Asymptomatic Properties of the Maximum Likelihood and Non-linear Least Squares Estimators for Noninvertible Moving Average Models PDF Download
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Author: Katsuto Tanaka
Publisher:
ISBN: 9780868311517
Category : Econometric models
Languages : en
Pages : 38
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Book Description
Author: Katsuto Tanaka
Publisher:
ISBN: 9780868311517
Category : Econometric models
Languages : en
Pages : 38
Get Book Here
Book Description
Author: Stanford University. Department of Statistics
Publisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 318
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Book Description
The author considers estimation procedures for the moving average model of order q. Walker's method uses k sample autocovariances (k> or = q). Assume that k depends on T in such a way that k nears infinity as T nears infinity. The estimates are consistent, asymptotically normal and asymptotically efficient if k = k (T) dominates log T and is dominated by (T sub 1/2). The approach in proving these theorems involves obtaining an explicit form for the components of the inverse of a symmetric matrix with equal elements along its five central diagonals, and zeroes elsewhere. The asymptotic normality follows from a central limit theorem for normalized sums of random variables that are dependent of order k, where k tends to infinity with T. An alternative form of the estimator facilitates the calculations and the analysis of the role of k, without changing the asymptotic properties.
Author: Robert Ernest Tarone
Publisher:
ISBN:
Category :
Languages : en
Pages : 190
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Author: Jeremy Sin-hing Wu
Publisher:
ISBN:
Category :
Languages : en
Pages : 246
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Author: Risto Donald Henri Heijmans
Publisher:
ISBN:
Category :
Languages : en
Pages : 71
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Author: R. D. H. Heijmans
Publisher:
ISBN:
Category :
Languages : en
Pages : 25
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Author: R. D. H. Heijmans
Publisher:
ISBN:
Category :
Languages : en
Pages : 84
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Author: R. D. H. Heijmans
Publisher:
ISBN:
Category :
Languages : en
Pages : 84
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Author: Takeshi Amemiya
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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The consistency and the asymptotic normality of the maximum likelihood estimator in the general nonlinear simultaneous equation model are proved. It is shown that the proof depends on the assumption of normality unlike in the linear simultaneous equation model. It is proved that the maximum likelihood estimator is asymptotically more efficient than the nonlinear three-stage least squares estimator if the specification is correct, However, the latter has the advantage of being consistent even when the normality assumption is removed. Hausrnan' s instrumental-variable-interpretation of the maximum likelihood estimator is extended to the general nonlinear simultaneous equation model.
Author: Cliff Attfield
Publisher:
ISBN:
Category :
Languages : en
Pages :
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