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Journal article

Optimal conditionally unbiased bounded-influence inference in dynamic location and scale models

  • Mancini, Loriano Istituto di finanza (IFin), Facoltà di scienze economiche, Università della Svizzera italiana, Svizzera
  • Ronchetti, Elvezio Istituto di finanza (IFin), Facoltà di scienze economiche, Università della Svizzera italiana, Svizzera
  • Trojani, Fabio Istituto di finanza (IFin), Facoltà di scienze economiche, Università della Svizzera italiana, Svizzera
    2004
Published in:
  • Journal of the American Statistical Association. - American Statistical Association. - 2005, vol. 100, no. 470, p. 628-641
Time series models
M-estimators
influence function
robust estimation and testing
English This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gaussian reference model. Based on these results, optimal bounded-influence versions of the classical likelihood-based tests for parametric hypotheses are obtained. We propose a feasible and efficient algorithm for the computation of our robust estimators, which makes use of analytical Laplace approximations to estimate the auxiliary recentering vectors ensuring Fisher consistency in robust estimation. This strongly reduces the necessary computation time by avoiding the simulation of multidimensional integrals, a task that has typically to be addressed in the robust estimation of nonlinear models for time series. In some Monte Carlo simulations of an AR(1)-ARCH(1) process we show that our robust procedures maintain a very high efficiency under ideal model conditions and at the same time perform very satisfactorily under several forms of departure from conditional normality. On the contrary, classical Pseudo Maximum Likelihood inference procedures are found to be highly inefficient under such local model misspecifications. These patterns are confirmed by an application to robust testing for ARCH.
Language
  • English
Classification
Economics
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https://n2t.net/ark:/12658/srd1318076
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