Basic econometrics / Damodar N. Gujarati.

By: Gujarati, Damodar N [author]
Publisher: Boston McGraw Hill 2003Copyright date: c2003Edition: Fourth editionDescription: xxix, 1002 pages : illustrations; 24 cm. + 1 computer optical disc (4 3/4 in.)Content type: text Media type: unmediated Carrier type: volumeISBN: 0072335424; 0071123423 (International ed.)Subject(s): EconometricsDDC classification: 330/.01/5195 LOC classification: HB139 | .G84 2003
Contents:
pt. 1. Single-equation regression models. The nature of regression analysis -- Two-variable regression analysis : some basic ideas -- Two-variable regression model : the problem of estimation -- Classical normal linear regression model (CNLRM) -- Two-variable regression : interval estimation and hypothesis testing -- Extensions of the two-variable linear regression model -- Multiple regression analysis : the problem of estimation -- Multiple regression analysis : the problem of inference -- Dummy variable regression models -- pt. 2. Relaxing the assumptions of the classical model. Multicollinearity : what happens if the regressors are correlated -- Heteroscedasticity : what happens if the error variance is nonconstant? -- Autocorrelation : what happens if the error terms are correlated -- Econometric modeling : model specification and diagnostic testing -- pt. 3. Topics in econometrics. Nonlinear regression models -- Qualitative response regression models -- Panel data regression models -- Dynamic econometric models : autoregressive and distributed-lag models -- pt. 4. Simultaneous-equation models. Simultaneous-equation models -- The identification problem -- Simultaneous-equation methods -- Time series econometrics : some basic concepts -- Time series econometrics : forecasting.
Summary: Accompanying disc contains all of the in-text data sets plus an additional 50 that are not in the text.
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SUBJECT REFERENCE 		330.015195 G949 2003 (Browse shelf) Available CL-35897
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Includes bibliographical references (p. 979-982) and indexes.

pt. 1. Single-equation regression models. The nature of regression analysis --
Two-variable regression analysis : some basic ideas --
Two-variable regression model : the problem of estimation --
Classical normal linear regression model (CNLRM) --
Two-variable regression : interval estimation and hypothesis testing --
Extensions of the two-variable linear regression model --
Multiple regression analysis : the problem of estimation --
Multiple regression analysis : the problem of inference --
Dummy variable regression models --
pt. 2. Relaxing the assumptions of the classical model. Multicollinearity : what happens if the regressors are correlated --
Heteroscedasticity : what happens if the error variance is nonconstant? --
Autocorrelation : what happens if the error terms are correlated --
Econometric modeling : model specification and diagnostic testing --
pt. 3. Topics in econometrics. Nonlinear regression models --
Qualitative response regression models --
Panel data regression models --
Dynamic econometric models : autoregressive and distributed-lag models --
pt. 4. Simultaneous-equation models. Simultaneous-equation models --
The identification problem --
Simultaneous-equation methods --
Time series econometrics : some basic concepts --
Time series econometrics : forecasting.


Accompanying disc contains all of the in-text data sets plus an additional 50 that are not in the text.

300-399

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