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Solving the multi-country real business cycle model using a Smolyak-collocation method

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  • Malin, Benjamin A.
  • Krueger, Dirk
  • Kubler, Felix

Abstract

We describe a sparse-grid collocation method to compute recursive solutions of dynamic economies with a sizable number of state variables. We show how powerful this method can be in applications by computing the non-linear recursive solution of an international real business cycle model with a substantial number of countries, complete insurance markets and frictions that impede frictionless international capital flows. In this economy, the aggregate state vector includes the distribution of world capital across different countries as well as the exogenous country-specific technology shocks. We use the algorithm to efficiently solve models with up to 10 countries (i.e., up to 20 continuous-valued state variables).

Suggested Citation

  • Malin, Benjamin A. & Krueger, Dirk & Kubler, Felix, 2011. "Solving the multi-country real business cycle model using a Smolyak-collocation method," Journal of Economic Dynamics and Control, Elsevier, vol. 35(2), pages 229-239, February.
  • Handle: RePEc:eee:dyncon:v:35:y:2011:i:2:p:229-239
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Backus, David K & Kehoe, Patrick J & Kydland, Finn E, 1992. "International Real Business Cycles," Journal of Political Economy, University of Chicago Press, vol. 100(4), pages 745-775, August.
    4. 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.
    5. Kollmann, Robert & Kim, Jinill & Kim, Sunghyun H., 2011. "Solving the multi-country Real Business Cycle model using a perturbation method," Journal of Economic Dynamics and Control, Elsevier, vol. 35(2), pages 203-206, February.
    6. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, January.
    7. 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.
    8. Wouter J. Den Haan & Kenneth L. Judd & Michel Juillard, 2010. "Computational suite of models with heterogeneous agents: Multi-country real business cycle models," Post-Print hal-00765828, HAL.
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    Cited by:

    1. Duong Ngotran, 2016. "The E-Monetary Theory," 2016 Papers png175, Job Market Papers.
    2. Yasuo Hirose & Takeki Sunakawa, 2016. "Parameter Bias in an Estimated DSGE Model," Working Papers halshs-01661908, HAL.
    3. Mele, Antonio, 2014. "Repeated moral hazard and recursive Lagrangeans," Journal of Economic Dynamics and Control, Elsevier, vol. 42(C), pages 69-85.
    4. Arellano, Cristina & Maliar, Lilia & Maliar, Serguei & Tsyrennikov, Viktor, 2016. "Envelope condition method with an application to default risk models," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 436-459.
    5. Dennis, Richard & Kirsanova, Tatiana, 2016. "Computing Markov-Perfect Optimal Policies In Business-Cycle Models," Macroeconomic Dynamics, Cambridge University Press, vol. 20(07), pages 1850-1872, October.
    6. Jesús Fernández-Villaverde & Oren Levintal, 2016. "Solution Methods for Models with Rare Disasters," NBER Working Papers 21997, National Bureau of Economic Research, Inc.
    7. Jasmina Hasanhodzic & Laurence J. Kotlikoff, 2013. "Generational Risk–Is It a Big Deal?: Simulating an 80-Period OLG Model with Aggregate Shocks," BYU Macroeconomics and Computational Laboratory Working Paper Series 2013-01, Brigham Young University, Department of Economics, BYU Macroeconomics and Computational Laboratory.
    8. Julien Albertini & Hong Lan, 2016. "The importance of time-varying parameters in new Keynesian models with zero lower bound," SFB 649 Discussion Papers SFB649DP2016-013, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    9. Zhou, Wei, 2015. "Three essays on modeling biofuel feedstock supply," ISU General Staff Papers 201501010800005728, Iowa State University, Department of Economics.
    10. Fernández-Villaverde, Jesús & Gordon, Grey & Guerrón-Quintana, Pablo & Rubio-Ramírez, Juan F., 2015. "Nonlinear adventures at the zero lower bound," Journal of Economic Dynamics and Control, Elsevier, vol. 57(C), pages 182-204.
    11. Tom Holden & Michael Paetz, 2012. "Efficient Simulation of DSGE Models with Inequality Constraints," Quantitative Macroeconomics Working Papers 21207b, Hamburg University, Department of Economics.
    12. repec:eee:macchp:v2-527 is not listed on IDEAS
    13. Julien Albertini & Arthur Poirier, 2015. "Unemployment Benefit Extension at the Zero Lower Bound," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 18(4), pages 733-751, October.
    14. Judd, Kenneth L. & Maliar, Lilia & Maliar, Serguei & Valero, Rafael, 2014. "Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain," Journal of Economic Dynamics and Control, Elsevier, vol. 44(C), pages 92-123.
    15. Yasuo Hirose & Takeki Sunakawa, 2015. "Parameter bias in an estimated DSGE model: does nonlinearity matter?," CAMA Working Papers 2015-46, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    16. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, Elsevier.
    17. Yongyang Cai & Kenneth Judd & Jevgenijs Steinbuks, 2017. "A nonlinear certainty equivalent approximation method for dynamic stochastic problems," Quantitative Economics, Econometric Society, vol. 8(1), pages 117-147, March.
    18. Yongyang Cai & Kenneth Judd, 2015. "Dynamic programming with Hermite approximation," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 81(3), pages 245-267, June.
    19. Zhou, Wei & Babcock, Bruce A., 2014. "Endogenous Price in a Dynamic Model for Agricultural Supply Analysis," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170584, Agricultural and Applied Economics Association.
    20. Pontus Rendahl, 2015. "Inequality Constraints and Euler Equation‐based Solution Methods," Economic Journal, Royal Economic Society, vol. 125(585), pages 1110-1135, June.
    21. Dennis, Richard & Kirsanova, Tatiana, 2014. "Computing Markov-Perfect Optimal Policies in Business-Cycle Models," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN 2015-64, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    22. Julien Albertini & Arthur Poirier, 2014. "Unemployment benefits extensions at the zero lower bound on nominal interest rate," SFB 649 Discussion Papers SFB649DP2014-019, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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