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A Box-Cox semiparametric multiplicative error model

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  • Xuehai Zhang

    (Paderborn University)

Abstract

A general class of SemiMEM (semiparametric multiplicative error) models is proposed by introducing a scale function into a MEM (multiplicative error) class model to analyze the non-negative observations. The estimation of the scale function is not limited by any parametric models specification and the moments condition is also reduced via the Box- Cox transformation. For the purpose, an equivalent scale function is applied in a local linear approach and converted to the scale function under weak moment conditions. The equivalent scale function estimation and the bandwidth, the constant factor in the asymp- totic variance and the power transformation parameters estimation are proposed based on the iterative plug-in (IPI) algorithms. In the power transformation estimation, the maximum likelihood estimation (MLE), the normality test and the the quantile-quantile regression (QQr) are employed and simulation algorithms for the confidence interval of estimated power transformation parameter are also developed by the block bootstrap method. The algorithms fit the selected real data well.

Suggested Citation

  • Xuehai Zhang, 2019. "A Box-Cox semiparametric multiplicative error model," Working Papers CIE 125, Paderborn University, CIE Center for International Economics.
    • Handle: RePEc:pdn:ciepap:125
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    File URL: http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/ciepap/WP125.pdf
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    References listed on IDEAS

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    Cited by:

    1. Xuehai Zhang, 2019. "Value at Risk and Expected Shortfall under General Semi-parametric GARCH models," Working Papers CIE 123, Paderborn University, CIE Center for International Economics.
    2. Xuehai Zhang, 2019. "Value at Risk and Expected Shortfall under General Semi-parametric GARCH models," Working Papers CIE 126, Paderborn University, CIE Center for International Economics.

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