Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM

Abstract

This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel MitISEM. The basic MitISEM algorithm provides an automatic and flexible method to approximate a non-elliptical target density using adaptive mixtures of Student-t densities, where only a kernel of the target density is required. The approximation can be used as a candidate density in Importance Sampling or Metropolis Hastings methods for Bayesian inference on model parameters and probabilities. We present and discuss four canonical econometric models using a Graphics Processing Unit and a multi-core Central Processing Unit version of the MitISEM algorithm. The results show that the parallelization of the MitISEM algorithm on Graphics Processing Units and multi-core Central Processing Units is straightforward and fast to program using MATLAB. Moreover the speed performance of the Graphics Processing Unit version is much higher than the Central Processing Unit one.

A Flexible Predictive Density Combination Model for Large Financial Data Sets in Regular and Crisis Periods

“…The choice of an accurate approximate density is crucial for the performance of any filter method and has received considerable attention in the SMC literature, see Doucet et al (2001), Liu (2001), Kunsch (2005) and Creal (2012). The M-Filter method approximates a target density using the Mixture of t by Importance Sampling Weighted Expectation-Maximization (MitISEM) algorithm proposed by Hoogerheide et al (2012) and further developed in Baştürk et al (2016). MitISEM has been shown to be able to e ectively approximate complex, non-elliptical distributions due to two features of this algorithm: the class of importance distributions (mixtures of multivariate Student's t distributions), and their joint optimisation (with the Expectation-Maximisation, EM, algorithm).…”

Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies

“…Doucet et al (2001), Liu (2001), Kunsch (2005) and Creal (2012). In the M-Filter we base our approximation of (16) on the Mixture of t by Importance Sampling weighted Expectation-Maximization (MitISEM) algorithm proposed by Hoogerheide et al (2012) and developed in Baştürk et al (2016a). It has been shown to be able to effectively approximate complex, non-elliptical distributions thanks to two main features of this algorithm: the class of importance distributions (mixtures of multivariate Student's t distributions), and their joint optimization (with the Expectation-Maximization algorithm).…”

“…In order to tackle this, we introduce a novel non-linear and non-Gaussian filter, labeled the M-Filter, which is embedded in the density combination procedure. This filter is based on the MitISEM procedure recently proposed by Hoogerheide et al (2012) and further developed in Baştürk et al (2016a) and Baştürk et al (2017).…”