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On the estimation of a large sparse Bayesian system: the Snaer program

Author

Listed:
  • Dmitry Danilov

    (Eurandom, Eindhoven University of Technology)

  • Jan R. Magnus

    (Department of Econometrics & OR, Tilburg University)

Abstract

The Snaer program calculates the posterior mean and variance of variables on some of which we have data (with precisions), on some we have prior information (with precisions), and on some prior indicator ratios (with precisions) are available. The variables must satisfy a number of exact restrictions. The system is both large and sparse. Two aspects of the statistical and computational development are a practical procedure to solve a linear integer system, and a stable linearization routine for ratios. We test our numerical method to solve large sparse linear least-squares estimation problems, and find that it performs well, even when the n*k design matrix is large (nk equivalent 2 27.5 ).

Suggested Citation

  • Dmitry Danilov & Jan R. Magnus, 2007. "On the estimation of a large sparse Bayesian system: the Snaer program," CIRJE F-Series CIRJE-F-478, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2007cf478
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    References listed on IDEAS

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    1. repec:bla:revinw:v:46:y:2000:i:3:p:329-50 is not listed on IDEAS
    2. John M. Abowd & Francis Kramarz & David N. Margolis, 1999. "High Wage Workers and High Wage Firms," Econometrica, Econometric Society, vol. 67(2), pages 251-334, March.
    3. John M. Abowd & Robert H. Creecy & Francis Kramarz, 2002. "Computing Person and Firm Effects Using Linked Longitudinal Employer-Employee Data," Longitudinal Employer-Household Dynamics Technical Papers 2002-06, Center for Economic Studies, U.S. Census Bureau.
    4. Jan R. Magnus & Jan W. van Tongeren & Aart F. de Vos, 2000. "National Accounts Estimation Using Indicator Ratios," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 46(3), pages 329-350, September.
    5. Dmitry Danilov & Jan R. Magnus, 2007. "Some equivalences in linear estimation (in Russian)," Quantile, Quantile, issue 3, pages 83-90, September.
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    1. Umed Temursho, 2017. "Uncertainty treatment in input–output analysis," Chapters, in: Thijs ten Raa (ed.), Handbook of Input–Output Analysis, chapter 12, pages 407-463, Edward Elgar Publishing.
    2. Van Tongeren, J.W. & Magnus, J.R., 2011. "Bayesian Integration of Large Scale SNA Data Frameworks with an Application to Guatemala," Other publications TiSEM 7a0ed98e-134b-4fa4-a97c-4, Tilburg University, School of Economics and Management.
    3. Van Tongeren, J.W. & Magnus, J.R., 2011. "Bayesian Integration of Large Scale SNA Data Frameworks with an Application to Guatemala," Discussion Paper 2011-022, Tilburg University, Center for Economic Research.
    4. Van Tongeren, J.W., 2011. "From national accounting to the design, compilation, and use of bayesian policy and analysis frameworks," Other publications TiSEM e2d6399b-fdf5-4147-b414-3, Tilburg University, School of Economics and Management.
    5. Dmitry Danilov & Jan R. Magnus, 2007. "Some equivalences in linear estimation (in Russian)," Quantile, Quantile, issue 3, pages 83-90, September.
    6. van Tongeren, Jan W. & Bruil, Arjan, 2022. "Projections to 2025 of the household sector within the Dutch economy," The Journal of the Economics of Ageing, Elsevier, vol. 23(C).
    7. Temel, Tugrul, 2011. "Estimation of a system of national accounts: implementation with mathematica," MPRA Paper 35446, University Library of Munich, Germany.
    8. Di Fonzo, Tommaso & Girolimetto, Daniele, 2023. "Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives," International Journal of Forecasting, Elsevier, vol. 39(1), pages 39-57.

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