Solving non-linear dynamic models (more) efficiently: application to a simple monetary policy model

Shalva Mkhatrishvili, Douglas Laxton (), Davit Tutberidze (), Tamta Sopromadze (), Saba Metreveli (), Lasha Arevadze (), Tamar Mdivnishvili () and Giorgi Tsutskiridze
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Douglas Laxton: NOVA School of Business and Economics, Saddle Point Research, The Better Policy Project
Davit Tutberidze: Macroeconomic Research Division, National Bank of Georgia
Tamta Sopromadze: Macroeconomic Research Division, National Bank of Georgia
Saba Metreveli: Macroeconomic Research Division, National Bank of Georgia
Lasha Arevadze: Macroeconomic Research Division, National Bank of Georgia
Tamar Mdivnishvili: Macroeconomic Research Division, National Bank of Georgia

No 01/2019, NBG Working Papers from National Bank of Georgia

Abstract: There has been an increased acceptance of non-linear linkages being the major driver of the most pronounced phases of business and financial cycles. However, modelling these non-linear phenomena has been a challenge, since existing solutions methods are either efficient but not able to accurately capture non-linear dynamics (e.g. linear methods), or accurate but quite resource-intensive (e.g. stacked system or stochastic Extended Path). This paper proposes two new solution approaches that try to be accurate enough and less costly. Moreover, one of those methods lets us do Kalman filtering on non-linear models in a non-linear way, which is also important for this kind of models, in general, to be more policy-relevant. Impulse responses, simulations and Kalman filtering exercises show the advantages of those new approaches when applied to a simple, but strongly non-linear, monetary policy model.

Keywords: Non-linear dynamic models; Solution methods; Monetary policy (search for similar items in EconPapers)
JEL-codes: C60 C61 C63 E17 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2019-10
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