000			03680nam a22004215i 4500
001			978-0-387-92870-8
003			DE-He213
005			20251006084432.0
007			cr nn 008mamaa
008			100710s2009 xxu| s |||| 0|eng d
020			_a9780387928708
020			_a99780387928708
024	7		_a10.1007/978-0-387-92870-8 _2doi
082	0	4	_a330.015195 _223
100	1		_aHorowitz, Joel L. _eauthor.
245	1	0	_aSemiparametric and Nonparametric Methods in Econometrics _h[electronic resource] / _cby Joel L. Horowitz.
264		1	_aNew York, NY : _bSpringer US, _c2009.
300			_aX, 276p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Series in Statistics, _x0172-7397
505	0		_aSingle-Index Models -- Nonparametric Additive Models and Semiparametric Partially Linear Models -- Binary-Response Models -- Statistical Inverse Problems -- Transformation Models.
520			_aStandard methods for estimating empirical models in economics and many other fields rely on strong assumptions about functional forms and the distributions of unobserved random variables. Often, it is assumed that functions of interest are linear or that unobserved random variables are normally distributed. Such assumptions simplify estimation and statistical inference but are rarely justified by economic theory or other a priori considerations. Inference based on convenient but incorrect assumptions about functional forms and distributions can be highly misleading. Nonparametric and semiparametric statistical methods provide a way to reduce the strength of the assumptions required for estimation and inference, thereby reducing the opportunities for obtaining misleading results. These methods are applicable to a wide variety of estimation problems in empirical economics and other fields, and they are being used in applied research with increasing frequency. The literature on nonparametric and semiparametric estimation is large and highly technical. This book presents the main ideas underlying a variety of nonparametric and semiparametric methods. It is accessible to graduate students and applied researchers who are familiar with econometric and statistical theory at the level taught in graduate-level courses in leading universities. The book emphasizes ideas instead of technical details and provides as intuitive an exposition as possible. Empirical examples illustrate the methods that are presented. This book updates and greatly expands the author's previous book on semiparametric methods in econometrics. Nearly half of the material is new. Joel L. Horowitz is the Charles E. and Emma H. Morrison Professor of Market Economics at Northwestern University. He is the author of over 100 journal articles and book chapters in econometrics and statistics, a winner of the Richard Stone prize in applied econometrics, a fellow of the Econometric Society and American Statistical Association, and a former co-editor of Econometrica.
650		0	_aSTATISTICS.
650		0	_aECONOMICS _xSTATISTICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICS FOR BUSINESS/ECONOMICS/MATHEMATICAL FINANCE/INSURANCE.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387928692
830		0	_aSpringer Series in Statistics, _x0172-7397
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-92870-8 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
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