scholarly journals A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls

2014 ◽  
Author(s):  
William Yang Wang ◽  
Zhenhao Hua
Keyword(s):  
Regression Model ◽  
Gaussian Copula ◽  
Financial Risks

scholarly journals A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis

2019 ◽  
Vol 11 (2) ◽  
pp. 418 ◽  
Author(s):  
Yajie Zou ◽  
Xinzhi Zhong ◽  
Jinjun Tang ◽  
Xin Ye ◽  
Lingtao Wu ◽  
...  
Keyword(s):  
Regression Model ◽  
Empirical Bayes ◽  
Negative Binomial ◽  
Washington State ◽  
Gaussian Copula ◽  
Copula Model ◽  
Crash Analysis ◽  
Vehicle Collisions ◽  
Vehicle Collision

Wildlife‒vehicle collision (WVC) data usually contain two types: the reported WVC data and carcass removal data. Previous studies often found a discrepancy between the number of reported WVC and carcass removal data, and the quality of both datasets is affected by underreporting. Underreporting means the number of WVCs is not fully recorded in the database; neglecting the underreporting in WVC data may result in biased parameter estimation results. In this study, a copula regression model linking wildlife‒vehicle collisions and the underreporting outcome was proposed to consider the underreporting in WVC data. The WVC data collected from 10 highways in Washington State were analyzed using the copula regression model and the Negative Binomial (NB) model. The main findings from this study are as follows: (1) the Gaussian copula model can provide different modeling results when compared with the conventional modeling approach; (2) the hotspot identification results indicate that the Gaussian copula-based Empirical Bayes (EB) method can more accurately identify hotspots than the NB-based EB method. Thus, the proposed copula model may be a better alternative to the conventional NB model for modeling underreported WVC data.

Multisite and multivariable statistical downscaling using a Gaussian copula quantile regression model

2015 ◽  
Vol 47 (5-6) ◽  
pp. 1383-1397 ◽  
Author(s):  
M. A. Ben Alaya ◽  
F. Chebana ◽  
T. B. M. J. Ouarda
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Statistical Downscaling ◽  
Gaussian Copula ◽  
Quantile Regression Model

Ease of Doing Business and Its Impact on Inward FDI

2018 ◽  
Vol 1 (1) ◽  
pp. 52 ◽  
Author(s):  
Mohamed Tareq Hossain ◽  
Zubair Hassan ◽  
Sumaiya Shafiq ◽  
Abdul Basit
Keyword(s):  
Regression Model ◽  
Causal Relationship ◽  
World Bank ◽  
Least Square ◽  
Doing Business ◽  
Least Square Regression ◽  
Foreign Countries ◽  
Ease Of Doing Business ◽  
The Impact ◽  
Starting A Business

This study investigates the impact of Ease of Doing Business on Inward FDI over the period from 2011 to 2015 across the globe. This study measures ease of doing business using starting a business, getting credit, registering property, paying taxes and enforcing contracts. The research used a sample of 177 countries from 190 countries listed in World Bank. Least square regression model via E-views software used to examine causal relationship. The study found that ease of doing business indicators ‘Enforcing Contracts’ was found to have a positive significant impact on Inward FDI. Nevertheless, ‘Getting Credit’ and ‘Registering Property’ were found to have a negative significant impact on Inward FDI. However, ‘Starting a Business’ and ‘Paying Taxes’ have no significant impact on Inward FDI in the studied timeframe of this research. The findings of the study suggested the ease of doing business enables inward FDI through better contract enforcements, getting credit and registering property. The findings of the research will assist international managers and companies to know the importance of ease of doing business when investing in foreign countries through FDI.

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