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Dealing with ZLB in DSGE models An application to the Japanese economy

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  • ADJEMIAN Stéphane
  • JUILLARD Michel

Abstract

In this paper we propose an estimation strategy for DSGE models with occasionaly binding constraints, such as models with a zero lower bound for the nominal interest rate (ZLB). The usual likelihood approach is based on a first order approximation of the model around its deterministic steady state. This is not possible when we deal with a model with occasionally binding constraints, because the model is non differentiable everywhere and because, putting this first problem aside, the agents in the approximated model do not anticipate that the economy may hit the zero lower bound in the future. A medium scaled DSGE model with ZLB is estimated by the Simulated Method of Moments, using the Extended Path approach to simulate artificial time series for the observed variables. The Extended Path approach to simulation of stochastic forward{looking models, takes into account the full nonlinearities of the deterministic part of the model, but ignores the Jensen inequality. The extended path method is well suited for models including the zero lower bound because (contrary to the perturbation method) it does not rely on a strong smoothness assumption and so can handle problems with non differentiabilities. This approach proves to be feasible in practice.

Suggested Citation

  • ADJEMIAN Stéphane & JUILLARD Michel, 2010. "Dealing with ZLB in DSGE models An application to the Japanese economy," ESRI Discussion paper series 258, Economic and Social Research Institute (ESRI).
  • Handle: RePEc:esj:esridp:258
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    File URL: http://www.esri.go.jp/jp/archive/e_dis/e_dis258/e_dis258.pdf
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    References listed on IDEAS

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    1. Gagnon, Joseph E, 1990. "Solving the Stochastic Growth Model by Deterministic Extended Path," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 35-36, January.
    2. Dixit, Avinash K & Stiglitz, Joseph E, 1977. "Monopolistic Competition and Optimum Product Diversity," American Economic Review, American Economic Association, vol. 67(3), pages 297-308, June.
    3. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    4. ADJEMIAN Stéphane & JUILLARD Michel, 2009. "Dealing with trends in DSGE models. An application to the Japanese economy," ESRI Discussion paper series 224, Economic and Social Research Institute (ESRI).
    5. David R.F. Love, 2009. "Accuracy of Deterministic Extended-Path Solution Methods for Dynamic Stochastic Optimization Problems in Macroeconomics," Working Papers 0907, Brock University, Department of Economics.
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    Cited by:

    1. Braun, R. Anton & Körber, Lena Mareen, 2011. "New Keynesian dynamics in a low interest rate environment," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 2213-2227.
    2. Jean Barthélemy & Magali Marx, 2012. "Solving Rational Expectations Models," Sciences Po publications info:hdl:2441/3ug0u3qte39, Sciences Po.
    3. Andrew Binning & Junior Maih, 2017. "Modelling Occasionally Binding Constraints Using Regime-Switching," Working Papers No 9/2017, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    4. Martin M. Andreasen & Anders Kronborg, 2803. "The Extended Perturbation Method: New Insights on the New Keynesian Model," CREATES Research Papers 2017-14, Department of Economics and Business Economics, Aarhus University.

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