Econometric modelling with time series : specification, estimation and testing / Vance Martin.
Material type: TextSeries: Themes in modern econometricsPublication details: Cambridge : Cambridge University Press, 2013.Description: xxxv, 887 p. : ill. ; 25 cmISBN:- 9780521139816
- HB141 .M3555 2013
Item type | Current library | Collection | Call number | Vol info | Status | Date due | Barcode |
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Main Long | KCA Kitengela Campus Library | Non-fiction | HB141 .M3555 2013 (Browse shelf(Opens below)) | 27317/14 | Available | MOOL14061373 | |
Main Long | Martin Oduor-Otieno Library This item is located on the library first floor | Non-fiction | HB141 .M3555 2013 (Browse shelf(Opens below)) | 27315/14 | Available | MOOL14061375 | |
Main Long | Martin Oduor-Otieno Library This item is located on the library first floor | Non-fiction | HB141 .M3555 2013 (Browse shelf(Opens below)) | 27316/14 | Available | MOOL14061374 | |
Main Long | Martin Oduor-Otieno Library This item is located on the library first floor | Non-fiction | HB141 .M3555 2013 (Browse shelf(Opens below)) | 27318/14 | Available | MOOL14061372 | |
Main Long | Martin Oduor-Otieno Library This item is located on the library first floor | Non-fiction | HB141 .M3555 2013 (Browse shelf(Opens below)) | 27319/14 | Available | MOOL14061371 |
Browsing KCA Kitengela Campus Library shelves, Collection: Non-fiction Close shelf browser (Hides shelf browser)
HB139 .G84 2013 Basic econometrics / | HB139 .S795 2017 A practical guide to using econometrics / | HB139 .W665 2013. Introductory econometrics : | HB141 .M3555 2013 Econometric modelling with time series : | HB171 .S59 1998 Economics for business / | HB171.5 .A695 1995 Economics in our times / | HB171.5 .A695 1995 Economics in our times / |
Includes bibliographical references (pages 865-876) and indexes.
"This book provides a general framework for specifying, estimating, and testing time series econometric models"--
"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"--
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