scholarly journals The Impact of the COVID-19 Pandemic on Cross-Border Mergers and Acquisitions’ Determinants: New Empirical Evidence from Quasi-Poisson and Negative Binomial Regression Models

Economies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 184
Author(s):  
Han-Sol Lee ◽  
Ekaterina A. Degtereva ◽  
Alexander M. Zobov
Keyword(s):  
Mergers And Acquisitions ◽  
Empirical Evidence ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Entry Modes ◽  
Cross Sectional ◽  
Cross Border ◽  
Binomial Regression ◽  
The Impact

The cross-border movement of capital has suffered due to the COVID-19 pandemic since December 2019. Nevertheless, it is unrealistic for multinational companies to withdraw giant global value chains (GVCs) overnight because of the pandemic. Instead, active discussions and achievements of deals in cross-border mergers and acquisitions (M&As) are expected in the post-COVID-19 era among various other market entry modes, considering the growing demand in high technologies in societies. This paper analyzes particular determinants of cross-border mergers and acquisitions (M&As) during the pandemic year (2020) based on cross-sectional datasets by employing quasi-Poisson and negative binomial regression models. According to the empirical evidence, COVID-19 indices do not hamper M&A deals in general. This indicates that managerial capabilities of the coronavirus, not the outbreak itself, determined locational decisions of M&A deals during the pandemic. In this vein, it is expected that the vaccination rate will become a key factor of locational decision for M&A deals in the near future. Furthermore, countries that have been outstanding in coping with COVID-19 and thus serve as a good example for other nations may seize more opportunities to take a leap forward. In addition, as hypothesized, the results present positive and significant associations with M&A deals and the SDG index, confirming the resource-based theory of internationalization. In particular, the achievement of SDGs seems to exercise much influence in developing countries for M&A bidders during the pandemic year. This indicates that the pandemic demands a new zeitgeist that pursues growth while resolving existing but disregarded environmental issues and cherishes humanitarian values, for all countries, non-exceptionally, standing at the start line of the post-COVID-19 era.

scholarly journals The impact of political instability upon the increase of terrorism in the Middle East

2021 ◽  
pp. 43-62
Author(s):  
Andrey Vadimovich Novikov
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Political Instability ◽  
Negative Binomial Regression ◽  
Severe Form ◽  
Political Regime ◽  
Level Of Education ◽  
Binomial Regression ◽  
Different Types ◽  
The Impact

The key goal of the article is to examine whether the domestic political instability associated with the “Arab Spring” caused the subsequent surge of global terrorism, which reached its peak in 2014. The author reviews six different types of domestic political instability: antigovernment demonstrations, national strikes, government crises, government repression, disturbances, and revolutions. Using the regression models, the author clarifies the impact of such factors as the level of education, Internet access, economic development, democratization indexes, and the degree of religious and ethnic fragmentariness. Analysis is conducted on the results of the models separately for different types of political regimes, forms of domestic political instability, and global regions. The results of construction and analysis a number of negative binomial regression models testify to the support of “escalation effect”, which implies that heightened intensity of domestic political instability leads to the surge of terrorist attacks. More severe forms of domestic political instability, namely repression and disturbances, generate a higher level of terrorism; however, revolution, as the most severe form of domestic political instability does not produce such effect. The formulated conclusions are also substantiated by the fact that certain forms of political instability have a different impact upon terrorism and its peculiarities, depending on the geographical region and the type of political regime.

The positive association between employment and self-reported mental health in the USA: a robust application of marginalized zero-inflated negative binomial regression (MZINB)

2020 ◽  
Vol 42 (2) ◽  
pp. 340-352
Author(s):  
Chinaeke Eric ◽  
Gwynn Melanie ◽  
Hong Yuan ◽  
Zhang Jiajia ◽  
Olatosi Bankole
Keyword(s):  
Mental Health ◽  
Chronic Conditions ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Chronically Ill ◽  
Interactive Effects ◽  
Behavioral Risk ◽  
Cross Sectional ◽  
Binomial Regression ◽  
The Impact

Abstract Background Few studies have assessed the impact of employment on mental health among chronically ill patients. This study investigated the association between employment and self-reported mental unhealthy days among US adults. Methods For this cross-sectional cohort study, we pooled 2011–2017 Behavioral Risk Factor Surveillance System (BRFSS) survey data. We examined the association between employment and mental health in nine self-reported chronic conditions using marginalized zero-inflated negative binomial regression (MZINB). All analyses were conducted using SAS statistical software 9.4. Results Respondents (weighted n = 245 319 917) were mostly white (77.16%), aged 18–64 (78.31%) and employed (57.08%). Approximately 10% of respondents reported one chronic condition. Expected relative risk of mental unhealthy days was highest for employed respondents living with arthritis (RR = 1.70, 95% CI = [1.66, 1.74]), COPD (RR = 1.45, 95% CI = [1.41, 1.49]) and stroke (RR = 1.31, 95% CI = [1.25, 1.36]) compared to unemployed respondents. Employed males had 25% lower risk of self-reported mental unhealthy days compared to females. Conclusions Results show the interactive effects of employment on self-reported mental health. Employment may significantly impact on self-reported mental health among patients suffering from chronic conditions than those without chronic conditions.

scholarly journals Being Friends with or Rejected by Classmates: Aggression Toward Same- and Cross-Ethnic Peers

2019 ◽  
Vol 49 (3) ◽  
pp. 678-692
Author(s):  
Marianne Hooijsma ◽  
Gijs Huitsing ◽  
Jan Kornelis Dijkstra ◽  
Andreas Flache ◽  
René Veenstra
Keyword(s):  
Aggressive Behavior ◽  
Ethnic Group ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Ethnic Background ◽  
Cross Sectional ◽  
Ethnic Boundaries ◽  
Binomial Regression ◽  
Peer Nomination

AbstractWhereas previous research suggests that adolescents’ aggressive behavior in itself does not highlight ethnic boundaries, it remains unclear whether classmates’ responses to same- and cross-ethnic aggression strengthen ethnic boundaries. This study examined how adolescents’ aggression toward same- and cross-ethnic peers relates to the positive (friendship) and negative (rejection) relationship nominations they receive from same- and cross-ethnic classmates. Cross-sectional peer nomination data on 917 Dutch and 125 Turkish adolescents in 56 secondary schools were analyzed (mean age = 14.9 year; 51.4% boys). Adolescents received more friendship nominations from same-ethnic than from cross-ethnic classmates, but were not more rejected by cross-ethnic than same-ethnic classmates. Multilevel Poisson and negative binomial regression models showed that, irrespective of aggressor’s ethnic background, adolescents’ aggressive behavior was related to rejection by classmates from the ethnic group that was the target of aggression and to being befriended by classmates from the ethnic group that was not the target of aggression. Specifically, both Dutch and Turkish adolescents who were aggressive toward Dutch peers were rejected by Dutch classmates and befriended by Turkish classmates and vice versa. These findings suggest that classmates’ positive and negative responses to adolescents are related to adolescents’ aggressive behavior based on the ethnic background of the victim, not on the ethnic background of the aggressor. This suggests that integration between ethnic groups in schools relates to aggression in general, not only cross-ethnic aggression.

Managing Inflation

2016 ◽  
Vol 63 (1) ◽  
pp. 77-87 ◽  
Author(s):  
William H. Fisher ◽  
Stephanie W. Hartwell ◽  
Xiaogang Deng
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Excess Zeros ◽  
Binomial Regression ◽  
Negative Binomial Models ◽  
Using Data ◽  
Over Dispersion ◽  
Binomial Models

Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany

2018 ◽  
Vol 37 (20) ◽  
pp. 3012-3026 ◽  
Author(s):  
Saptarshi Chatterjee ◽  
Shrabanti Chowdhury ◽  
Himel Mallick ◽  
Prithish Banerjee ◽  
Broti Garai
Keyword(s):  
Health Care ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Health Care Demand ◽  
Binomial Regression

scholarly journals Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA

2019 ◽  
Vol 11 (17) ◽  
pp. 1958 ◽  
Author(s):  
Hanlin Zhou ◽  
Lin Liu ◽  
Minxuan Lan ◽  
Bo Yang ◽  
Zengli Wang
Keyword(s):  
Negative Binomial ◽  
Infrared Imaging ◽  
Negative Impact ◽  
Positive Impact ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Binomial Regression ◽  
Street Robbery ◽  
The Impact

Previous research has recognized the importance of edges to crime. Various scholars have explored how one specific type of edges such as physical edges or social edges affect crime, but rarely investigated the importance of the composite edge effect. To address this gap, this study introduces nightlight data from the Visible Infrared Imaging Radiometer Suite sensor on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS) to measure composite edges. This study defines edges as nightlight gradients—the maximum change of nightlight from a pixel to its neighbors. Using nightlight gradients and other control variables at the tract level, this study applies negative binomial regression models to investigate the effects of edges on the street robbery rate and the burglary rate in Cincinnati. The Akaike Information Criterion (AIC) of models show that nightlight gradients improve the fitness of models of street robbery and burglary. Also, nightlight gradients make a positive impact on the street robbery rate whilst a negative impact on the burglary rate, both of which are statistically significant under the alpha level of 0.05. The different impacts on these two types of crimes may be explained by the nature of crimes and the in-situ characteristics, including nightlight.

What Do We Know About Changing Economic Activity of Firms?

2019 ◽  
pp. 232102221886979
Author(s):  
Radhika Pandey ◽  
Amey Sapre ◽  
Pramod Sinha
Keyword(s):  
Economic Activity ◽  
Regression Models ◽  
Statistical Approach ◽  
Negative Binomial ◽  
Large Data ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Binomial Regression ◽  
History Of

Identification of primary economic activity of firms is a prerequisite for compiling several macro aggregates. In this paper, we take a statistical approach to understand the extent of changes in primary economic activity of firms over time and across different industries. We use the history of economic activity of over 46,000 firms spread over 25 years from CMIE Prowess to identify the number of times firms change the nature of their business. Using the count of changes, we estimate Poisson and Negative Binomial regression models to gain predictability over changing economic activity across industry groups. We show that a Poisson model accurately characterizes the distribution of count of changes across industries and that firms with a long history are more likely to have changed their primary economic activity over the years. Findings show that classification can be a crucial problem in a large data set like the MCA21 and can even lead to distortions in value addition estimates at the industry level. JEL Classifications: D22, E00, E01

Traffic accident modeling: some statistical issues

2006 ◽  
Vol 33 (9) ◽  
pp. 1115-1124 ◽  
Author(s):  
Z Sawalha ◽  
T Sayed
Keyword(s):  
Road Safety ◽  
Regression Models ◽  
Model Building ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Outlier Analysis ◽  
Accident Prediction ◽  
Binomial Regression ◽  
Statistical Issues

Accident prediction models are invaluable tools that have many applications in road safety analysis. However, there are certain statistical issues related to accident modeling that either deserve further attention or have not been dealt with adequately in the road safety literature. This paper discusses and illustrates how to deal with two statistical issues related to modeling accidents using Poisson and negative binomial regression. The first issue is that of model building or deciding which explanatory variables to include in an accident prediction model. The study differentiates between applications for which it is advisable to avoid model over-fitting and other applications for which it is desirable to fit the model to the data as closely as possible. It then suggests procedures for developing parsimonious models, i.e., models that are not over-fitted, and best-fit models. The second issue discussed in the paper is that of outlier analysis. The study suggests a procedure for the identification and exclusion of extremely influential outliers from the development of Poisson and negative binomial regression models. The procedures suggested for model building and conducting outlier analysis are more straightforward to apply in the case of Poisson regression models because of an added complexity presented by the shape parameter of the negative binomial distribution. The paper, therefore, presents flowcharts detailing the application of the procedures when modeling is carried out using negative binomial regression. The described procedures are then applied in the development of negative binomial accident prediction models for the urban arterials of the cities of Vancouver and Richmond located in the province of British Columbia, Canada. Key words: accident prediction models, overfitting, parsimony, outlier analysis, Poisson regression, negative binomial regression.

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