Asymptotic Properties of Simultaneous Least Squares Estimators PDF Download
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Author: Phoebus J. Dhrymes
Publisher:
ISBN:
Category :
Languages : en
Pages : 54
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Book Description
Author: Phoebus J. Dhrymes
Publisher:
ISBN:
Category :
Languages : en
Pages : 54
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Book Description
Author: Virendra K. Srivastava
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 18
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Author: Jeremy Sin-hing Wu
Publisher:
ISBN:
Category :
Languages : en
Pages : 246
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Book Description
Author: Enrico Gori
Publisher:
ISBN:
Category :
Languages : en
Pages : 9
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Book Description
Author: Phoebus J. Dhrymes
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
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Author: Anders Klevmarken
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ISBN:
Category :
Languages : en
Pages : 22
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Author: Katsuto Tanaka
Publisher:
ISBN: 9780868311517
Category : Econometric models
Languages : en
Pages : 38
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Author: Michael Mohr
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ISBN:
Category :
Languages : de
Pages : 25
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Author: Stanford University. Department of Statistics
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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:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
A particular two dimensional model in a stationary random field, which has a wide applications in statistical signal processing and in texture classifications, is considered. We prove the consistency and also obtain the asymptotic distributions of the least squares estimators of the different model parameters. It is observed that the asymptotic distribution of the least squares estimators are multivariate normal. Some numerical experiments are performed to see how the asymptotic results work for finite samples. We propose some open problems at the end.
