The Experts below are selected from a list of 255585 Experts worldwide ranked by ideXlab platform
Geoffrey J. D. Hewings - One of the best experts on this subject based on the ideXlab platform.
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linkages and multipliers in a multiregional framework integration of alternative approaches
MPRA Paper, 2005Co-Authors: Joaquim Jose Martins Guilhoto, Michael Sonis, Geoffrey J. D. HewingsAbstract:In this paper, two literatures that have explored the structure of economies are brought together. In the first case, the approaches to Key Sector identification (initially associated with Hirschman and Rasmussen) that were modified by Cella, Clements and Rossi and Guilhoto et al. to reveal what may be referred to a pure linkage approach are related to the concerns of Miyazawa and his identification of internal and external multiplier effects. While Miyazawa was interested mainly in identifying the sources of change in an economy, his approach shares considerable commonality with the new ideas in Key Sector identification in which a Sector or set of Sectors are separated from the rest of the economy. Hence, in both cases, a decomposition of the economy needs to be considered; the present paper reveals the similarity of perspective and provides the formal link between the two methodologies.
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The Identification of Structure at the Sectoral Level: a Reformulation of the Hirschman–Rasmussen Key Sector Indices
Economic Systems Research, 1992Co-Authors: Federico A. Cuello, Fayçal Mansouri, Geoffrey J. D. HewingsAbstract:The Hirschman–Rasmussen Key Sector identification indices are reformulated to correct for their sensitivity to large multiplier values in the Leontief inverse matrix. This leads to four results: (1) the generalized Key Sector indices; (2) the generalized coefficients of variation; (3) a likelihood ratio test, obtained from computing the likelihood function of each sample of Cauchy-distributed indices; and (4) the elasticity of intermediate Sectoral supply response. Empirical analysis reveals that the methods proposed identify more accurately the structure of Washington State, while not capturing the spread of the linkages associated with that structure.
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the identification of structure at the Sectoral level a reformulation of the hirschman rasmussen Key Sector indices
Economic Systems Research, 1992Co-Authors: Federico A. Cuello, Fayçal Mansouri, Geoffrey J. D. HewingsAbstract:The Hirschman–Rasmussen Key Sector identification indices are reformulated to correct for their sensitivity to large multiplier values in the Leontief inverse matrix. This leads to four results: (1) the generalized Key Sector indices; (2) the generalized coefficients of variation; (3) a likelihood ratio test, obtained from computing the likelihood function of each sample of Cauchy-distributed indices; and (4) the elasticity of intermediate Sectoral supply response. Empirical analysis reveals that the methods proposed identify more accurately the structure of Washington State, while not capturing the spread of the linkages associated with that structure.
Federico A. Cuello - One of the best experts on this subject based on the ideXlab platform.
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The Identification of Structure at the Sectoral Level: a Reformulation of the Hirschman–Rasmussen Key Sector Indices
Economic Systems Research, 1992Co-Authors: Federico A. Cuello, Fayçal Mansouri, Geoffrey J. D. HewingsAbstract:The Hirschman–Rasmussen Key Sector identification indices are reformulated to correct for their sensitivity to large multiplier values in the Leontief inverse matrix. This leads to four results: (1) the generalized Key Sector indices; (2) the generalized coefficients of variation; (3) a likelihood ratio test, obtained from computing the likelihood function of each sample of Cauchy-distributed indices; and (4) the elasticity of intermediate Sectoral supply response. Empirical analysis reveals that the methods proposed identify more accurately the structure of Washington State, while not capturing the spread of the linkages associated with that structure.
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the identification of structure at the Sectoral level a reformulation of the hirschman rasmussen Key Sector indices
Economic Systems Research, 1992Co-Authors: Federico A. Cuello, Fayçal Mansouri, Geoffrey J. D. HewingsAbstract:The Hirschman–Rasmussen Key Sector identification indices are reformulated to correct for their sensitivity to large multiplier values in the Leontief inverse matrix. This leads to four results: (1) the generalized Key Sector indices; (2) the generalized coefficients of variation; (3) a likelihood ratio test, obtained from computing the likelihood function of each sample of Cauchy-distributed indices; and (4) the elasticity of intermediate Sectoral supply response. Empirical analysis reveals that the methods proposed identify more accurately the structure of Washington State, while not capturing the spread of the linkages associated with that structure.
Angel Cobo - One of the best experts on this subject based on the ideXlab platform.
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Fiber Optic Sensors in Structural Health Monitoring
J. Lightwave Technol., 2011Co-Authors: Jose Miguel Lopez-higuera, Antonio Quintela Incera, Luis Rodríguez Cobo, Antonio Quintela Incera, Luis Rodriguez-cobo, Angel CoboAbstract:Structural Health Monitoring (SHM) can be understood as the integration of sensing and intelligence to enable the structure loading and damage-provoking conditions to be recorded, analyzed, localized, and predicted in such a way that nondestructive testing becomes an integral part of them. In addition, SHM systems can include actuation devices to take proper reaction or correction actions. SHM sensing requirements are very well suited for the application of optical fiber sensors (OFS), in particular, to provide integrated, quasi-distributed or fully distributed technologies. In this tutorial, after a brief introduction of the basic SHM concepts, the main fiber optic techniques available for this application are reviewed, emphasizing the four most successful ones. Then, several examples of the use of OFS in real structures are also addressed, including those from the renewable energy, transportation, civil engineering and the oil and gas industry Sectors. Finally, the most relevant current technical challenges and the Key Sector markets are identified. This paper provides a tutorial introduction, a comprehensive background on this subject and also a forecast of the future of OFS for SHM. In addition, some of the challenges to be faced in the near future are addressed.
Angelos Amditis - One of the best experts on this subject based on the ideXlab platform.
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Structural health monitoring fiber optic sensors
Smart Sensors Measurement and Instrumentation, 2017Co-Authors: Konstantinos Loupos, Angelos AmditisAbstract:Structural Health Monitoring (SHM) can be understood as the integration of sensing and intelligence to enable the structure loading and damage-provoking conditions to be recorded, analyzed, localized, and predicted in such a way that nondestructive testing becomes an integral part of them. In addition, SHM systems can include actuation devices to take proper reaction or correction actions. SHM sensing requirements are very well suited for the application of optical fiber sensors (OFS), in particular, to provide integrated, quasi-distributed or fully distributed technologies. In this tutorial, after a brief introduction of the basic SHM concepts, the main fiber optic techniques available for this application are reviewed, emphasizing the four most successful ones. Then, several examples of the use of OFS in real structures are also addressed, including those from the renewable energy, transportation, civil engineering and the oil and gas industry Sectors. Finally, the most relevant current technical challenges and the Key Sector markets are identified. This paper provides a tutorial introduction, a comprehensive background on this subject and also a forecast of the future of OFS for SHM. In addition, some of the challenges to be faced in the near future are addressed.
Jan Oosterhaven - One of the best experts on this subject based on the ideXlab platform.
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Analytical and Empirical Comparison of Policy-Relevant Key Sector Measures
Spatial Economic Analysis, 2014Co-Authors: Umed Temurshoev, Jan OosterhavenAbstract:AbstractWe consider the 10 most salient Key Sector measures (linkages) and identify groups of the most similar indicators on both analytical and empirical grounds. We derive new closed-form formulas for the generalized complete and partial hypothetical extraction linkages and add the up-till-now-undefined forward counterpart of the net backward linkage. The analytical relations and some stylized facts enable us to formulate hypotheses about the direction and strength of the relationships between various linkages. To study policy-relevant measures, our empirical tests are based on income (GDP) linkages, CO2 emission linkages and employment linkages for 34 industries and 33 countries. The data show that the information on the 10 Key Sector measures may be summarized by three to at most four measures.
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Analytical and Empirical Comparison of Policy-Relevant Key Sector Measures
2013Co-Authors: Umed Temurshoev, Jan OosterhavenAbstract:We consider ten widely used Key Sector measures (linkages) and identify groups of the most similar indicators on both analytical and empirical grounds. We derive new closed-form formulas for the generalized complete and incomplete hypothetical extraction linkages and add the up till now undefined forward counterpart of the net backward linkage. The analytical relations and some stylized facts enable us to formulate hypotheses about the direction and strength of the relationships between various linkages. To study policyrelevant measures, our empirical tests are based on Sectoral income (GDP) linkages, CO2-emission linkages and employment linkages for 34 industries and 33 countries. They show that the information on the ten Key Sector measures may be summarized by four measures.
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the net multiplier is a new Key Sector indicator reply to de mesnard s comment
Social Science Research Network, 2007Co-Authors: Jan OosterhavenAbstract:Most of the comment of de Mesnard applies to a causal interpretation of the net multiplier that is applied to economically impossible exogenous (changes in) total output. This reply shows that this interpretation is incorrect and that his further argumentation is based on a time inconsistent derivation of the Leontief output multiplier. Instead the net multiplier concept is designed as a two-way dependency or net contribution indicator thatwhen applied to all Sectorsreproduces the exact size of the economy at hand. De Mesnard's iterative alternative does not satisfy this output preservation requirement and therefore it is not a proper net multiplier. Instead it equals an old gross multiplier, namely the one that indicates the indirect output effect per unit of final demand.
