Modern Econometrics: An Introduction
Econometrics has experienced remarkable changes in the past 15 years, particularly in the area of time series analysis. The development of cointegration techniques has enabled econometricians to deal with the problems of spurious regression and non-stationary time series. Parallel to this development has come the increased acceptance of general-to-specific methodology, combined with the use of error correction models. Modern Econometrics recognises the need for today's students to have a sound grasp of recent developments in econometrics. It successfully incorporates modern topics and integrates them with more traditional material. Whilst not avoiding a rigorous mathematical treatment where necessary, the text takes an intuitive approach to the more advanced topics. The result is an accessible undergraduate text providing a motivating, relevant and understandable introduction to econometrics.
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asymptotic autocorrelation bias biased Chapter classical assumptions coefficient of determination cointegration column COMPUTER EXERCISE confidence interval consider consistent estimator constant consumption Correlogram covariance critical values data set degrees of freedom dependent variable disequilibrium error disturbance Durbin-Watson statistic econometricians econometrics economic estimating equation example expenditure explanatory variables fact Figure first-order function given Hence heteroskedasticity homoskedastic household implies income large samples least squares level of significance likelihood function long-run relationship marginal probability matrix multicollinearity multiple regression non-stationary nonlinear normally distributed null hypothesis observations obtain OLS estimators parameters plim population regression equation population regression line possible predicted probability distribution problem procedure properties random ratio regressors reject H0 residual sum restrictions result sample regression sampling distribution scalar Section standard errors standard normal stationary stochastic Student's t distribution sum of squares Suppose Table test statistic Theorem trend two-variable regression unbiased estimator uncorrelated variance-covariance matrix vector x2 distribution yields zero