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Computation of LQ Approximations to Optimal Policy Problems in Different Information Settings under Zero Lower Bound Constraints

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  • Levine, Paul
  • Pearlman, Joseph

Abstract

This paper describes a series of algorithms that are used to compute optimal policy under full and imperfect information. Firstly we describe how to obtain linear quadratic (LQ) approximations to a nonlinear optimal policy problem. We develop novel algorithms that are required as a result of having agents with forward-looking expectations, that go beyond the scope of those that are used when all equations are backward-looking; these are utilised to generate impulse response functions and second moments for the case of imperfect information. We describe algorithms for reducing a system to minimal form that are based on conventional approaches, and that are necessary to ensure that a solution for fully optimal policy can be computed. Finally we outline a computational algorithm that is used to generate solutions when there is a zero lower bound constraint for the nominal interest rate.

Suggested Citation

  • Levine, Paul & Pearlman, Joseph, 2011. "Computation of LQ Approximations to Optimal Policy Problems in Different Information Settings under Zero Lower Bound Constraints," Dynare Working Papers 10, CEPREMAP.
    • Handle: RePEc:cpm:dynare:010
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    5. Rajendra Narayan Paramanik & Bandi Kamaiah, 2014. "A Structural Vector Autoregression Model for Monetary Policy Analysis in India," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 8(4), pages 401-429, November.

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