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A not sign-preserving iteration algorithm for the “improved normalized squared differences” matrix adjustment model. (English) Zbl 07700322

Summary: Estimating the elements of a matrix, when only the margins (row and column sums) are known, but a supposedly similar ‘reference matrix’ is available, is a standard problem in many disciplines. After discussing the main types, issues and applications of these two-directional matrix adjustment problems the paper concentrates on the case of negative matrix elements and models with quadratic objective functions. The solution of the Improved Normalized Squared Differences (INSD) model is proved to be the same as the result of that iteration algorithm which is presented in the paper. It is also argued that if the sign-preservation requirement is dropped then the iteration procedure suggested by W. Huang et al. [Econ. Syst. Res. 20, No. 1, 111–123 (2008; doi:10.1080/09535310801892082)] boils down to the same algorithm. Using the numerical example of the earlier literature it is also demonstrated that even in this not sign-preserving case, which even requires sign-flips for some elements, the INSD-model produces good fit in mathematical terms.

MSC:

90Bxx Operations research and management science

References:

[1] Bacharach, M., Estimating non-negative matrices from marginal data, Int Econ Rev, 6, 294-310 (1965) · Zbl 0136.14301 · doi:10.2307/2525582
[2] Bacharach, M., Biproportional matrices and input-output change (1970), Cambridge, UK: Cambridge University Press, Cambridge, UK · Zbl 0195.49705
[3] Byron RP (1978) The estimation of large social account matrices. J Roy Stat Soc Ser A 141(Part 3):359-367. doi:10.2307/2344807 · Zbl 0444.62120
[4] de Mesnard, L., Biproportional method for analysing interindustry dynamics: the case of France, Econ Syst Res, 2, 3, 271-293 (1990) · doi:10.1080/09535319000000019
[5] de Mesnard L (2011) Six matrix adjustment problems solved by some fundamental theorems on biproportion. Working paper, University of Burgundy and CNRS. doi:10.2139/ssrn.1692512
[6] Deming, WE; Stephan, FF, On a least-squares adjustment of a sampled frequency table when the expected marginal totals are known, Ann Math Stat, 11, 4, 427-444 (1940) · Zbl 0024.05502 · doi:10.1214/aoms/1177731829
[7] Friedlander, D., A technique for estimating contingency tables, given marginal totals and some supplemental data, J R Stat Soc Ser A, 124, 3, 412-420 (1961) · doi:10.2307/2343244
[8] Günlük-Şenesen G, Bates JM (1988) Some experiments with methods of adjusting unbalanced data matrices. J Roy Stat Soc Ser A 151(3):473-490. doi:10.2307/2982995
[9] Henry EW (1973) Relative efficiency of RAS versus least squares methods of updating input-output structures, as adjudged by application to Irish data. Econ Social Rev 5(1):7-29. v5n11973_2.pdf(tcd.ie)
[10] Henry EW (1974) Relative efficiency of RAS versus least squares methods of updating input-output structures: an addendum. Econ Social Rev 5(2):175-179. v5n21974_2.pdf(tcd.ie)
[11] Holý V, Šafr (2020) Disaggregating input-output tables by the Mmultidimensional RAS method. Working paper, University of Economics, Prague, arXiv:1704.07814 [stat. AP]
[12] Huang, W.; Kobayashi, S.; Tanji, H., Updating an input-output matrix with sign-preservation: some improved objective functions and their solutions, Econ Syst Res, 20, 1, 111-123 (2008) · doi:10.1080/09535310801892082
[13] Jackson, RW; Murray, AT, Alternative input-output matrix updating formulations, Econ Syst Res, 16, 2, 135-148 (2004) · doi:10.1080/0953531042000219268
[14] Junius, T.; Oosterhaven, J., The solution of updating or regionalizing a matrix with both positive and negative entries, Econ Syst Res, 15, 1, 87-96 (2003) · doi:10.1080/0953531032000056954
[15] Kullback, S.; Leibler, RA, On information and sufficiency, Ann Math Stat, 22, 1, 79-96 (1951) · Zbl 0042.38403 · doi:10.1214/aoms/1177729694
[16] Lahr, M.; de Mesnard, L., Biproportional techniques in input-output analysis: table updating and structural analysis, Econ Syst Res, 16, 2, 115-134 (2004) · doi:10.1080/0953531042000219259
[17] Lecomber R (1971) A critique of methods of adjusting, updating and projecting matrices, together with some new proposals. Discussion Paper in Economics, No. 40, Department of Economics, University of Bristol, August 1971.
[18] Lecomber JRC (1975) A critique of methods of adjusting, updating and projecting matrices. In: Allen RIG, Gossling WF (eds) Estimating and projecting input-output coefficients. Input-Output Publishing Company, London, UK, pp 1-25
[19] Lemelin, A., A GRAS variant solving for minimum information loss, Econ Syst Res, 21, 4, 399-408 (2009) · doi:10.1080/09535311003589310
[20] Lenzen, M.; Moran, D.; Geschke, A.; Keiichiro, K., A non-sign preserving GRAS-variant, Econ Syst Res, 26, 2, 197-208 (2014) · doi:10.1080/09535314.2014.897933
[21] Lenzen, M.; Wood, R.; Gallego, B., Some comments on the GRAS method, Econ Syst Res, 19, 4, 461-465 (2007) · doi:10.1080/09535310701698613
[22] MacGill, SM, Theoretical properties of biproportional matrix adjustments, Environ Plan A, 9, 687-701 (1977) · doi:10.1068/a090687
[23] McDougall RA (1999) Entropy theory and RAS are friends. GTAP working papers 300, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University (see at http://ideas.repec.org/p/gta/workpp/300.html)
[24] Möhr, M.; Crown, WH; Polenske, KR, A linear programming approach to solving infeasible RAS problems, J Reg Sci, 27, 4, 587-603 (1987) · doi:10.1111/j.1467-9787.1987.tb01183.x
[25] Oosterhaven, J., GRAS versus minimizing absolute and squared differences: a comment, Econ Syst Res, 17, 3, 327-331 (2005) · doi:10.1080/09535310500221864
[26] Pearson K (1904) On the theory of contingency and its relation to association and normal correlation. part of the Drapers’ Company research memoirs, biometric series I.—mathematical contribution to the theory of evolution. London, Dulau and Co. https://archive.org/details/cu31924003064833/page/n3/mode/2up
[27] Révész T (2001) Költségvetési és környezetpolitikák elemzése általános egyensúlyi modellekkel (Fiscal and Environmental Policy Analyses Using General Equilibrium Models). Budapest University of Economic Sciences, Ph.D. Thesis (written in Hungarian with English summary), 2001 March (http://phd.lib.uni-corvinus.hu/1085/)
[28] Smith, JH, Estimation of linear functions of cell proportions, Ann Math Stat, 18, 2, 231-254 (1947) · Zbl 0034.07501 · doi:10.1214/aoms/1177730440
[29] Stone R (1977) The development of economic data systems. In: Pyatt G, Stone R, Roe AR (eds) Social accounting for development planning. Cambridge University Press, New York (Foreword)
[30] Temurshoev, U.; Miller, RE; Bouwmeester, MC, A note on the GRAS method, Econ Syst Res, 25, 3, 361-367 (2013) · doi:10.1080/09535314.2012.746645
[31] Temurshoev, U.; Webb, C.; Yamano, N., Projection of supply and use tables: methods and their empirical assessment, Econ Syst Res, 23, 1, 91-123 (2011) · doi:10.1080/09535314.2010.534978
[32] Theil, H., Principles of econometrics (1971), New York: Wiley, New York · Zbl 0221.62002
[33] Valderas-Jaramillo, JM; Rueda-Cantuche, JM, The multidimensional nD-GRAS method: applications for the projection of multiregional input-output frameworks and valuation matrices, Pap Reg Sci, 100, 6, 1599-1624 (2021) · doi:10.1111/pirs.12625
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