Finite-sample Properties of Maximum-likelihood Estimators PDF Download
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Author: Alex McMillan
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 262
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Book Description
Author: Alex McMillan
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 262
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Book Description
Author: Nancy Milena Hoyos Gomez
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Vance Martin
Publisher: Cambridge University Press
ISBN: 0521139813
Category : Business & Economics
Languages : en
Pages : 925
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Book Description
"Maximum likelihood estimation is a general method for estimating the parameters of econometric models from observed data. The principle of maximum likelihood plays a central role in the exposition of this book, since a number of estimators used in econometrics can be derived within this framework. Examples include ordinary least squares, generalized least squares and full-information maximum likelihood. In deriving the maximum likelihood estimator, a key concept is the joint probability density function (pdf) of the observed random variables, yt. Maximum likelihood estimation requires that the following conditions are satisfied. (1) The form of the joint pdf of yt is known. (2) The specification of the moments of the joint pdf are known. (3) The joint pdf can be evaluated for all values of the parameters, 9. Parts ONE and TWO of this book deal with models in which all these conditions are satisfied. Part THREE investigates models in which these conditions are not satisfied and considers four important cases. First, if the distribution of yt is misspecified, resulting in both conditions 1 and 2 being violated, estimation is by quasi-maximum likelihood (Chapter 9). Second, if condition 1 is not satisfied, a generalized method of moments estimator (Chapter 10) is required. Third, if condition 2 is not satisfied, estimation relies on nonparametric methods (Chapter 11). Fourth, if condition 3 is violated, simulation-based estimation methods are used (Chapter 12). 1.2 Motivating Examples To highlight the role of probability distributions in maximum likelihood estimation, this section emphasizes the link between observed sample data and 4 The Maximum Likelihood Principle the probability distribution from which they are drawn"-- publisher.
Author: Borwornsri Somboonpanya
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 190
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Author: Zacharias Psaradakis
Publisher:
ISBN:
Category : Autoregression (Statistics)
Languages : en
Pages : 0
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Author: Zacharias G. Psaradakis
Publisher:
ISBN:
Category :
Languages : en
Pages : 15
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Author: Chin Chin Ong
Publisher:
ISBN:
Category :
Languages : en
Pages : 52
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Author: Nicholas Herbert Stern
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 28
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Author: Lars Peter Hansen
Publisher: Franklin Classics Trade Press
ISBN: 9780353246904
Category : History
Languages : en
Pages : 64
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Book Description
This work has been selected by scholars as being culturally important and is part of the knowledge base of civilization as we know it. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. To ensure a quality reading experience, this work has been proofread and republished using a format that seamlessly blends the original graphical elements with text in an easy-to-read typeface. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Author: Yong Bao
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
We investigate the finite sample properties of the maximum likelihood estimator for the spatial autoregressive model. A stochastic expansion of the score function is used to develop the second-order bias and mean squared error of the maximum likelihood estimator. We show that the results can be expressed in terms of the expectations of cross products of quadratic forms, or ratios of quadratic forms in a normal vector which can be evaluated using the top order invariant polynomial. Our numerical calculations demonstrate that the second-order behaviors of the maximum likelihood estimator depend on the degree of sparseness of the weights matrix.
