Ouyou toukeigaku
Online ISSN : 1883-8081
Print ISSN : 0285-0370
ISSN-L : 0285-0370
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Volume 32, Issue 2
Displaying 1-3 of 3 articles from this issue
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  • Bayesian Analysis of the Seasonal Moving Average Model: A Gibbs Sampling Approach
    M.A. Ismail
    2003 Volume 32 Issue 2 Pages 61-75
    Published: December 15, 2003
    Released on J-STAGE: June 12, 2009
    DOIhttps://doi.org/10.5023/jappstat.32.61
    JOURNAL FREE ACCESS
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    This paper develops a Bayesian analysis for the multiplicative seasonal moving average model by implementing a fast, easy and accurate Gibbs sampling algorithm. The proposed algorithm does not involve any Metropolis-Hastings generation but is generated from normal and inverse gamma distributions. The problem of forecasting multiple future observations is considered. The proposed algorithm is illustrated using a simulated example and airline data. Unlike the classical approach, by using the airline data, the proposed algorithm is easily used to test the significance of the interaction parameter.
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  • Copulas and Their Applications
    Hideatsu Tsukahara
    2003 Volume 32 Issue 2 Pages 77-88
    Published: December 15, 2003
    Released on J-STAGE: June 12, 2009
    DOIhttps://doi.org/10.5023/jappstat.32.77
    JOURNAL FREE ACCESS
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    Copulas have recently been of great interest to statisticians as well as financial econometricians since they give a promising, flexible tool for understanding dependence among random variables, and for modeling and simulating nonnormal multivariate data. In its simplest form, a d-dimensional copula function (or simply d-copula) is a d-dimensional distribution function with all univariate marginals being U(0, 1) distribution. The usefulness of copulas comes from Sklar's theorem, which states that any d-dimensional distribution function F can be represented as
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  • Instrumental Variable (IV) Selection for Estimating Total Effects in Conditional IV Method
    Manabu KUROKI, Masami MIYAKAWA, Ryouhei KAWATA
    2003 Volume 32 Issue 2 Pages 89-100
    Published: December 15, 2003
    Released on J-STAGE: June 12, 2009
    DOIhttps://doi.org/10.5023/jappstat.32.89
    JOURNAL FREE ACCESS
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    Suppose that causal relationships among variables can be described by a causal diagram and the corresponding linear structural equation model. In order to identify total effects, Brito and Pearl (2002) have suggested the conditional instrumental variable method, which is provided as a generalization of the instrumental variable method. However, the selection of conditional instrumental variable given a set of covariates is not unique. In this paper, when a total effect can be estimated through the conditional instrumental variable method, we formulate the asymptotic variance of the total effect. In addition, when there are some conditional instrumental variables given a set of covariates, the difference between them is investigated based on the formulation.
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