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Asymptotic Properties of Conditional Least-squares Estimators for Array Time Series


  • Rajae Azrak
  • Guy Melard


The paper provides a kind of Klimko-Nelson theorems alternative in the case of conditional least-squares and M-estimators for array time series, when the assumptions of almost sure convergence cannot be established. We do not assume stationarity nor even local stationarity. In addition, we provide sufficient conditions for two of the assumptions and two theorems for the evaluation of the information matrix in array time series. In addition to time dependent models, illustrations to a threshold model and to a count data model are given.

Suggested Citation

  • Rajae Azrak & Guy Melard, 2020. "Asymptotic Properties of Conditional Least-squares Estimators for Array Time Series," Working Papers ECARES 2020-12, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:eca:wpaper:2013/304276

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