Title : | Introductory econometrics : a modern approach |
Material Type: | printed text |
Authors: | Jeffrey M. Wooldridge, Author |
Edition statement: | 7th edition |
Publisher: | Cengage Learning (Boston, MA) |
Publication Date: | 2020 |
Pagination: | xxii, 826 p. |
Layout: | ill. |
Size: | 26 cm |
ISBN (or other code): | 978-1-337-55886-0 |
General note: | Includes bibliographical references (p. 791-796)
Includes index (p. 812-826) |
Languages : | English (eng) Original Language : English (eng) |
Descriptors: | Econometric models Econometrics Economics
|
Class number: | 330.015195 |
Abstract: | Wooldridge uses a systematic approach motivated by the major problems facing applied researchers. This text provides important understanding for empirical work in many social sciences, as well as for carrying out research projects. |
Contents note: | The nature of econometrics and economic date; 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 information; Heteroskedasticity; More on specification and data issues; 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 heteroskedasticity in time series regressions; Advances 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; Math refresher A, basic mathematical tools; Math reresher B, fundamentals of probability; Math refresher C, fundamentals of mathematical statistics; Advanced treatment D, summary of matrix algebra; Advanced treatment D, the linear regression model in matrix form; |
Record link: | https://library.seeu.edu.mk/index.php?lvl=notice_display&id=19537 |