000			02786nam a2200265 a 4500
999			_c24833 _d24833
001			58296
010			_a 2008934356
020			_a9780387773162
020			_a0387773169
040			_aBTCTA
082	0	4	_a519.50285/5133 22
100	1		_aKleiber, Christian _958860
245	1	0	_aApplied Econometrics with R / _cChristian Kleiber, Achim Zeileis
260			_aNew York : _bSpringer, _cc2008.
300			_ax, 221 p. : _bill. ; _c24 cm.
504			_aIncludes bibliographical references and index.
520			_aR is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.
520			_aThis is the first book on applied econometrics using the R system for statistical computing and graphics. It presents hands-on examples for a wide range of econometric models, from classical linear regression models for cross-section, time series or panel data and the common non-linear models of microeconometrics such as logit, probit and tobit models, to recent semiparametric extensions. In addition, it provides a chapter on programming, including simulations, optimization, and an introduction to R tools enabling reproducible econometric
650		0	_aR (Computer program language) _92458
650		0	_aEconometrics _xData processing _927247
650		0	_aStatistics _xData processing _936422
700	1		_aZeileis, Achim _958861
856			_uhttps://uowd.box.com/s/qb1me0s3046evk008bsvl8f0v0cngh64 _zLocation Map
942			_cREGULAR _2ddc