Estimation in Non-Linear Non-Gaussian State Space Models with Precision-Based Methods

Joshua Chan and Rodney Strachan

MPRA Paper from University Library of Munich, Germany

Abstract: In recent years state space models, particularly the linear Gaussian version, have become the standard framework for analyzing macro-economic and financial data. However, many theoretically motivated models imply non-linear or non-Gaussian specifications or both. Existing methods for estimating such models are computationally intensive, and often cannot be applied to models with more than a few states. Building upon recent developments in precision-based algorithms, we propose a general approach to estimating high-dimensional non-linear non-Gaussian state space models. The baseline algorithm approximates the conditional distribution of the states by a multivariate Gaussian or t density, which is then used for posterior simulation. We further develop this baseline algorithm to construct more sophisticated samplers with attractive properties: one based on the accept-reject Metropolis-Hastings (ARMH) algorithm, and another adaptive collapsed sampler inspired by the cross-entropy method. To illustrate the proposed approach, we investigate the effect of the zero lower bound of interest rate on monetary transmission mechanism.

Keywords: integrated likelihood; accept-reject Metropolis-Hastings; cross-entropy; liquidity trap; zero lower bound (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 E52 (search for similar items in EconPapers)
Date: 2012
New Economics Papers: this item is included in nep-ets
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Citations: View citations in EconPapers (15)

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Working Paper: Estimation in Non-Linear Non-Gaussian State Space Models with Precision-Based Methods (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:39360

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