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Solving the Stochastic Growth Model by Backsolving with a Particular Nonlinear Form for the Decision Rule

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  • Sims, Christopher A

Abstract

Backsolving is a class of methods that generate simulated values for exogenous forcing processes in a stochastic equilibrium model from specified assumed distributions for Euler-equation disturbances. It can be thought of as a way to force the approximation error generated by inexact choice of decision rule or boundary condition into distortions of the distribution of the exogenous shocks in the simulations rather than into violations of the Euler equations as with standard approaches. Here it is applied to a one-sector neoclassical growth model with decision rule generated from a linear-quadratic approximation.

Suggested Citation

  • Sims, Christopher A, 1990. "Solving the Stochastic Growth Model by Backsolving with a Particular Nonlinear Form for the Decision Rule," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 45-47, January.
  • Handle: RePEc:bes:jnlbes:v:8:y:1990:i:1:p:45-47
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    Cited by:

    1. David de la Croix & Frédéric Docquier, 2007. "School Attendance and Skill Premiums in France and the US: A General Equilibrium Approach," Fiscal Studies, Institute for Fiscal Studies, vol. 28(4), pages 383-416, December.
    2. Cogley, Timothy, 2001. "Estimating and testing rational expectations models when the trend specification is uncertain," Journal of Economic Dynamics and Control, Elsevier, vol. 25(10), pages 1485-1525, October.
    3. JosÈ Mô MartÌn Moreno & Jes˙s Ruiz, "undated". "Bienes comerciables y no comerciables en la economÌa espanola: Un enfoque de ciclo real," Studies on the Spanish Economy 206, FEDEA.
    4. de la Croix, David & Docquier, Frederic & Liegeois, Philippe, 2007. "Income growth in the 21st century: Forecasts with an overlapping generations model," International Journal of Forecasting, Elsevier, vol. 23(4), pages 621-635.
    5. 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.
    6. Alfonso Novales & Javier J. PÈrez, 2004. "Is It Worth Refining Linear Approximations to Non-Linear Rational Expectations Models?," Computational Economics, Springer;Society for Computational Economics, vol. 23(4), pages 343-377, June.
    7. Xavier Chojnicki & Lionel Ragot, 2011. "Impacts of Immigration on Aging Welfare-State An Applied General Equilibrium Model for France," Working Papers 2011-13, CEPII research center.
    8. S. Sirakaya & Stephen Turnovsky & M. Alemdar, 2006. "Feedback Approximation of the Stochastic Growth Model by Genetic Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 185-206, May.
    9. Sargent, Thomas J & Velde, Francois R, 1999. "The Big Problem of Small Change," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 31(2), pages 137-161, May.
    10. Hanno Lustig & Stijn Van Nieuwerburgh, 2002. "Housing Collateral, Consumption Insurance and Risk Premia," Macroeconomics 0211008, University Library of Munich, Germany.
    11. repec:wly:emetrp:v:85:y:2017:i::p:991-1012 is not listed on IDEAS
    12. Kenneth L. Judd & Lilia Maliar & Serguei Maliar, 2014. "Lower Bounds on Approximation Errors: Testing the Hypothesis That a Numerical Solution Is Accurate?," BYU Macroeconomics and Computational Laboratory Working Paper Series 2014-06, Brigham Young University, Department of Economics, BYU Macroeconomics and Computational Laboratory.

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