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A Cluster-Grid Projection Method: Solving Problems with High Dimensionality

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  • Kenneth L. Judd
  • Lilia Maliar
  • Serguei Maliar

Abstract

We develop a projection method that can solve dynamic economic models with a large number of state variables. A distinctive feature of our method is that it operates on the ergodic set realized in equilibrium: we simulate a model, distinguish clusters on simulated series and use the clusters’ centers as a grid for projections. Making the grid endogenous to the model allows us to avoid costs associated with finding a solution in areas of state space that are never visited in equilibrium. On a standard desktop computer, we calculate linear and quadratic solutions to a multi-country growth model with up to 400 and 80 state variables, respectively. Our solutions are global, and their accuracy does not rapidly decline away from steady state.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 15965.

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Date of creation: May 2010
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Handle: RePEc:nbr:nberwo:15965

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  1. Juillard, Michel & Villemot, Sébastien, 2011. "Multi-country real business cycle models: Accuracy tests and test bench," Journal of Economic Dynamics and Control, Elsevier, vol. 35(2), pages 178-185, February.
  2. Lawrence J. Christiano & Jonas D.M. Fisher, 1997. "Algorithms for solving dynamic models with occasionally binding constraints," Working Paper Series, Macroeconomic Issues WP-97-15, Federal Reserve Bank of Chicago.
  3. Aruoba, S. Boragan & Fernandez-Villaverde, Jesus & Rubio-Ramirez, Juan F., 2006. "Comparing solution methods for dynamic equilibrium economies," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2477-2508, December.
  4. Taylor, John B & Uhlig, Harald, 1990. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 1-17, January.
  5. Serguei Maliar & Lilia Maliar & Kenneth Judd, 2010. "Numerically Stable Stochastic Simulation Approaches for Solving Dynamic Economic Models," 2010 Meeting Papers 280, Society for Economic Dynamics.
  6. Jess Gaspar & Kenneth L. Judd, 1997. "Solving Large Scale Rational Expectations Models," NBER Technical Working Papers 0207, National Bureau of Economic Research, Inc.
  7. Pakes, Ariel & McGuire, Paul, 2001. "Stochastic Algorithms, Symmetric Markov Perfect Equilibrium, and the 'Curse' of Dimensionality," Econometrica, Econometric Society, vol. 69(5), pages 1261-81, September.
  8. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
  9. Krueger, Dirk & Kubler, Felix, 2004. "Computing equilibrium in OLG models with stochastic production," Journal of Economic Dynamics and Control, Elsevier, vol. 28(7), pages 1411-1436, April.
  10. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
  11. Maliar, Serguei & Maliar, Lilia & Judd, Kenneth, 2011. "Solving the multi-country real business cycle model using ergodic set methods," Journal of Economic Dynamics and Control, Elsevier, vol. 35(2), pages 207-228, February.
  12. Collard, Fabrice & Juillard, Michel, 2001. "Accuracy of stochastic perturbation methods: The case of asset pricing models," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 979-999, June.
  13. den Haan, Wouter J & Marcet, Albert, 1990. "Solving the Stochastic Growth Model by Parameterizing Expectations," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 31-34, January.
  14. Kollmann, Robert & Maliar, Serguei & Malin, Benjamin A. & Pichler, Paul, 2011. "Comparison of solutions to the multi-country Real Business Cycle model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(2), pages 186-202, February.
  15. Den Haan, Wouter J, 1996. "Heterogeneity, Aggregate Uncertainty, and the Short-Term Interest Rate," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 399-411, October.
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Cited by:
  1. Senbeta, Sisay, 2011. "How applicable are the new keynesian DSGE models to a typical low-income economy?," MPRA Paper 30931, University Library of Munich, Germany.
  2. Kenneth L. Judd & Lilia Maliar & Serguei Maliar, 2011. "How to Solve Dynamic Stochastic Models Computing Expectations Just Once," NBER Working Papers 17418, National Bureau of Economic Research, Inc.
  3. Marco Del Negro & Marc P. Giannoni & Frank Schorfheide, 2013. "Inflation in the Great Recession and New Keynesian models," Staff Reports 618, Federal Reserve Bank of New York.
  4. Kenneth Judd & Lilia Maliar & Rafael Valero & Serguei Maliar, 2013. "Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain," Working Papers. Serie AD 2013-06, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  5. Mele, Antonio, 2011. "Repeated moral hazard and recursive Lagrangeans," MPRA Paper 30310, University Library of Munich, Germany.
  6. Grey Gordon, 2011. "Computing Dynamic Heterogeneous-Agent Economies: Tracking the Distribution," PIER Working Paper Archive 11-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  7. S. Borağan Aruoba & Pablo Cuba-Borda & Frank Schorfheide, 2013. "Macroeconomic Dynamics Near the ZLB: A Tale of Two Countries," NBER Working Papers 19248, National Bureau of Economic Research, Inc.
  8. Alexander W. Richter & Nathaniel A. Throckmorton, 2014. "The Zero Lower Bound: Frequency, Duration, and Numerical Convergence," Auburn Economics Working Paper Series auwp2014-09, Department of Economics, Auburn University.
  9. S. Boragan Aruoba & Frank Schorfheide, 2013. "Macroeconomic dynamics near the ZLB: a tale of two equilibria," Working Papers 13-29, Federal Reserve Bank of Philadelphia.
  10. Thomas Mertens, 2012. "Solving General Incomplete Market Models with Substantial Heterogeneity," 2012 Meeting Papers 1173, Society for Economic Dynamics.

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