Random parameters multivariate tobit and zero-inflated count data models: Addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis

Anastasopoulos, Panagiotis Ch.

doi:10.1016/j.amar.2016.06.001

Cited by 126 publications

(61 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The average marginal effect for the NB model implies that the number of accidents increased by 0.421. Our findings are conform to previous results (Abdel-Aty & Radwan, 2000; Agbelie, 2016a; Anastasopoulos, 2016;Kumara & Chin, 2003;Venkataraman et al, 2013;Venkataraman. et al, 2011).…”

Section: Empirical Results and Their Discussionsupporting

confidence: 94%

Untitled

2020

International Journal of Public Policy and Administration Resea

View full text Add to dashboard Cite

Article History KeywordsTraffic accidents Hazardous road locations Negative binomial model Random effects negative binomial model Tunisia Efficient transportation systems Traffic policies. JEL Classification:R41, R42.The main objective of this study was to investigate the impact of factors linked to the geometric, roadway and traffic flow characteristics on traffic accident occurrence at Hazardous Road Locations (HRL) in the region of Sousse, Tunisia using longitudinal data of 52 HRLs for an 11-year monitoring period from January 1, 2004 to December 31, 2014. The results of estimation of Negative Binomial model and the Random Effects Negative Binomial model reported that the different variables representing the presence of rural segment, the curved alignment, the visibility, the average daily traffic volume, the presence of public lighting, the number of lane, the presence of vertical/horizontal sign, the presence of drainage system, the presence of paved shoulder, the roadway surface condition, and the presence of major road were the main significant variables influencing accident occurrences. Therefore, the results could be useful for deciders in the transport sector in order to implement effective measures to improve road safety by adopting proper sound use practices, establishing efficient transportation systems and formulating appropriate traffic policies and legislation. Contribution/Originality:This is one of the few studies which have investigated the occurrence of traffic accidents at Hazardous Road Locations in the region of Sousse (Tunisia) as a function of various explanatory factors linked to the geometric characteristics, roadway characteristics and traffic flow characteristics. 1443 persons were killed in 2016 and 11035 were injured in traffic-related crashes on the roads (National International

show abstract

Section: Empirical Results and Their Discussionsupporting

confidence: 94%

Untitled

2020

International Journal of Public Policy and Administration Resea

View full text Add to dashboard Cite

show abstract

“…Model 2 regressed the factors of TE rate in EU-28 region for the term starting from 1990 to 2013 (see Table 2). According to previous papers (Anastasopoulos, 2016) the Tobit estimator is highly efficient and consistent with results of previous study that there is positive correlation between lnCI and TE. Also, shows that GDP positively and significantly influence the rate of TE at 1% statistical level.…”

Section: Resultssupporting

confidence: 88%

“…Model 1 shows the influence of specific-country determinants and environmental (external) economics determinants on the TE rate in the EU-28 zone starting from 1990 till 2013 see Table 1. Following previous study Anastasopoulos (2016) Tobit estimator is highly sufficient and aligned with findings of previous study Alsaleh et al (2017) regarding the positive correlation of lnCI and TE. Moreover, shows that lnLI impact positively and significantly on the dependent variable TE at the 1% statistical level.…”

Section: Resultssupporting

confidence: 83%

Estimating the Economic Determinants of Technical Efficiency of Bioenergy in EU-28: An Application of Tobit Analysis

Alsaleh¹,

Abdul‐Rahim²

2019

Journal of Social Economics Research

View full text Add to dashboard Cite

Article History KeywordsBioenergy industry Technical efficiency Capital input Labour input Gross domestic product Real interest rate EU-28 region. JEL Classification:Q20; Q01; Q56.This study identified the effects of economic factors on technical efficiency (TE) rate in the bio-energy section for the European Union zone (EU-28). A Tobit framework estimation was used to investigate the economic factors of TE rate in the bio-energy sector for the EU-28 zone during the period among 1990 through 2013. The findings point that economic determinants have a significant impact on TE rate of the bioenergy sector in the EU-28 countries. The empirical findings suggest that labor input and gross domestic product (GDP) significantly affected the TE of the bio-energy sector in the EU-28 zone through the duration of this paper. The results obviously inviting governors and politicians to investigate the TE rate of the bio-energy sector within the EU-28 zone. This paper gives further facts and details to the senates of the bio-energy sectors, as they require to get a further comprehension of the impact TE has on bio-energy production execution. In addition, the findings of this paper have suggestions for financiers who concentrates significantly on revenues from their directinvestments. Contribution/Originality:This study contributes to the existing literature in investigating the TE of the bioenergy industry in the EU-28 region. This study uses new estimation methodologies such as the Data Envelopment Analysis (DEA) and the Tobit Model to investigate the impact of economic determinants on the TE of the bioenergy industry in the EU28 region during the period between 1990 and 2013. 1990 to 16500 TOE in 2013, while developing countries gross inland consumption increased from 500 TOE in 1990 to 24750 TOE in 2013. In addition, the CO2 trend shows unstable reduction during the period between 1990 and 2013; this refers to that bioenergy production is insufficient and could not meet the bioenergy consumption in developing countries. In developed countries (such as; Germany, France, UK, Finland, etc.), the trend of CO2 emission reduction did not decrease significantly and was unstable during the period 1990-2013. Moreover, bioenergy production trend shows that in developed countries the production increased from 36000 TOE in 1990 to 60000 TOE in 2013. On the other hand, bioenergy gross inland consumption in developed countries during the period between 1990 through 2013 has increased significantly in comparison with the production trend from the but lnLI giving the largest contribution. On the other hand, lnCI factor does not participate significantly in comparison with lnLI. Second macroeconomic factor is lnGDP, which was the highest significant external factor that participated to enhancing the TE rate in comparison with lnRIR. Thus, the lnGDP positively and significantly impact the TE in Model 1 and Model 2 to provide the highes contributor in the TE rate comparing with lnRIR.The findings showed in this paper obviously that TE affected...

show abstract

“…To test for potential censoring issues due to the non‐negativity of the dependent variable, we perform a robustness check by using a Tobit regression specification for the inventory dynamics models of Equations (2) and (3). Tobit regressions (Tobin, 1958) explicitly account for non‐negative dependent variables and are widely used in the economics and insurance literature (e.g., Bourassa et al, 2008; Anastasopoulos, 2016). Results from the Tobit model are consistent with the results reported in this paper.…”

Section: Resultsmentioning

confidence: 99%

Inventory agility upon demand shocks: Empirical evidence from the financial crisis

Udenio

Hoberg

Fransoo

2018

J of Ops Management

View full text Add to dashboard Cite

The objective of this paper is to identify antecedents of inventory agility (i.e., the capability to quickly adapt inventories to changes in demand) upon demand shocks based on the awareness‐motivation‐capability (AMC) framework and to explore the link between inventory agility and financial performance. We introduce an empirical measure of inventory agility based on the deviation of relative inventories (i.e., inventory days) from their forecasted values. We hypothesize that firms with higher awareness, motivation, and capabilities are associated with higher inventory agility in the presence of demand shocks. We define two empirical measures for each of the three dimensions of the AMC framework in the context of inventory agility: awareness (i.e., market orientation and technology orientation), motivation (i.e., gross margin and liquidity), and capabilities (i.e., inventory management capability and resource availability). In addition, we incorporate the constraining factor model (CFM) into the AMC framework, thus allowing for complementarity among the different measures. In this view, the influence of each of the measures on inventory agility varies according to which of the measures is the constraining factor for a given firm. The 2008 financial crisis may have tested firms' inventory agility more than any other crisis since the Great Depression, as an unprecedented collapse of demand coincided with a reduction in credit availability. Therefore, for our analysis, we use firm‐level empirical data from 1263 public U.S. manufacturing firms for the 2005–2011 period. We find that firms' motivation and capabilities are key factors associated with inventory agility. Through the CFM, we show that identifying the constraining factors leads to a more refined understanding of the moderating effects of the antecedents of inventory agility. In a separate analysis, we find that inventory agility is positively associated with a number of financial performance metrics during crisis periods. We distinguish between inventory underages and overages and find that, during the crisis, they are both associated with lower financial performance. Furthermore, we find evidence that higher underages (overages) magnify the effect of overages (underages). Among other managerial insights, our findings suggest that the use of inventory reductions as a quick way to increase liquidity must be gauged against their potential impact on other aspects of financial performance.

show abstract

Random parameters multivariate tobit and zero-inflated count data models: Addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis

Cited by 126 publications

References 53 publications

Untitled

Untitled

Estimating the Economic Determinants of Technical Efficiency of Bioenergy in EU-28: An Application of Tobit Analysis

Inventory agility upon demand shocks: Empirical evidence from the financial crisis

Contact Info

Product

Resources

About