TY - GEN
T1 - Italian firms' geographical location in high-tech industries
T2 - 1st Joint Meeting of the Societe Francophone de Classification and the Classification and Data Analysis Group of the Italian Statistical Society, SFC-CLADAG 2008
AU - Bini, Matilde
AU - Velucchi, Margherita
PY - 2011/1/1
Y1 - 2011/1/1
N2 - Recent debates in economic-statistical research concern the relationship between firms' performance and their capabilities to develop new technologies and products. Several studies argue that economic performance and geographical proximity strongly affect firms' level of technology. The aim of the paper is twofold. Firstly, we propose to generalize this approach and to develop a model to identify the relationship between the firm's technology level and some firm's characteristics. Secondly, we use an outlier detection method to identify units that affect the analysis results and the estimates stability. This analysis is implemented using a generalized regression model with a diagnostic robust approach based on forward search. The method we use reveals how the fitted regression model depends on individual observations and the results show how the firms' technology level is influenced by their geographical proximity.
AB - Recent debates in economic-statistical research concern the relationship between firms' performance and their capabilities to develop new technologies and products. Several studies argue that economic performance and geographical proximity strongly affect firms' level of technology. The aim of the paper is twofold. Firstly, we propose to generalize this approach and to develop a model to identify the relationship between the firm's technology level and some firm's characteristics. Secondly, we use an outlier detection method to identify units that affect the analysis results and the estimates stability. This analysis is implemented using a generalized regression model with a diagnostic robust approach based on forward search. The method we use reveals how the fitted regression model depends on individual observations and the results show how the firms' technology level is influenced by their geographical proximity.
UR - http://www.scopus.com/inward/record.url?scp=84888255452&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13312-1_18
DO - 10.1007/978-3-642-13312-1_18
M3 - Contribución a la conferencia
AN - SCOPUS:84888255452
SN - 9783642133114
T3 - Studies in Classification, Data Analysis, and Knowledge Organization
SP - 185
EP - 192
BT - Classification and Multivariate Analysis for Complex Data Structures
PB - Kluwer Academic Publishers
Y2 - 11 June 2008 through 13 June 2008
ER -