Chan, Joshua and Strachan, Rodney (2012): Estimation in NonLinear NonGaussian State Space Models with PrecisionBased Methods.

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Abstract
In recent years state space models, particularly the linear Gaussian version, have become the standard framework for analyzing macroeconomic and financial data. However, many theoretically motivated models imply nonlinear or nonGaussian 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 precisionbased algorithms, we propose a general approach to estimating highdimensional nonlinear nonGaussian 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 acceptreject MetropolisHastings (ARMH) algorithm, and another adaptive collapsed sampler inspired by the crossentropy method. To illustrate the proposed approach, we investigate the effect of the zero lower bound of interest rate on monetary transmission mechanism.
Item Type:  MPRA Paper 

Original Title:  Estimation in NonLinear NonGaussian State Space Models with PrecisionBased Methods 
Language:  English 
Keywords:  integrated likelihood; acceptreject MetropolisHastings; crossentropy; liquidity trap; zero lower bound 
Subjects:  C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models; Multiple Variables > C32  TimeSeries Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models E  Macroeconomics and Monetary Economics > E5  Monetary Policy, Central Banking, and the Supply of Money and Credit > E52  Monetary Policy C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C11  Bayesian Analysis: General 
Item ID:  39360 
Depositing User:  Joshua Chan 
Date Deposited:  10. Jun 2012 12:49 
Last Modified:  13. Feb 2013 10:43 
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URI:  http://mpra.ub.unimuenchen.de/id/eprint/39360 