Efficient Estimation in a Semiparametric Autoregressive Model |
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Authors: | Anton Schick |
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Affiliation: | (1) Department of Mathematical Sciences, Binghamton University, Binghamton, NY 13902-6000, USA |
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Abstract: | This paper constructs efficient estimates of the parameter in the semiparametric auto-regression model ,with a smooth function and independent and identically distributed innovations t with zero means and finite variances. This will be done under the assumptions that and that the errors have a density with finite Fisher information for location. The former condition guarantees that the process can be chosen to be stationary and ergodic. |
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Keywords: | Stationary Markov chains ergodicity V-uniform ergodicity local asymptotic normality local asymptotic minimaxity contiguity sample splitting |
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