Elements of Econometrics
This classic text has proven its worth in university classrooms and as a tool kit in research--selling over 40,000 copies in the United States and abroad in its first edition alone. Users have included undergraduate and graduate students of economics and business, and students and researchers in political science, sociology, and other fields where regression models and their extensions are relevant. The book has also served as a handy reference in the "real world" for people who need a clear and accurate explanation of techniques that are used in empirical research.
Throughout the book the emphasis is on simplification whenever possible, assuming the readers know college algebra and basic calculus. Jan Kmenta explains all methods within the simplest framework, and generalizations are presented as logical extensions of simple cases. And while a relatively high degree of rigor is preserved, every conflict between rigor and clarity is resolved in favor of the latter. Apart from its clear exposition, the book's strength lies in emphasizing the basic ideas rather than just presenting formulas to learn and rules to apply.
The book consists of two parts, which could be considered jointly or separately. Part one covers the basic elements of the theory of statistics and provides readers with a good understanding of the process of scientific generalization from incomplete information. Part two contains a thorough exposition of all basic econometric methods and includes some of the more recent developments in several areas.
As a textbook,Elements of Econometricsis intended for upper-level undergraduate and master's degree courses and may usefully serve as a supplement for traditional Ph.D. courses in econometrics. Researchers in the social sciences will find it an invaluable reference tool.
A solutions manual is also available for teachers who adopt the text for coursework.
Jan Kmenta is Professor Emeritus of Economics and Statistics, University of Michigan.
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Introduction to Statistical Inference
Experimental Derivation of Sampling Distributions
Probability and Probability Distributions
12 other sections not shown
acceptance region assumed asymptotic variance asymptotically efficient autoregressive calculated CN CN CN confidence intervals consider consistent estimator correlation corresponding covariance defined dependent variable derived determined discussion distributed lag Econometrics economic elements endogenous variables equal to zero Error Type example explanatory variables finite follows formula given heteroskedasticity homoskedasticity income independent instrumental variable involves known least squares estimators least squares method level of significance likelihood function linear regression linear regression model maximum likelihood estimators measured multicollinearity nonlinear nonstochastic normally distributed Note null hypothesis obtained ordinary least squares population mean problem random variable reduced form regression coefficients regression disturbance regression equation regression line reject represents restrictions sample mean sample observations sampling distribution Section simple regression specification standard deviation standard errors stochastic structural equation sum of squares Suppose Table test statistic theorem tion two-stage least squares unbiased estimator unbiasedness unrestricted variance-covariance matrix