A Semiparametric Bivariate Probit Model for Joint Modeling of Outcomes in STEMI Patients

Ieva, Francesca; Marra, Giampiero; Paganoni, Anna Maria; Radice, Rosalba

doi:10.1155/2014/240435

Cited by 9 publications

(17 citation statements)

References 29 publications

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“…A recursive bivariate probit (RBP) model adopts a recursive simultaneous-equation structure that allows the endogenous variable in one equation to be an exogenous variable in another equation (31). This model structure can address the cause-effect chain of truck crashes and the correlated uncertainty of the two dependent variables; it has been used in public health to assess the effects of specific treatments on subjects, and in travel behavior studies (32)(33)(34)(35)(36)(37)(38).…”

Section: Methodsmentioning

confidence: 99%

Recursive Bivariate Probit Analysis of Injury Severity and Non-Truck Improper Actions in Large Truck-Related Crashes on Florida Suburban Roads

Wang

Vasili

Yang

et al. 2021

Transportation Research Record

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This study investigated the hierarchical connection among injury severity, non-truck improper actions, and contributing factors in large-truck-involved crashes. Data for 4 years (2011–2014) of crashes that involved a large truck (≥ 10,000 lb) and a non-truck vehicle were collected from suburban roads in Florida, U.S. A recursive bivariate probit model was fitted with collected data to identify the cause-effect chain, including contributing factors influenced by improper actions, the effects of improper actions on injury severity, and contributing factors indirectly affecting injury severity in large-truck-related crashes. Study results indicate that non-truck vehicle improper actions, such as excessive speed, careless driving, failure to yield right-of-way, and others, significantly increase the likelihood of fatal and severe injury in large-truck crashes, and factors such as crash month, darkness, intersection-related, surface and shoulder width, truck parking, truck driver age, non-truck driver age, and non-truck alcohol/drug impaired indirectly influence injury severity through their impacts on non-truck improper actions. Two factors—truck right-turn and non-truck driver physical defects—affect injury severity and non-truck improper actions simultaneously. Other factors, including crash year, annual average daily traffic, speed limit, crash type, truck type, truck speed, truck alcohol/drug-impaired, and motorcycle involvement, directly contribute to injury severity in large-truck crashes and have no influence on non-truck improper actions.

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Section: Methodsmentioning

confidence: 99%

Recursive Bivariate Probit Analysis of Injury Severity and Non-Truck Improper Actions in Large Truck-Related Crashes on Florida Suburban Roads

Wang

Vasili

Yang

et al. 2021

Transportation Research Record

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“…the model fitting exercise are the possible presence of a nonlinear covariate-response relationship and how this changes when considering the whole response variable distribution [49]. Instrumental variable techniques are mostly used in the economic literature for isolating the effect of a given predictor in the presence of unobserved confounding (see, for instance, [49,61,98].…”

Section: Recursive Bivariate Probit (Rbp): Formal Modelling Of the Re...mentioning

confidence: 99%

“…The current paper aims to contribute to filling this research gap. This paper also makes a contribution by applying of empirical estimation approach following recent advances such as [34,40,49,53,61] in econometrics for analysing the impact.…”

Section: Introductionmentioning

confidence: 99%

Social networking and risk attitudes nexus: implication for technology adoption among smallholder cassava farmers in Ghana

et al. 2022

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Background Theoretically, social climate and social networking which may affect attitudes, motivations, and readiness towards quality improvement and rewards, influence the adoption decision process by possibly modifying the risk behaviour/attitudes of individuals. Thus for effective promotion of agricultural technologies among farmers in Africa, it is necessary to understand the social context within which risk attitudes are formed and social participation decisions are made. The study, therefore, employed the recursive bivariate endogenous switching probit model to examine how risk attitudes of farmers are shaped through social interactions in the information and communication networks of farmers to influence their technology adoption decisions. Here, the empirical application was done with the contextual case of agriculture intensification technologies in the Root and Tuber Improvement and Marketing Programme (RTIMP) introduced to smallholder cassava farmers in Ghana. This paper makes a contribution to recent advances in the empirical analysis of impact where anticipated problems posed by unobserved confounders are accounted for. This was possible since the approach used allows for the estimation of the treatment effect of endogenous risk attitude variable of interest on farmers’ adoption decisions. Results The study found that the effectiveness and usefulness of social interactions as well as a high degree of trust by cassava farmers in their social networks have higher tendencies to lower the degree of risk aversion behaviour of the farmers to significantly influence RTIMP Technologies adoption decisions positively. The ATE estimate confirms that there has been a significant increase of 38% probability in the likelihood of adopting agricultural intensification technologies in the RTIMP attributable to the non-risk aversion attitudes of cassava farmers. The cassava farmers’ risk attitudes were found to be significantly influenced by the effectiveness and usefulness of social interactions, and a high degree of trust as well as age, frequency of extension services, access to credit, and revenue. Conclusion By implication, attempts to introduce innovations to improve agricultural commodities value chains that target farmers must pay particular attention to the information and communication network to ensure the effectiveness and usefulness of information dissemination to farmers in an honest manner, so as to build trust; which will go a long way to reduce doubts and uncertainties (function of risk aversion). This will help achieve higher innovation adoption impacts thereby resulting in attaining the overall deliverable objectives of agricultural innovations.

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“…The asymptotic theory developed in this paper is based on the results established in the sieve extremum estimation literature (e.g., Bierens, 2014;CFT06;Chen, 2007). A semiparametric version of bivariate threshold crossing models was also considered in Marra and Radice (2011) and Ieva, Marra, Paganoni, and Radice (2014). In contrast to our setting, however, they introduced flexibility for the index function of the threshold, and not for the distribution of the unobservables.…”

Section: Introductionmentioning

confidence: 99%

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

Han

Lee

2019

J of Applied Econometrics

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Summary The purpose of this paper is to provide guidelines for empirical researchers who use a class of bivariate threshold crossing models with dummy endogenous variables. A common practice employed by the researchers is the specification of the joint distribution of unobservables as a bivariate normal distribution, which results in a bivariate probit model. To address the problem of misspecification in this practice, we propose an easy‐to‐implement semiparametric estimation framework with parametric copula and nonparametric marginal distributions. We establish asymptotic theory, including root‐n normality, for the sieve maximum likelihood estimators that can be used to conduct inference on the individual structural parameters and the average treatment effect (ATE). In order to show the practical relevance of the proposed framework, we conduct a sensitivity analysis via extensive Monte Carlo simulation exercises. The results suggest that estimates of the parameters, especially the ATE, are sensitive to parametric specification, while semiparametric estimation exhibits robustness to underlying data‐generating processes. We then provide an empirical illustration where we estimate the effect of health insurance on doctor visits. In this paper, we also show that the absence of excluded instruments may result in identification failure, in contrast to what some practitioners believe.

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A Semiparametric Bivariate Probit Model for Joint Modeling of Outcomes in STEMI Patients

Cited by 9 publications

References 29 publications

Recursive Bivariate Probit Analysis of Injury Severity and Non-Truck Improper Actions in Large Truck-Related Crashes on Florida Suburban Roads

Recursive Bivariate Probit Analysis of Injury Severity and Non-Truck Improper Actions in Large Truck-Related Crashes on Florida Suburban Roads

Social networking and risk attitudes nexus: implication for technology adoption among smallholder cassava farmers in Ghana

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

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