000 02300nam a2200277Ii 4500
001 991034258779706706
005 20240319104108.0
008 170103t20162016gw ad b 001 0 eng c
020 _a9781523285136
020 _a1523285133
035 _a(ES-BaCBU).b68335660
040 _aES-CaUJI
_beng
_erda
_cES-CaUJI
050 _aHB139
_b.H437 2016
100 1 _aHeiss, Florian,
_d1973-
_eautor
245 1 0 _aUsing R for introductory econometrics
_c/ Florian Heiss
264 1 _aDüsseldorf :
_b[Florian Heiss],
_c[2016]
300 _a344 pages :
_billustrations, diagrams ;
_c26 cm
504 _aBibliography: p. [335]-336. Index.
505 0 _aI. Regression analysis with cross-sectional data. The simple regression model — Multiple regression analysis: estimation — Multiple regression analysis: inference — Multiple regression analysis: OLS asymptotics — Multiple regression analysis: further issues — Multiple regression analysis with qualitative regressors — Heteroscedasticity — More on specification and data issues — II. Regression analysis with time series data. Basic regression analysis with time series data — Further issues in using OLS with time series data — Serial correlation and heteroscedasticity in time series regressions — III. Advanced topics. Pooling cross-sections across time: simple panel data methods — Advanced panel data methods — Instrumental variables estimation and two stage least squares — Simultaneous equations models — Limited dependent variable models and sample selection corrections — Advanced time series topics — Carrying out an empirical project — IV. Appendices. R scripts.
520 _a"This book does not attempt to provide a self-contained discussion of econometric models and methods. It also does not give an independent general introduction to R. Instead, it builds on the excellent and popular textbook 'Introductory Econometrics' by Wooldridge (2016). It is compatible in terms of topics, organization, terminology, and notation, and is designed for a seamless transition from theory to practice."--
650 0 _aEconometrics
_96309
650 0 _aR (Computer program language)
_912305
650 0 _aRegression analysis
_95701
942 _2lcc
999 _c3623
_d3623
041 _aEnglish