Normal view
MARC view
Using econometrics : a practical guide / A.H. Studenmund.
Series: Pearson series in economicsPublication details: Boston : Addison-Wesley, c2011.Edition: 6th edDescription: xvii, 616 p. : ill. ; 24 cmContent type:- text
- unmediated
- volume
- 9780131379985
- 0131379984
- 9781292021270
- HB139.S795 2011
Contents:
An overview of regression analysis -- Ordinary least squares -- Learning to use regression analysis -- The classical model -- Hypothesis testing -- Specification : choosing the independent variables -- Specification : choosing a functional form -- Multicollinearity -- Serial correlation -- Heteroskedasticity -- Running your own regression project -- Time-series models -- Dummy dependent variable techniques -- Simultaneous equations -- Forecasting -- Experimental and panel data -- Statistical principles.
| Item type | Current library | Home library | Shelving location | Call number | Status | Barcode | |
|---|---|---|---|---|---|---|---|
Books
|
American University in Dubai | American University in Dubai | Main Collection | HB139.S795 2011 (Browse shelf(Opens below)) | Available | 5134650 |
Browsing American University in Dubai shelves, Shelving location: Main Collection Close shelf browser (Hides shelf browser)
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| HB 139 .M35 1977 Econometrics / | HB 139 .M3813 1980 Statistical methods of econometrics / | HB 139 .S765 2011 Introduction to econometrics / | HB139.S795 2011 Using econometrics : a practical guide / | HB 139 .S795 2016 A practical guide to using econometrics / | HB 141 .B38 2006 An introduction to modern econometrics using Stata / | HB 141 .K53 1974 An introduction to econometric forecasting and forecasting models / |
Includes bibliographical references and index.
An overview of regression analysis -- Ordinary least squares -- Learning to use regression analysis -- The classical model -- Hypothesis testing -- Specification : choosing the independent variables -- Specification : choosing a functional form -- Multicollinearity -- Serial correlation -- Heteroskedasticity -- Running your own regression project -- Time-series models -- Dummy dependent variable techniques -- Simultaneous equations -- Forecasting -- Experimental and panel data -- Statistical principles.
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