摘要
In this article,we study a robust estimation method for a general class of integervalued time series models.The conditional distribution of the process belongs to a broad class of distributions and unlike the classical autoregressive framework,the conditional mean of the process also depends on some exogenous covariates.We derive a robust inference procedure based on the minimum density power divergence.Under certain regularity conditions,we establish that the proposed estimator is consistent and asymptotically normal.In the case where the conditional distribution belongs to the exponential family,we provide sufficient conditions for the existence of a stationary and ergodicτ-weakly dependent solution.Simulation experiments are conducted to illustrate the empirical performances of the estimator.An application to the number of transactions per minute for the stock Ericsson B is also provided.
基金
supported by the MME-DII center of excellence(ANR-11-LABEX-0023-01)
the ANR BREAKRISK:ANR-17-CE26-0001-01
the CY Initiative of Excellence(grant“Investissements d’Avenir”ANR-16-IDEX-0008)
Project“EcoDep”PSI-AAP2020-0000000013.
