A New Measure for the Efficiency of the Ordinary Least Squares Estimator in a General Linear Regression Model PDF Download
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Author: Hiroshi Kurata
Publisher:
ISBN:
Category : Least squares
Languages : en
Pages : 50
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Book Description
Author: Hiroshi Kurata
Publisher:
ISBN:
Category : Least squares
Languages : en
Pages : 50
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Book Description
Author: Marvin Gruber
Publisher: Routledge
ISBN: 1351439162
Category : Mathematics
Languages : en
Pages : 648
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Book Description
Offers a treatment of different kinds of James-Stein and ridge regression estimators from a frequentist and Bayesian point of view. The book explains and compares estimators analytically as well as numerically and includes Mathematica and Maple programs used in numerical comparison.;College or university bookshops may order five or more copies at a special student rate, available on request.
Author: Denzil G. Fiebig
Publisher:
ISBN:
Category : Least squares
Languages : en
Pages : 44
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Book Description
Author: Takeshi Amemiya
Publisher: Harvard University Press
ISBN: 9780674005600
Category : Business & Economics
Languages : en
Pages : 540
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Book Description
The main features of this text are a thorough treatment of cross-section models—including qualitative response models, censored and truncated regression models, and Markov and duration models—and a rigorous presentation of large sample theory, classical least-squares and generalized least-squares theory, and nonlinear simultaneous equation models.
Author: Jaechoul Lee
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
When a straight line is fitted to time series data, generalized least squares (GLS) estimators of the trend slope and intercept are attractive as they are unbiased and of minimum variance. However, computing GLS estimators is laborious as their form depends on the autocovariances of the regression errors. On the other hand, ordinary least squares (OLS) estimators are easy to compute and do not involve the error autocovariance structure. It has been known for 50 years that OLS and GLS estimators have the same asymptotic variance when the errors are second-order stationary. Hence, little precision is gained by using GLS estimators in stationary error settings. This article revisits this classical issue, deriving explicit expressions for the GLS estimators and their variances when the regression errors are drawn from an autoregressive process. These expressions are used to show that OLS methods are even more efficient than previously thought. Specifically, we show that the convergence rate of variance differences is one polynomial degree higher than that of least squares estimator variances. We also refine Grenander's (1954) variance ratio. An example is presented where our new rates cannot be improved upon. Simulations show that the results change little when the autoregressive parameters are estimated.
Author: Michael McAleer
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
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Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 804
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Book Description
Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
Author:
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 780
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Book Description
Author: Butte Gotu
Publisher:
ISBN:
Category :
Languages : en
Pages : 15
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Book Description
Author: Laurie L. Burney
Publisher: Emerald Group Publishing
ISBN: 1839829141
Category : Business & Economics
Languages : en
Pages : 145
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Book Description
Advances in Management Accounting (AIMA) is a publication of quality applied research in management accounting. The journal’s purpose is to publish thought-provoking articles that advance knowledge in the management accounting discipline and are of interest to both academics and practitioners.
