Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

Victor Leiva, Shuangzhe LIU, Lei Shi, Francisco Cysneiros

    Research output: Contribution to journalArticle

    11 Citations (Scopus)

    Abstract

    We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.
    Original languageEnglish
    Pages (from-to)627-642
    Number of pages16
    JournalJournal of Applied Statistics
    Volume43
    Issue number4
    DOIs
    Publication statusPublished - 2016

    Fingerprint

    Econometrics
    Regression Model
    Diagnostics
    Elliptically Contoured Distribution
    Influence Diagnostics
    Normal Curvature
    Restriction
    Perturbation
    Methodology
    Linear Regression Model
    Slope
    Evaluate
    Simulation
    Regression model
    Model
    Influence
    Curvature
    World economy
    Linear regression model

    Cite this

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    abstract = "We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.",
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    Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics. / Leiva, Victor; LIU, Shuangzhe; Shi, Lei; Cysneiros, Francisco.

    In: Journal of Applied Statistics, Vol. 43, No. 4, 2016, p. 627-642.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

    AU - Leiva, Victor

    AU - LIU, Shuangzhe

    AU - Shi, Lei

    AU - Cysneiros, Francisco

    PY - 2016

    Y1 - 2016

    N2 - We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.

    AB - We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.

    KW - computational statistics

    KW - elliptically contoured distributions

    KW - generalized least squares

    U2 - 10.1080/02664763.2015.1072140

    DO - 10.1080/02664763.2015.1072140

    M3 - Article

    VL - 43

    SP - 627

    EP - 642

    JO - Journal of Applied Statistics

    JF - Journal of Applied Statistics

    SN - 0266-4763

    IS - 4

    ER -