Introduction to Econometrics
Introduction to Econometrics has been significantly revised to include new developments in the field. The previous editions of this text were renowned for Maddala's clear exposition and the presentation of concepts in an easily accessible manner.
* New chapters have been included on panel data analysis, large sample inference and small sample inference
* Chapter 14 Unit Roots and Cointegration has been rewritten to reflect recent developments in the Dickey-Fuller (DF), the Augmented Dickey-Fuller (ADF) tests and the Johansen procedure
* A selection of data sets and the instructor's manual for the book can be found on our web site
Comments on the previous edition:
'Maddala is an outstanding econometrician who has a deep understaning of the use and potential abuse of econometrics...'
'The strengths of the Maddala book are its simplicity, its accessibility and the large number of examples the book contains...'
'The second edition is well written and the chapters are focused and easy to follow from beginning to end. Maddala has an oustanding grasp of the issues, and the level of mathematics and statistics is appropriate as well.'
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INTRODUCTION AND THE LINEAR REGRESSION MODEL
Statistical Background and Matrix Algebra
16 other sections not shown
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2SLS a x*-distribution adaptive expectations analysis assumptions asymptotic autocorrelation autoregressive bootstrap Chapter cointegration compute confidence interval consider consistent estimates constant term data sets defined demand function denote dependent variable discussed in Section dummy variables Econometrica Econometrics economic endogenous error term errors in variables exogenous variables explanatory variables F-test forecast given Hence heteroskedasticity Illustrative Example independent instrumental variable least squares estimators least squares residuals logit matrix measure multicollinearity multiple regression normal distribution Note null hypothesis observations obtained OLS estimation omitted variables parameters plim probit probit models problem procedure random variables rational expectations regression equation regression model regressors relationship residual sum root tests sample serial correlation significance level standard errors studentized residuals suggested sum of squares supply function Suppose t-ratios Table test statistic time-series unbiased uncorrelated unit root unit root tests values vectors zero
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Regression Models for Categorical and Limited Dependent Variables
J. Scott Long
Limited preview - 1997