Comment on article by Wyse et al.

Research output: Contribution to journalEditorial

Abstract

Computation can be a serious challenge in multiple changepoint models specially when simulation methods are used. This paper, building on earlier contributions such as Fearnhead (2006), makes an important contribution in addressing this challenge. The main contribution of the present paper is, through the use of INLAs, to extend the feasibility of such methods to a wider class of likelihood functions by allowing for data that is dependent within segments. An additional contribution is the development of reduced ¯ltering recursions which further decreases the computational burden. These methods will be found useful in a wide variety of empirical applications. In economics there is a large changepoint (or structural break) literature. Papers such as Stock andWatson (1996), Ang and Bekaert (2002) and Bauwens et al (2011) ¯nd
empirical evidence of widespread parameter instability in macroeconomic and ¯nancial time series. Clements and Hendry (1998) argue that structural breaks are the main reason for forecast failure. Pesaran, Pettenuzzo and Timmermann (2006), Koop and Potter (2007) and Maheu and Gordon (2008), among many others, develop forecasting methods for changepoint models. My comments will focus on the issue of how the methods proposed in the paper can be used in the context of this economic literature.
LanguageEnglish
Pages533-539
Number of pages7
JournalBayesian Analysis
Volume6
Issue number4
DOIs
Publication statusPublished - 2011

Fingerprint

Change-point Model
Structural Breaks
Economics
Time series
Change Point
Macroeconomics
Multiple Models
Likelihood Function
Recursion
Simulation Methods
Forecast
Forecasting
Decrease
Dependent
Context
Evidence
Class

Keywords

  • models
  • structural breaks
  • comment

Cite this

Koop, G. / Comment on article by Wyse et al. In: Bayesian Analysis. 2011 ; Vol. 6, No. 4. pp. 533-539.
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Comment on article by Wyse et al. / Koop, G.

In: Bayesian Analysis, Vol. 6, No. 4, 2011, p. 533-539.

Research output: Contribution to journalEditorial

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AB - Computation can be a serious challenge in multiple changepoint models specially when simulation methods are used. This paper, building on earlier contributions such as Fearnhead (2006), makes an important contribution in addressing this challenge. The main contribution of the present paper is, through the use of INLAs, to extend the feasibility of such methods to a wider class of likelihood functions by allowing for data that is dependent within segments. An additional contribution is the development of reduced ¯ltering recursions which further decreases the computational burden. These methods will be found useful in a wide variety of empirical applications. In economics there is a large changepoint (or structural break) literature. Papers such as Stock andWatson (1996), Ang and Bekaert (2002) and Bauwens et al (2011) ¯ndempirical evidence of widespread parameter instability in macroeconomic and ¯nancial time series. Clements and Hendry (1998) argue that structural breaks are the main reason for forecast failure. Pesaran, Pettenuzzo and Timmermann (2006), Koop and Potter (2007) and Maheu and Gordon (2008), among many others, develop forecasting methods for changepoint models. My comments will focus on the issue of how the methods proposed in the paper can be used in the context of this economic literature.

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