000			02155nam a22003257a 4500
001			sulb-eb0016849
003			BD-SySUS
005			20160405140623.0
008			110225s2011||||enk o ||1 0|eng|d
020			_a9781139033848 (ebook)
020			_z9780521869591 (hardback)
020			_z9780521689731 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP _dBD-SySUS.
050	0	0	_aHB139 _b.J795 2012
082	0	0	_a330.01/5195 _223
100	1		_aJudge, George G., _eauthor.
245	1	3	_aAn Information Theoretic Approach to Econometrics / _cGeorge G. Judge, Ron C. Mittelhammer.
264		1	_aCambridge : _bCambridge University Press, _c2011.
300			_a1 online resource (248 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 04 Apr 2016).
520			_aThis book is intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic econometric models and methods. Because most data are observational, practitioners work with indirect noisy observations and ill-posed econometric models in the form of stochastic inverse problems. Consequently, traditional econometric methods in many cases are not applicable for answering many of the quantitative questions that analysts wish to ask. After initial chapters deal with parametric and semiparametric linear probability models, the focus turns to solving nonparametric stochastic inverse problems. In succeeding chapters, a family of power divergence measure-likelihood functions are introduced for a range of traditional and nontraditional econometric-model problems. Finally, within either an empirical maximum likelihood or loss context, Ron C. Mittelhammer and George G. Judge suggest a basis for choosing a member of the divergence family.
650		0	_aEconometrics
700	1		_aMittelhammer, Ron C., _eauthor.
776	0	8	_iPrint version: _z9780521869591
856	4	0	_uhttp://dx.doi.org/10.1017/CBO9781139033848
942			_2Dewey Decimal Classification _ceBooks
999			_c38287 _d38287