000			03305nam a2200361 i 4500
005			20250930145250.0
008			130814s2013 enka b 001 0 eng
020			_a9780521196604 (hbk.) _cRM700.57
020			_a9780521139816 (pbk.)
039		9	_a201312021446 _bzabidah _c201311140849 _dhamudah _y08-14-2013 _zhamudah
040			_aDLC _beng _cDLC _erda _dUKM
090			_aHB141.M377
090			_aHB141 _b.M377
100	1		_aMartin, Vance, _d1955-
245	1	0	_aEconometric modelling with time series : _bspecification, estimation and testing / _cVance Martin, University of Melbourne, Australia, Stan Hurn, Queensland University of Technology, Australia, David Harris, Monash University, Australia.
264		1	_aCambridge, U. K. : _bCambridge University Press, _c2013.
300			_axxxv, 887 pages : _billustrations ; _c25 cm.
490	0		_aThemes in modern econometrics
504			_aIncludes bibliographical references (pages 865-876) and indexes.
520			_a'This book provides a general framework for specifying, estimating, and testing time series econometric models'-- _cProvided by publisher.
520			_a'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'-- _cProvided by publisher.
650		0	_aEconometric models.
650		0	_aTime-series analysis. _961130
700	1		_aHurn, Stan.
700	1		_aHarris, David, _d1969-
907			_a.b15701086 _b2019-11-12 _c2019-11-12
942			_c01 _n0 _kHB141.M377
914			_avtls003537099
990			_aza
991			_aFakulti Ekonomi dan Pengurusan
998			_at _b2013-01-08 _cm _da _feng _genk _y0 _z.b15701086
999			_c681694 _d681694