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Forecasting with estimated dynamic stochastic general equilibrium models: The role of nonlinearities

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Abstract

In this paper we study the e®ects of nonlinearities on the forecast- ing performance of a dynamic stochastic general equilibrium model. We compute ¯rst and second-order approximations to a New Keyne- sian monetary model, and use arti¯cial data to estimate the model's structural parameters based on its linear and quadratic solution. We and that, although our model in not far from being linear, the fore- casting performance improves by capturing the second-order terms in the solution. Our ¯ndings suggest that accounting for nonlinearities will improve the predictive abilities of DSGE models in many appli- cations.

Suggested Citation

  • Paul Pichler, 2007. "Forecasting with estimated dynamic stochastic general equilibrium models: The role of nonlinearities," Vienna Economics Papers 0702, University of Vienna, Department of Economics.
  • Handle: RePEc:vie:viennp:0702
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    File URL: http://homepage.univie.ac.at/Papers.Econ/RePEc/vie/viennp/vie0702.pdf
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    1. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez, 2007. "Estimating Macroeconomic Models: A Likelihood Approach," Review of Economic Studies, Oxford University Press, vol. 74(4), pages 1059-1087.
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    Cited by:

    1. Ahmed, Shahzad & Ahmed, Waqas & Khan, Sajawal & Pasha, Farooq & Rehman, Muhammad, 2012. "Pakistan Economy DSGE Model with Informality," MPRA Paper 53135, University Library of Munich, Germany.

    More about this item

    JEL classification:

    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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