http://www.cnr.it/ontology/cnr/individuo/prodotto/ID60229
On the estimation of the consumption matrix from inexact data in the Leontief model (Articolo in rivista)
- Type
- Label
- On the estimation of the consumption matrix from inexact data in the Leontief model (Articolo in rivista) (literal)
- Anno
- 2007-01-01T00:00:00+01:00 (literal)
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- Stefania Ragni; Moody T. Chu; Fasma Diele; Carmela Marangi (literal)
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- http://jnaiam.org/index.php?/archives/48-On-the-Estimation-of-the-Consumption-Matrix-from-Inexact-Data-in-the-Leontief-Model.html (literal)
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- Mathematical Reviews on the web (MathSciNet) (literal)
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- Stefania Ragni, Facoltà di Economia, Università di Bari, Via Camillo Rosalba 56, 70100 Bari, Italy;
Moody T. Chu, Department of Mathematics, North Carolina State University, Raleigh, NC 27695-8205;
Fasma Diele, Istituto per le Applicazioni del Calcolo M. Picone,CNR, Via Amendola 122, 70126 Bari, Italy;
Carmela Marangi, Istituto per le Applicazioni del Calcolo M. Picone,CNR, Via Amendola 122, 70126 Bari, Italy (literal)
- Titolo
- On the estimation of the consumption matrix from inexact data in the Leontief model (literal)
- Abstract
- The Leontief model, originally developed for describing an economic system in terms of mutually interrelated and structurally conditioned simultaneous flows of commodities and services, has important applications to wide ranging disciplines. A basic model assumes the linear form x=Tx+d, where x represents the total output vector and d represents the final demand vector. The consumption matrix T plays the critical role of characterizing the entire input-output dynamics. Normally, T is determined by massive and arduous data gathering means which inadvertently bring in measurement noises. This paper considers the inverse problem of reconstructing the consumption matrix in the open Leontief model based on a sequence of inexact output vectors and demand vectors. Such a formulation might have two advantages: one is that no internal consumption measurements are required and the other is that inherent errors could be reduced by total least squares techniques. Several numerical methods are suggested. A comparison of performance and an application to real-world data are demonstrated. (literal)
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