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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.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 8 (1990)
Issue (Month): 1 (January)
Pages: 45-47

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Handle: RePEc:bes:jnlbes:v:8:y:1990:i:1:p:45-47

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Cited by:
  1. John B. Taylor & Harald Uhlig, 1990. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," NBER Working Papers 3117, National Bureau of Economic Research, Inc.
  2. de la Croix, David & Docquier, Frédéric, 2003. "Diverging Patterns of Education Premium and School Attendance in France and the US: A Walrasian View," IZA Discussion Papers 846, Institute for the Study of Labor (IZA).
  3. Thomas J. Sargent & Francois R. Velde, 1997. "The big problem of small change," Working Paper Series, Macroeconomic Issues WP-97-08, Federal Reserve Bank of Chicago.
  4. David, DE LA CROIX & Frédéric DOCQUIER & Philippe, LIEGEOIS, 2007. "Income Growth in the 21st century : forecasts with an overlapping generations model," Discussion Papers (ECON - Département des Sciences Economiques) 2007029, Université catholique de Louvain, Département des Sciences Economiques.
  5. Hanno Lustig & Stijn Van Nieuwerburgh, 2002. "Housing Collateral, Consumption Insurance and Risk Premia," Macroeconomics 0211008, EconWPA.
  6. Sibel Sirakaya & Stephen Turnovsky & N.M. Alemdar, 2005. "Feedback Approximation of the Stochastic Growth Model by Genetic Neural Networks," Working Papers UWEC-2006-03-P, University of Washington, Department of Economics, revised Jul 2005.
  7. Xavier Chojnicki & Lionel Ragot, 2014. "Impacts of Immigration on Aging Welfare-State An Applied General Equilibrium Model for France," EconomiX Working Papers 2014-33, University of Paris West - Nanterre la Défense, EconomiX.
  8. Alfonso Novales & Javier J. Pérez, 2002. "Is it Worth Refining Linear Approximations to Non-Linear Rational Expectations Models?," Economic Working Papers at Centro de Estudios Andaluces E2002/15, Centro de Estudios Andaluces.
  9. 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.
  10. José Mª Martín Moreno & Jesús Ruiz, . "Bienes comerciables y no comerciables en la economía española: Un enfoque de ciclo real," Studies on the Spanish Economy 206, FEDEA.

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