decisions

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