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Improving Tatonnement Methods of Solving Heterogeneous Agent Models

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  • Alexander Ludwig

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    (Munich Center for the Economics of Aging (MEA))

Abstract

This paper modifies standard block Gauss-Seidel iterations used by tatonnement methods for solving large scale deterministic heterogeneous agent models. The composite method between first- and second-order tatonnement methods is shown to considerably improve convergence both in terms of speed as well as robustness relative to conventional first-order tatonnement methods. In addition, the relative advantage of the modified algorithm increases in the size and complexity of the economic model. Therefore, the algorithm allows significant reductions in computational time when solving large models. The algorithm is particularly attractive since it is easy to implement – it only augments conventional and intuitive tatonnement iterations with standard numerical methods.

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Paper provided by Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy in its series MEA discussion paper series with number 04058.

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Date of creation: 10 Sep 2004
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Handle: RePEc:mea:meawpa:04058

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  1. Axel Boersch-Supan & Alexander Ludwig & Joachim Winter, 2005. "Aging, Pension Reform, and Capital Flows: A Multi-Country Simulation Model," NBER Working Papers 11850, National Bureau of Economic Research, Inc.
  2. Judd, Kenneth L., 2002. "The parametric path method: an alternative to Fair-Taylor and L-B-J for solving perfect foresight models," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1557-1583, August.
  3. David Domeij & Martin Flodén, 2006. "Population Aging And International Capital Flows," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(3), pages 1013-1032, 08.
  4. Juillard, Michel & Laxton, Douglas & McAdam, Peter & Pioro, Hope, 1998. "An algorithm competition: First-order iterations versus Newton-based techniques," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1291-1318, August.
  5. Fair, Ray C & Taylor, John B, 1983. "Solution and Maximum Likelihood Estimation of Dynamic Nonlinear Rational Expectations Models," Econometrica, Econometric Society, vol. 51(4), pages 1169-85, July.
  6. Boucekkine, Raouf, 1995. "An alternative methodology for solving nonlinear forward-looking models," Journal of Economic Dynamics and Control, Elsevier, vol. 19(4), pages 711-734, May.
  7. David Altig, 2001. "Simulating Fundamental Tax Reform in the United States," American Economic Review, American Economic Association, vol. 91(3), pages 574-595, June.
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Cited by:
  1. Alexander Ludwig, 2005. "Moment estimation in Auerbach-Kotlikoff models: How well do they match the data?," MEA discussion paper series 05093, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
  2. Axel B�rsch-Supan & Alexander Ludwig & Joachim Winter, 2005. "Aging, Pension Reform, and Capital Flows: A Multi-Country Simulation Model," DNB Working Papers 065, Netherlands Central Bank, Research Department.
  3. Alexander Ludwig, 2005. "Aging and Economic Growth: The Role of Factor Markets and of Fundamental Pension Reforms," MEA discussion paper series 05094, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
  4. Axel Börsch-Supan & Alexander Ludwig, 2010. "Old Europe Ages: Reforms and Reform Backlashes," NBER Chapters, in: Demography and the Economy, pages 169-204 National Bureau of Economic Research, Inc.

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