scholarly journals The Employment in Innovative Enterprises in Europe

2022 ◽  
Author(s):  
Lucio Laureti ◽  
Costantiello Alberto ◽  
Marco Maria Matarrese ◽  
Angelo Leogrande
Keyword(s):  
Machine Learning ◽  
Panel Data ◽  
Fixed Effects ◽  
Value Added ◽  
Machine Learning Algorithms ◽  
High Tech ◽  
Dynamic Panel ◽  
Silhouette Coefficient ◽  
High Tech Product ◽  
And Training

Abstract In this article we evaluate the determinants of the Employment in Innovative Enterprises in Europe. We use data from the European Innovation Scoreboard of the European Commission for 36 countries in the period 2000-2019 with Panel Data with Fixed Effects, Panel Data with Random Effects, Dynamic Panel, WLS and Pooled OLS. We found that the “Employment in Innovative Enterprises in Europe” is positively associated with “Broadband Penetration in Europe”, “Foreign Controlled Enterprises Share of Value Added”, “Innovation Index”, “Medium and High-Tech Product Exports” and negatively associated to “Basic School Entrepreneurial Education and Training”, “International Co-Publications”, and “Marketing or Organizational Innovators”. Secondly, we perform a cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient and we found the presence of four different clusters. Finally, we perform a comparison among eight different machine learning algorithms to predict the level of “Employment in Innovative Enterprises” in Europe and we found that the Linear Regression is the best predictor.

scholarly journals The Determinants of Design Applications in Europe

2021 ◽  
Author(s):  
Angelo Leogrande ◽  
Alberto Costantiello ◽  
Lucio Laureti ◽  
Domenico Leogrande
Keyword(s):  
Machine Learning ◽  
Panel Data ◽  
Fixed Effects ◽  
Econometric Models ◽  
Machine Learning Algorithms ◽  
Dynamic Panel ◽  
A Value ◽  
Silhouette Coefficient ◽  
Firm Investments ◽  
Intellectual Assets

Abstract In this article we estimate the level of “Design Application” in 37 European Countries in the period 2010-2019. We use data from the European Innovation Scoreboard-EIS of the European Commission. We perform four econometric models i.e., Pooled OLS, Panel Data with Random Effects, Panel Data with Fixed Effects, Dynamic Panel. We found that the level of Design Applications is negatively associated to “Enterprise Births”, “Finance and Support”, “Firm Investments” and positively associated with “Venture Capital”, “Turnover share large enterprises”, “R&D expenditure public sector”, “Intellectual Assets”. In adjunct we perform a cluster analysis with the application of the k-Means algorithm optimized with the Silhouette Coefficient and we found three different clusters. Finally, we confront eight different machine learning algorithms to predict the level of “Design Application” and we found that the Tree Ensemble is the best predictor with a value for the 30% of the dataset analyzed that is expected to decrease in mean of -12,86%.

scholarly journals The Determinants of Landscape and Cultural Heritage Among Italian Regions in the Period 2004-2019

2021 ◽  
Author(s):  
Angelo Leogrande ◽  
Alberto Costantiello ◽  
Laureti Lucio ◽  
Domenico Leogrande
Keyword(s):  
Panel Data ◽  
Cultural Heritage ◽  
Fixed Effects ◽  
Machine Learning Algorithms ◽  
Urban Dispersion ◽  
Dynamic Panel ◽  
Rural Space ◽  
Italian Regions ◽  
Level Of Concern ◽  
Using Data

Abstract We estimate the Landscape and Cultural Heritage among Italian regions in the period 2004-2019 using data from ISTAT-BES. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, Dynamic Panel. We found that the Landscape and Cultural Heritage is negatively associated with “Dissatisfaction with the landscape of the place of life”, “Illegal building”, “Density and relevance of the museum heritage”, “Internal material consumption”, “Erosion of the rural space due to abandonment”, “Availability of urban green”, and positively associated with “Pressure from mining activities”, “Erosion of the rural space by urban dispersion”, “Concern about the deterioration of the landscape”, “Diffusion of agritourism farms”, “Current expenditure of the Municipalities for culture”. Secondly, we have realized a cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient and we found two clusters in the sense of “Concern about the deterioration of the landscape”. Finally, we use eight different machine learning algorithms to predict the level of “Concern about the deterioration of the landscape” and we found that the Tree Ensemble Regression is the best predictor.

scholarly journals The SMEs Innovation in Europe

2021 ◽  
Author(s):  
Alberto Costantiello ◽  
Laureti Lucio ◽  
Leogrande Angelo
Keyword(s):  
Panel Data ◽  
Population Density ◽  
Fixed Effects ◽  
Government Procurement ◽  
Advanced Technology ◽  
High Tech ◽  
Dynamic Panel ◽  
Technology Products ◽  
Firm Investments ◽  
Intellectual Assets

Abstract In this article we investigate the determinants of SMEs Innovation in Europe. We use data from the European Innovation Scoreboard of the European Commission in the period 2000-2019 for 36 countries. Data are analyzed through Panel Data with Fixed Effects, Random Effects, Dynamic Panel at 1 Stage and WLS. Results show that the presence of Innovators is positively associated with “Enterprise births”, “Government Procurement of Advanced Technology Products”, “Firm Investments”, “Intellectual Assets”, “Sales Impacts”, “Share High and Medium High-Tech Manufacturing” and negatively associated to “FDI Net Inflows” and “Population Density”.

scholarly journals Human Resources in Europe. Estimation, Clusterization, Machine Learning and Prediction

2021 ◽  
Author(s):  
ANGELO LEOGRANDE ◽  
ALBERTO COSTANTIELLO
Keyword(s):  
Machine Learning ◽  
Human Resources ◽  
Fixed Effects ◽  
Tertiary Education ◽  
Government Procurement ◽  
Machine Learning Algorithms ◽  
Advanced Technology ◽  
High Tech ◽  
Technology Products ◽  
Using Data

Abstract We estimate the relationships between innovation and human resources in Europe using the European Innovation Scoreboard of the European Commission for 36 countries for the period 2010-2019. We perform Panel Data with Fixed Effects, Random Effects, Pooled OLS, Dynamic Panel and WLS. We found that Human resources is positively associated to “Basic-school entrepreneurial education and training”, “Employment MHT manufacturing KIS services”, “Employment share Manufacturing (SD)”, “Lifelong learning”, “New doctorate graduates”, “R&D expenditure business sector”, “R&D expenditure public sector”, “Tertiary education”. Our results also show that “Human Resources” is negatively associated to “Government procurement of advanced technology products”, “Medium and high-tech product exports”, “SMEs innovating in-house”, “Venture capital”. In adjunct we perform a clusterization with k-Means algorithm and we find the presence of three clusters. Clusterization shows the presence of Central and Northern European countries that has higher levels of Human Resources, while Southern and Eastern Europe has very low degree of Human Resources. Finally, we use seven machine learning algorithms to predict the value of Human Resources in Europe Countries using data in the period 2014-2021 and we show that the linear regression algorithm performs at the highest level.

scholarly journals Bias correction methods for dynamic panel data models with fixed effects

2017 ◽  
Vol 6 (2) ◽  
pp. 58
Author(s):  
Mohamed Abonazel
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Estimation Methods ◽  
Time Dimension ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Cross Sectional Dimension ◽  
Ls Estimator

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.

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