scholarly journals Factor Endowments, Economic Integration, Round-Tripping, and Inward FDI: Evidence from the Baltic Economies

2021 ◽  
Vol 14 (8) ◽  
pp. 348
Author(s):  
Andrzej Cieślik ◽  
Oleg Gurshev
Keyword(s):  
Multinational Firms ◽  
Bayesian Model Averaging ◽  
Dominant Role ◽  
Estimation Method ◽  
Model Averaging ◽  
Physical Capital ◽  
Round Trip ◽  
Factor Endowments ◽  
Vertical Foreign Direct Investment ◽  
The Baltic

This paper studies the location choice of foreign multinational firms in the Baltic economies of Estonia, Latvia, and Lithuania using a knowledge-and-physical capital model across 2004–2017. We used the Bayesian model averaging estimation method to investigate a set of possible factors that drive inward FDI. Our analysis demonstrates that factor endowments play a dominant role in driving vertical foreign direct investment, while external market barriers generate “tariff-jumping” FDI. Our analysis quantifies the effects of round-trip FDI, European integration, and external bilateral free trade agreements vis-à-vis inward FDI in the Baltics.

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.

The use of meta-analysis in research on corporate cash holdings speed of adjustment

2019 ◽  
Vol 64 (5) ◽  
pp. 48-73
Author(s):  
Fryderyk Mirota
Keyword(s):  
Selection Bias ◽  
Bayesian Model Averaging ◽  
Meta Analysis ◽  
Estimation Method ◽  
Cash Holdings ◽  
Model Averaging ◽  
Model Specification ◽  
Data Set ◽  
Speed Of Adjustment ◽  
Corporate Cash Holdings

In empirical research significant diversity of corporate cash holdings speed of adjustment (SOA) estimates can be observed. It is possible that some of the results are affected by publication selection bias. Articles whose results are clearly in line with economic theories may be preferred by authors and reviewers and, consequently, conclusions from this area can be published more frequently. The aim of this article is to verify whether there is a publication selection bias with respect to studies related to corporate cash holdings adjustments and to investigate the sources of heterogeneity in cash holdings SOA estimates. The statistical method used in the study was meta-analysis, which allows a combined analysis of the results from independent research. Meta-analysis enables to verify the occurrence of the publication selection bias and to explain the heterogeneity of the results presented in articles. The study was based on data collected asa result of a review of the literature published between 2003 and 2017. On the basis of information on 402 estimates from 58 different studies it has been shown that the publication selection bias does not occur. The Bayesian Model Averaging was used for modelling. It was identified that the characteristics associated with the data set used in the study, model specification and the estimation method significantly affect the hetero-geneity of corporate cash holdings SOA estimates. This diversity is determined, among others, by the choice of estimation method, length of the period covered by the analysis and characteristics of the market environment of the concerned entities.

Incorporating stakeholders' knowledge to stock assessment: Central Baltic herring

2013 ◽  
Vol 70 (4) ◽  
pp. 591-599 ◽  
Author(s):  
Samu Mäntyniemi ◽  
Päivi Haapasaari ◽  
Sakari Kuikka ◽  
Raimo Parmanne ◽  
Maiju Lehtiniemi ◽  
...  
Keyword(s):  
Bayesian Model Averaging ◽  
Stock Assessment ◽  
Model Averaging ◽  
Structured Interview ◽  
Clupea Harengus ◽  
Quantitative Modelling ◽  
Assessment Data ◽  
Probability Models ◽  
The Baltic Sea ◽  
The Baltic

We present a method by which the knowledge of stakeholders can be taken into account in stock assessment. The approach consists of a structured interview process followed by quantitative modelling of the answers. The outcome is a set of probability models, each describing the views of different stakeholders. Individual models are then merged to a large model by applying the techniques of Bayesian model averaging, and this model is conditioned on stock assessment data. As a result, the views of interviewed stakeholders have been taken into account and weighed based on how well their views are supported by the observed data. We applied this method to the Baltic Sea herring (Clupea harengus) stock assessment by interviewing six stakeholders and conditioning the resulting models on stock assessment data provided by the International Council for the Exploration of the Sea.

scholarly journals Between the Eurozone crisis and the Brexit: the decade of British outward FDI into Europe

2021 ◽  
Author(s):  
Andrzej Cieślik ◽  
Oleg Gurshev ◽  
Sarhad Hamza
Keyword(s):  
Multinational Firms ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Political Stability ◽  
Trade Association ◽  
The European Union ◽  
Eurozone Crisis ◽  
Outward Fdi ◽  
Negative Effects ◽  
Profit Shifting

AbstractThis paper investigates the determinants of outward foreign direct investment (OFDI) of British multinational firms in the European Union (EU) and the European Free Trade Association members across 2009–2019 using Bayesian model averaging. We find evidence that supports the existence and dynamic behavior of the East–West structure of FDI between three groups of countries: core-EU, Central and Eastern European economies (CEE), and the Nordics. Further, we document the importance of relative market size, urbanization, the rule of law in attaining horizontal FDI in the core-EU economies. In turn, infrastructure spending and enhanced political stability are the most important drivers for FDI in CEE (post-2000 accession). Finally, our results highlight the negative effects of the Eurozone crisis and Brexit anticipation on British OFDI activity in the region. The findings remain robust when accounting for potential MNE profit shifting to partners such as Ireland, Luxembourg, and alike.

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.

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