Bireprob: An Estimator for Bivariate Random-effects Probit Models

Plum, Alexander

doi:10.1177/1536867x1601600111

Cited by 13 publications

(12 citation statements)

References 19 publications

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“…e estimation can make full use of quasirandom numbers (Halton draws) and maximum simulated likelihood to achieve the correlation between the error terms of both processes. For more details about the bivariate probit and random-effects probit models, refer to Yildirim et al [40] and Plum [42].…”

Section: Methodsmentioning

confidence: 99%

Insights on Crash Injury Severity Control from Novice and Experienced Drivers: A Bivariate Random-Effects Probit Analysis

Xiao

Yuan

Kang

et al. 2021

Discrete Dynamics in Nature and Society

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This study intended to investigate the crash injury severity from the insights of the novice and experienced drivers. To achieve this objective, a bivariate panel data probit model was initially proposed to account for the correlation between both time-specific and individual-specific error terms. The geocrash data of Las Vegas metropolitan area from 2014 to 2017 were collected. In order to estimate two (seemingly unrelated) nonlinear processes and to control for interrelations between the unobservables, the bivariate random-effects probit model was built up, in which injury severity levels of novice and experienced drivers were addressed by bivariate (seemingly unrelated) probit simultaneously, and the interrelations between the unobservables (i.e., heterogeneity issue) were accommodated by bivariate random-effects model. Results revealed that crash types, vehicle types of minor responsibility, pedestrians, and motorcyclists were potentially significant factors of injury severity for novice drivers, while crash types, driver condition of minor responsibility, first harm, and highway factor were significant for experienced drivers. The findings provide useful insights for practitioners to improve traffic safety levels of novice and experienced drivers.

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Section: Methodsmentioning

confidence: 99%

Insights on Crash Injury Severity Control from Novice and Experienced Drivers: A Bivariate Random-Effects Probit Analysis

Xiao

Yuan

Kang

et al. 2021

Discrete Dynamics in Nature and Society

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“…The other objective of this paper is to model the determinants of households' storage motives: consumption or market. We utilize a bivariate probit model that enables modeling the two decisions simultaneously and explore their potential relationship (Plum 2016). The model also allows for interactions between unobserved factors associated with the two outcomes.…”

Section: Determinants Of Grain Storagementioning

confidence: 99%

Consumption smoothing and price enhancement motives for grain storage: empirical perspectives from rural Ethiopia

Tesfaye¹,

Gebremariam

2020

Agric Econ

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Consumption smoothing and temporal price arbitrage are the two main economic motives for grain storage in semi-subsistence economies. Nonetheless, little has been documented on the determinants of households’ grain storage behavior. Using a rich panel data from maize producing households in Ethiopia, this paper investigates the determinants of households’ decision to store grain for consumption and/or for the market. We found that grain storage is mainly determined by climatic factors, technological innovations, and shocks. Grain storage for consumption and for the markets are not mutually exclusive decisions. While the decisions made by the households to store maize for consumption and for the markets are influenced by a host of similar factors, the effects of climatic factors and infrastructure are found to be heterogeneous.

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“…This concludes the full description of our baseline econometric model. All estimations are undertaken in the statistical data analysis program Stata 13.1 and using the command bireprob ([65]).…”

Section: Data and Descriptive Statisticsmentioning

confidence: 99%

Local unemployment changes the springboard effect of low pay: Evidence from England

Plum

Knies

2019

PLoS ONE

Self Cite

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There is considerable debate on whether the employment and earnings prospects are better for those on low pay or for the unemployed. Low-pay work tends to be undertaken more locally but no empirical analysis has focused on how local opportunities alter prospects. Using Understanding Society data for England matched with local unemployment rates, we estimate dynamic random effects panel models, which show robust evidence that the future unemployment risk is lower for those who are currently on low pay compared to those who are currently unemployed. The low-paid also have a higher chance than the unemployed of becoming higher-paid. These findings are most marked in neighbourhoods with high unemployment.

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Bireprob: An Estimator for Bivariate Random-effects Probit Models

Cited by 13 publications

References 19 publications

Insights on Crash Injury Severity Control from Novice and Experienced Drivers: A Bivariate Random-Effects Probit Analysis

Insights on Crash Injury Severity Control from Novice and Experienced Drivers: A Bivariate Random-Effects Probit Analysis

Consumption smoothing and price enhancement motives for grain storage: empirical perspectives from rural Ethiopia

Local unemployment changes the springboard effect of low pay: Evidence from England

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