Distribution theory for the analysis of binary choice under uncertainty with nonparametric estimation of expectations

Ahn, Hyo Jun; Manski, Charles F.

doi:10.1016/0304-4076(93)90123-m

Cited by 44 publications

(49 citation statements)

References 5 publications

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“…This result differs from Stock's in the inclusion of the term g(v)-g(x)-p , Ahn and Manski (1989) and Hotz and Miller (1989), and the average derivative estimator of Hardle and Stoker (1989 Integration by Powell, Stock, and Stoker's (1989) weighted average derivative estimator and Robinson's (1989) test statistics.…”

Section: Letmentioning

confidence: 58%

The Asymptotic Variance of Semiparametric Estimators

Newey¹

1994

Econometrica

582

715

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Section: Letmentioning

confidence: 58%

The Asymptotic Variance of Semiparametric Estimators

Newey¹

1994

Econometrica

582

715

View full text Add to dashboard Cite

“…The conditional median independence assumption in (2.2) allows for heteroskedasticity of unknown form, and hence, is substantially weaker than the assumption imposed in Ahn and Manski (1993). Given (2.3), the model (2.1) then satisfies…”

Section: Model Under Uncertaintymentioning

confidence: 99%

“…The pioneering papers of Manski (1991Manski ( , 1993 establish nonparametric identification of agents' expectations in the discrete choice model under uncertainty when the expectations are fulfilled and conditioned only on observable variables. Utilizing this result, Ahn and Manski (1993) proposed a two-stage estimator for a binary choice model under uncertainty where agent's utility was linear in parameter and the unobserved preference shock had a known distribution. Specifically, Ahn and Manski (1993) estimated the agent's expectations nonparametrically in the first stage and then the preference parameters in the second stage by maximum likelihood estimation using the choice data and the expectation estimates.…”

Section: Introductionmentioning

confidence: 99%

“…Utilizing this result, Ahn and Manski (1993) proposed a two-stage estimator for a binary choice model under uncertainty where agent's utility was linear in parameter and the unobserved preference shock had a known distribution. Specifically, Ahn and Manski (1993) estimated the agent's expectations nonparametrically in the first stage and then the preference parameters in the second stage by maximum likelihood estimation using the choice data and the expectation estimates. Ahn (1995Ahn ( , 1997) extended the two-step approach further.…”

Section: Introductionmentioning

confidence: 99%

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Maximum score estimation of preference parameters for a binary choice model under uncertainty

Chen¹,

Lee²,

Sung

2013

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This paper develops maximum score estimation of preference parameters in the binary choice model under uncertainty in which the decision rule is affected by conditional expectations. The preference parameters are estimated in two stages: we estimate conditional expectations nonparametrically in the first stage and then the preference parameters in the second stage based on Manski (1975Manski ( , 1985's maximum score estimator using the choice data and first stage estimates. The paper establishes consistency and derives the rate of convergence of the corresponding two-stage estimator, which is of independent interest for maximum score estimation with generated regressors. The paper also provides results of some Monte Carlo experiments.

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“…5 We believe that this assumption is sensible in many, but certainly not all, applications. For instance, in the classic example of married couples' contraceptive choice examined in Hotz and Miller (1993), the time homogeneity assumption implies that the period utility from choosing to become sterilized does not directly depend on a couple's age, conditioning on health status, income level, the stock of existing children, etc.…”

mentioning

confidence: 98%

Identification and semiparametric estimation of a finite horizon dynamic discrete choice model with a terminating action

et al. 2016

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We study identi…cation and estimation of …nite-horizon dynamic discrete choice models with a terminal action. We …rst demonstrate a new set of conditions for the identi…cation of agents' time preferences. Then we prove conditions under which the per-period utilities are identi…ed for all actions in the agent's choice-set, without having to normalize the utility for one of the actions. Finally, we develop a computationally tractable semiparametric estimator. The estimator uses a two-step approach that does not use either backward induction or forward simulation. Our methodology can be implemented using standard statistical packages without the need to write specialized computational routines, as it involves linear (or nonlinear) projections only. Monte Carlo studies demonstrate the superior performance of our estimator compared with existing two-step estimation methods.Monte Carlo studies further demonstrate that the ability to identify the per-period utilities for all actions is crucial for counterfactual predictions. As an empirical illustration, we apply the estimator to the optimal default behavior of subprime mortgage borrowers, and the results show that the ability to identify the discount factor, rather than assuming an arbitrary number as typically done in the literature, is also crucial for obtaining correct counterfactual predictions. These …ndings highlight the empirical relevance of key identi…cation results of the paper.

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Distribution theory for the analysis of binary choice under uncertainty with nonparametric estimation of expectations

Cited by 44 publications

References 5 publications

The Asymptotic Variance of Semiparametric Estimators

The Asymptotic Variance of Semiparametric Estimators

Maximum score estimation of preference parameters for a binary choice model under uncertainty

Identification and semiparametric estimation of a finite horizon dynamic discrete choice model with a terminating action

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