Likelihood inference for dynamic linear models with Markov switching parameters: on the efficiency of the Kim filter

Young Min Kim, Kyu Ho Kang

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)

    Abstract

    The Kim filter (KF) approximation is widely used for the likelihood calculation of dynamic linear models with Markov regime-switching parameters. However, despite its popularity, its approximation error has not yet been examined rigorously. Therefore, this study investigates the reliability of the KF approximation for maximum likelihood (ML) and Bayesian estimations. To measure the approximation error, we compare the outcomes of the KF method with those of the auxiliary particle filter (APF). The APF is a numerical method that requires a longer computing time, but its numerical error can be sufficiently minimized by increasing simulation size. According to our extensive simulation and empirical studies, the likelihood values obtained from the KF approximation are practically identical to those of the APF. Furthermore, we show that the KF method is reliable, particularly when regimes are persistent and sample size is small. From the Bayesian perspective, we show that the KF method improves the efficiency of posterior simulation. This study contributes to the literature by providing evidence to justify the use of the KF method in both ML and Bayesian estimations.

    Original languageEnglish
    Pages (from-to)1109-1130
    Number of pages22
    JournalEconometric Reviews
    Volume38
    Issue number10
    DOIs
    Publication statusPublished - 2019 Nov 26

    Bibliographical note

    Publisher Copyright:
    © 2018, © 2018 Taylor & Francis Group, LLC.

    Keywords

    • State space model
    • auxiliary particle filter
    • maximum likelihood estimation
    • posterior sampling

    ASJC Scopus subject areas

    • Economics and Econometrics

    Fingerprint

    Dive into the research topics of 'Likelihood inference for dynamic linear models with Markov switching parameters: on the efficiency of the Kim filter'. Together they form a unique fingerprint.

    Cite this