The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Updating and Estimating a Social Accounting Matrix Using Cross Entropy Methods
Open accessAbstract
The problem in estimating a social accounting matrix (SAM) for a recent year is to find an efficient and cost-effective way to incorporate and reconcile information from a variety of sources, including data from prior years. Based on information theory, the paper presents a flexible 'cross entropy' (CE) approach to estimating a consistent SAM starting from inconsistent data estimated with error, a common experience in many countries. The method represents an efficient information processing rule-using only and all information available. It allows incorporating errors in variables, inequality constraints, and prior knowledge about any part of the SAM. An example is presented, applying the CE approach to data from Mozambique, using a Monte Carlo approach to compare the CE approach to the standard RAS method and to evaluate the gains in precision from utilizing additional information.
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A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
Brook, A., Kendrick, D., & Meeraus, A. (1988) · ACM SIGNUM Newsletter · doi:10.1145/58859.58863 · Cited by 2,973
Introduction to the Theory and Practice of Econometrics
Ziegel, E. (1989) · Technometrics · doi:10.1080/00401706.1989.10488621 · Cited by 2,239
Economics and Information Theory
Sastry, M. V. R., & Theil, H. (1969) · Econometrica · doi:10.2307/1912805 · Cited by 2,117
Economics and Information Theory
Silver, W. E., & Theil, H. (1967) · OR · doi:10.2307/3006993 · Cited by 1,233
Introduction to the Theory and Practice of Econometrics.
Judge, G. G., Hill, C., Griffiths, W. E., Lutkepohl, H., & Lee, T.-C. (1988) · Journal of the American Statistical Association · doi:10.2307/2290184 · Cited by 1,104
Entropy optimization principles with applications
Kapur, J. N., & Kesavan, H. K. (1992) · Medical Entomology and Zoology · Cited by 858
Maximum entropy econometrics: robust estimation with limited data
Golan, A., Judge, G. G., & Miller, D. J. (1996) · Cited by 829
The Annals of Mathematical Statistics
King, W. I. (1930) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177733256 · Cited by 827
Cited by
Stadler, K., Wood, R., Bulavskaya, T., Södersten, C., Simas, M., Schmidt, S., Usubiaga, A., Acosta‐Fernández, J., Kuenen, J., Bruckner, M., et al. (2018) · Journal of Industrial Ecology · doi:10.1111/jiec.12715 · Cited by 1,024
Fish to 2030: The Role and Opportunity for Aquaculture
Kobayashi, M., Msangi, S., Batka, M., Vannuccini, S., Dey, M. M., & Anderson, J. L. (2015) · Aquaculture Economics & Management · doi:10.1080/13657305.2015.994240 · Cited by 416
Wang, T., Lu, C., Yang, M., Hong, F., & Liu, C. (2020) · PeerJ Computer Science · doi:10.7717/peerj-cs.324 · Cited by 37
Metadata
- Authors
- Robinson, S., Cattaneo, A., & El-Said, M.
- Venue
- Economic Systems Research
- Publisher
- Informa UK Limited
- Published
- 2001-03
- Volume
- 13
- Issue
- 1
- Pages
- 47-64
- ISSN
- 0953-5314 (ISSN-L)
- 1469-5758
- OpenAlex
- W2140146054
- Cited by
- 455
- References
- 17
- FWCI
- 25.28
- Type
- journal article
- Language
- en
- Open access
- green
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