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 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 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.

scholarly journals The Interplay of Having an Abortion, Relationship Satisfaction, and Union Dissolution

2021 ◽  
Vol 46 ◽  
Author(s):  
Kristin Hajek
Keyword(s):  
Panel Data ◽  
Relationship Satisfaction ◽  
Fixed Effects ◽  
Union Dissolution ◽  
Prospective Data ◽  
Causal Mechanisms ◽  
Significant Difference ◽  
Negative Effect ◽  
Using Data ◽  
Fixed Effects Regression

This study researches the associations between having an abortion, relationship satisfaction, and union dissolution. Empirical evidence on this topic is scarce, and there is a pronounced lack of studies analysing longitudinal data: Most previous studies have used data from women recruited from abortion clinics who are about to undergo an abortion, and therefore do not incorporate a prospective measure of relationship satisfaction pre-pregnancy. Panel studies, on the other hand, collect prospective data on various topics and allow for the estimation of more advanced models that can help identify causal mechanisms. Using data from the German Family Panel pairfam in combination with pooled logistic regressions, discrete-time event history models, as well as fixed effects regression models, this study compares relationships up to nine years before having had an abortion and eight years afterwards. The findings of the analyses can neither confirm that relationship satisfaction acts as a confounding factor that influences both the likelihood of terminating a pregnancy and union dissolution, nor as a mediating factor between having an abortion and union dissolution. A negative effect of having an abortion on relationship satisfaction appears to be only temporary. In the year of an abortion, relationship satisfaction decreases slightly. In the following years, a significant difference in relationship satisfaction to pre-abortion years is no longer visible. By using panel data, the temporal order of events can be retraced, resulting in the discovery that relationship satisfaction and union dissolution do not change drastically from pre-abortion values after having an abortion. * This article belongs to a special issue on "Identification of causal mechanisms in demographic research: The contribution of panel data".

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