scholarly journals Institutional Determinants of Budgetary Expenditures. A BMA-Based Re-Evaluation of Contemporary Theories for OECD Countries

2020 ◽  
Vol 12 (10) ◽  
pp. 4104
Author(s):  
Krzysztof Beck ◽  
Michał Możdżeń
Keyword(s):  
Path Dependence ◽  
Institutional Economics ◽  
Bayesian Model Averaging ◽  
Strong Support ◽  
Model Averaging ◽  
Veto Players ◽  
Public Expenditures ◽  
Political Budget Cycle ◽  
Budget Cycle ◽  
Institutional Determinants

The article tackles the problem of the most important institutional determinants of public expenditures. Within the traditions of public choice and institutional economics, it tests several theories ranging from the fiscal commons framework, Political Business/Budget Cycle (PBC) and path dependence to veto players theory. Its novelty compared to previous research stems from an attempt to test several theories simultaneously, dealing with model uncertainty by using sensitivity analysis within the Bayesian Model Averaging framework with a vast prior structure in terms of model, g and multicollinearity dilution priors. The results confirm several hypotheses tested in the area of fiscal management across the recent decades within the group of developed economies, giving especially strong support to the tragedy of the fiscal commons and path dependence concepts, while only partial support to veto players theory. In contrast, explanations based on political budget cycle (PBC) theory are dismissed. Among other interesting findings reported in the study, Scandinavian countries turn out to be the most fiscally responsible when other institutional factors are taken into account. Similarly, contrary to other recent research into the issue of EU fiscal institutional framework, Euro area countries are characterized by limited public expenditures.

Determinants of corruption in developing countries: case of Tunisia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Azzouz Zouaoui ◽  
Mounira Ben Arab ◽  
Ahmad Mohammed Alamri
Keyword(s):  
Developing Countries ◽  
Unemployment Rate ◽  
Bayesian Model Averaging ◽  
Estimation Method ◽  
Model Averaging ◽  
Public Expenditures ◽  
Annual Data ◽  
Content Type ◽  
Determinants Of Corruption ◽  
The Government

Purpose This paper aims to investigate the economic, political or sociocultural determinants of corruption in Tunisia. Design/methodology/approach To better understand the main determinants of corruption in Tunisia. This study uses The Bayesian Model Averaging (BMA) model, which allows us to include a large number of explanatory variables and for a shorter period. Findings The results show that economic freedom is the most important variable of corruption in Tunisia. In second place comes the subsidies granted by the government, which is one of the best shelters of corruption in Tunisia through their use for purposes different from those already allocated to them. Third, this paper finds the high unemployment rate, which, in turn, is getting worse even nowadays. The other three factors considered as causal but of lesser importance are public expenditures, the human development index (HDI) and education. Education, the HDI and the unemployment rate are all socio-economic factors that promote corruption. Originality/value The realization of this study will lead to triple net contributions. The first is to introduce explicitly and simultaneously political, social and economic determinants of corruption in developing countries. Second, unlike previous studies based on the simple and generalized regression model, the present research uses another novel and highly developed estimation method. More precisely, this study uses the BMA model, on the set of annual data for a period of 1998–2018. The third contribution of this research resides in the choice of the sample.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models

scholarly journals KBoost: a new method to infer gene regulatory networks from gene expression data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luis F. Iglesias-Martinez ◽  
Barbara De Kegel ◽  
Walter Kolch
Keyword(s):  
Breast Cancer ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
R Package ◽  
Breast Cancer Patients ◽  
Sources Of Information ◽  
Cancer Subtypes ◽  
Gene Regulatory

AbstractReconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.

scholarly journals Identification of Mortality Risks in the Advancement of Old Age: Application of Proportional Hazard Models Based on the Stepwise Variable Selection and the Bayesian Model Averaging Approach

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1098
Author(s):  
Ewelina Łukaszyk ◽  
Katarzyna Bień-Barkowska ◽  
Barbara Bień
Keyword(s):  
Vitamin D ◽  
Mortality Rate ◽  
Plasma Level ◽  
Variable Selection ◽  
Lymphocyte Count ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Multivariable Analysis ◽  
Model Averaging ◽  
Nutritional Risk Index

Identifying factors that affect mortality requires a robust statistical approach. This study’s objective is to assess an optimal set of variables that are independently associated with the mortality risk of 433 older comorbid adults that have been discharged from the geriatric ward. We used both the stepwise backward variable selection and the iterative Bayesian model averaging (BMA) approaches to the Cox proportional hazards models. Potential predictors of the mortality rate were based on a broad range of clinical data; functional and laboratory tests, including geriatric nutritional risk index (GNRI); lymphocyte count; vitamin D, and the age-weighted Charlson comorbidity index. The results of the multivariable analysis identified seven explanatory variables that are independently associated with the length of survival. The mortality rate was higher in males than in females; it increased with the comorbidity level and C-reactive proteins plasma level but was negatively affected by a person’s mobility, GNRI and lymphocyte count, as well as the vitamin D plasma level.

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