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

Paul Levine () and Joseph Pearlman

No 10, Dynare Working Papers from CEPREMAP

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.

Pages: 26 pages
Date: 2011-11
New Economics Papers: this item is included in nep-cba and nep-cmp
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:cpm:dynare:010

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