Jump to ContentJump to Main Navigation

You are looking at 1-9 of 9 items

  • Keywords: particle filter x
Clear All Modify Search

View:

Particle Filters

Edward P. Herbst and Frank Schorfheide

in Bayesian Estimation of DSGE Models

Published in print:
2015
Published Online:
October 2017
ISBN:
9780691161082
eISBN:
9781400873739
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691161082.003.0008
Subject:
Economics and Finance, Econometrics

This chapter explains how the key difficulty that arises when the Bayesian estimation of DSGE models is extended from linear to nonlinear models is the evaluation of the likelihood function, and ... More


Are there discontinuities in financial prices?

Neil Shephard

in Celebrating Statistics: Papers in honour of Sir David Cox on his 80th birthday

Published in print:
2005
Published Online:
September 2007
ISBN:
9780198566540
eISBN:
9780191718038
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198566540.003.0012
Subject:
Mathematics, Probability / Statistics

This chapter explores whether there are discontinuities in financial price processes using daily data on the Japanese yen and United States dollar. It opens with a brief description of the data, ... More


Particle filtering

J. Durbin and S.J. Koopman

in Time Series Analysis by State Space Methods: Second Edition

Published in print:
2012
Published Online:
December 2013
ISBN:
9780199641178
eISBN:
9780191774881
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199641178.003.0012
Subject:
Mathematics, Probability / Statistics

This chapter discusses the filtering of non-Gaussian and nonlinear series by fixing the sample at the values previously obtained at times …, t − 2, t − 1 and choosing a fresh value at time t only. A ... More


Online Bayesian learning in dynamic models: an illustrative introduction to particle methods

Hedibert F Lopes and Carlos M Carvalho

in Bayesian Theory and Applications

Published in print:
2013
Published Online:
May 2013
ISBN:
9780199695607
eISBN:
9780191744167
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199695607.003.0011
Subject:
Mathematics, Probability / Statistics

This chapter provides a step-by-step review of Monte Carlo (MC) methods for filtering in general nonlinear and non-Gaussian dynamic models, also known as state-space models or hidden Markov models. ... More


Combining Particle Filters with MH Samplers

Edward P. Herbst and Frank Schorfheide

in Bayesian Estimation of DSGE Models

Published in print:
2015
Published Online:
October 2017
ISBN:
9780691161082
eISBN:
9781400873739
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691161082.003.0009
Subject:
Economics and Finance, Econometrics

This chapter argues that in order to conduct Bayesian inference, the approximate likelihood function has to be embedded into a posterior sampler. It begins by combining the particle filtering methods ... More


Combining Particle Filters with SMC Samplers

Edward P. Herbst and Frank Schorfheide

in Bayesian Estimation of DSGE Models

Published in print:
2015
Published Online:
October 2017
ISBN:
9780691161082
eISBN:
9781400873739
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691161082.003.0010
Subject:
Economics and Finance, Econometrics

This chapter combines the SMC algorithm with the particle filter approximation of the likelihood function to develop an SMC2 algorithm. As with the PFMH algorithm, the goal is to obtain a posterior ... More


9 Time‐varying parameters and state space models

Timo Teräsvirta, Dag Tjøstheim, and W. J. Granger

in Modelling Nonlinear Economic Time Series

Published in print:
2010
Published Online:
May 2011
ISBN:
9780199587148
eISBN:
9780191595387
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199587148.003.0009
Subject:
Economics and Finance, Econometrics

Linear state space models have become popular in time series, and there are applications to many fields. The Kalman filter is often a fundamental tool. In this chapter it is shown that there are ... More


Free Energy Sequential Monte Carlo, Application to Mixture Modelling *

Nicolas Chopin and Pierre Jacob

in Bayesian Statistics 9

Published in print:
2011
Published Online:
January 2012
ISBN:
9780199694587
eISBN:
9780191731921
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199694587.003.0003
Subject:
Mathematics, Probability / Statistics

We introduce a new class of Sequential Monte Carlo (SMC) methods, which we call free energy SMC. This class is inspired by free energy methods, which originate from physics, and where one samples ... More


Martingale unobserved component models

Neil Shephard

in Unobserved Components and Time Series Econometrics

Published in print:
2015
Published Online:
January 2016
ISBN:
9780199683666
eISBN:
9780191763298
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199683666.003.0010
Subject:
Economics and Finance, Econometrics

This chapter generalizes the familiar linear Gaussian unobserved component models or structural time series models to martingale unobserved component models. This generates forecasts whose rate of ... More


View: