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Introductory econometrics : a modern approach / Jeffrey M. Wooldridge.

By: Material type: TextTextPublication details: Mason, OH : South-Western Cengage Learning, c2013.Edition: FifthDescription: xxv, 881 p. : ill. ; 24 cmISBN:
  • 1111531048 (hbk.)
  • 9781111531041 (hbk.)
Subject(s): LOC classification:
  • HB139 .W51
Online resources:
Contents:
Ch. 1. The nature of econometrics and economic data -- Pt. 1. Regression analysis with cross-sectional data -- Ch. 2. The simple regression model -- Ch. 3. Multiple regression analysis: estimation -- Ch. 4. Multiple regression analysis: inference -- Ch. 5. Multiple regression analysis: OLS asymptotics -- Ch. 6. Multiple regression analysis: further issues -- Ch. 7. Multiple regression analysis with qualitative information: binary (or dummy) variables -- Ch. 8. Hetroskedasticity -- Ch. 9. More on specification and data issues -- Pt. 2. Regression analysis with time series data -- Ch. 10. Basic regression analysis with time series data -- Ch. 11. Further issues in using OLS with time series data -- Ch. 12. Serial correlation and heteroskedasticity in time series regressions -- Ch. 13. Pooling cross sections across time: simple panel data methods -- Ch. 14. Advanced panel data methods -- Ch. 15. Instrumental variables estimation and two stage least squares -- Ch. 16. Simultaneous equations models -- Ch. 17. Limited dependent variable models and sample selection corrections -- Ch. 18. Advanced time series topics -- Ch. 19. Carrying out an empirical project -- Appendices.
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Holdings
Item type Current library Call number Status Date due Barcode
Books Books CKT-UTAS Library General Stacks HB139 W51 (Browse shelf(Opens below)) Available NAV-51703062216

Includes bibliographical references (p. 838-843) and index.

Ch. 1. The nature of econometrics and economic data -- Pt. 1. Regression analysis with cross-sectional data -- Ch. 2. The simple regression model -- Ch. 3. Multiple regression analysis: estimation -- Ch. 4. Multiple regression analysis: inference -- Ch. 5. Multiple regression analysis: OLS asymptotics -- Ch. 6. Multiple regression analysis: further issues -- Ch. 7. Multiple regression analysis with qualitative information: binary (or dummy) variables -- Ch. 8. Hetroskedasticity -- Ch. 9. More on specification and data issues -- Pt. 2. Regression analysis with time series data -- Ch. 10. Basic regression analysis with time series data -- Ch. 11. Further issues in using OLS with time series data -- Ch. 12. Serial correlation and heteroskedasticity in time series regressions -- Ch. 13. Pooling cross sections across time: simple panel data methods -- Ch. 14. Advanced panel data methods -- Ch. 15. Instrumental variables estimation and two stage least squares -- Ch. 16. Simultaneous equations models -- Ch. 17. Limited dependent variable models and sample selection corrections -- Ch. 18. Advanced time series topics -- Ch. 19. Carrying out an empirical project -- Appendices.

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