Asymptotic properties of conditional least-squares estimators for array time series

Rajae Azrak () and Guy Mélard ()
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Rajae Azrak: Mohammed V University of Rabat
Guy Mélard: Université libre de Bruxelles

Statistical Inference for Stochastic Processes, 2021, vol. 24, issue 3, No 2, 525-547

Abstract: Abstract The paper provides a kind of Klimko–Nelson’s 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. Besides, we provide sufficient conditions for two of the assumptions and a procedure for the evaluation of the information matrix in array time series. In addition to time-dependent models, illustrations to a threshold model and a count data model are given.

Keywords: Klimko–Nelson’s theorems; Non-stationary process; Multivariate time series; Time-varying models; Information matrix; Primary 62M10; 60K35; secondary 60G12 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:sistpr:v:24:y:2021:i:3:d:10.1007_s11203-021-09242-8

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DOI: 10.1007/s11203-021-09242-8

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