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文章基本信息

  • 标题:Sequential estimation of shape parameters in multivariate dynamic models
  • 本地全文:下载
  • 作者:Dante Amengual ; Gabriele Fiorentini ; Enrique Sentana
  • 期刊名称:CEMFI Working Papers / Centro de Estudios Monetarios y Financieros, Madrid
  • 出版年度:2012
  • 卷号:2012
  • 出版社:Centro de Estudios Monetarios y Financieros, Madrid
  • 摘要:

    Sequential maximum likelihood and GMM estimators of distributional parameters obtained from the standardised innovations of multivariate conditionally heteroskedastic dynamic regression models evaluated at Gaussian PML estimators preserve the consistency of mean and variance parameters while allowing for realistic distributions. We assess the efficiency of those estimators, and obtain moment conditions leading to sequential estimators as efficient as their joint maximum likelihood counterparts. We also obtain standard errors for the quantiles required in VaR and CoVaR calculations, and analyse the effects on these measures of distributional misspecification. Finally, we illustrate the small sample performance of these procedures through Monte Carlo simulations.

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