Econometrics (eBook)

Fumio Hayashi (Autor)

eBook Download: EPUB

2011
712 Seiten
Princeton University Press (Verlag)
978-1-4008-2383-3 (ISBN)

Infos

The most authoritative and comprehensive synthesis of modern econometrics availableEconometrics provides first-year graduate students with a thoroughly modern introduction to the subject, covering all the standard material necessary for understanding the principal techniques of econometrics, from ordinary least squares through cointegration. The book is distinctive in developing both time-series and cross-section analysis fully, giving readers a unified framework for understanding and integrating results.Econometrics covers all the important topics in a succinct manner. All the estimation techniques that could possibly be taught in a first-year graduate course, except maximum likelihood, are treated as special cases of GMM (generalized methods of moments). Maximum likelihood estimators for a variety of models, such as probit and tobit, are collected in a separate chapter. This arrangement enables students to learn various estimation techniques in an efficient way. Virtually all the chapters include empirical applications drawn from labor economics, industrial organization, domestic and international finance, and macroeconomics. These empirical exercises provide students with hands-on experience applying the techniques covered. The exposition is rigorous yet accessible, requiring a working knowledge of very basic linear algebra and probability theory. All the results are stated as propositions so that students can see the points of the discussion and also the conditions under which those results hold. Most propositions are proved in the text.For students who intend to write a thesis on applied topics, the empirical applications in Econometrics are an excellent way to learn how to conduct empirical research. For theoretically inclined students, the no-compromise treatment of basic techniques is an ideal preparation for more advanced theory courses.

Autor

Fumio Hayashi is Professor of Economics at the University of Tokyo, where he teaches macroeconomics and econometrics. Previously, he has taught at the University of Pennsylvania and at Columbia University. He is the author of Understanding Saving: Evidence from the United States and Japan.

Produkt-Details

• Erscheint lt. Verlag: 12.12.2011
• Verlagsort: Princeton
• Sprache: englisch
• Themenwelt: Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie
• Schlagworte: absolute value • Addition • Almost surely • Asymptotic Distribution • Augmented Dickey–Fuller test • autocorrelation • autocovariance • Autoregressive model • Bayesian information criterion • Big O notation • Block Matrix • Calculation • Chow Test • coefficient • Cointegration • combination • Computation • conditional expectation • Consistent estimator • Convergence of random variables • Covariance matrix • Cramér–Rao bound • Critical Value • data set • Degrees of freedom (statistics) • Delta method • Derivative • Determinant • Dummy variable (statistics) • Endogeneity (econometrics) • Equation • Errors and residuals • Error Term • estimation • Estimator • existential quantification • expected value • Finite difference • Forecast Error • Generalized method of moments • Independent and identically distributed random variables • inference • instrumental variable • Inverse function • Invertible matrix • Joint probability distribution • Kronecker Product • law of large numbers • Least Squares • Likelihood Function • Likelihood-ratio test • Linear combination • Linear Function • Linearity • linear regression • Log-linear Model • Loss Function • Martingale difference sequence • Martingale (probability theory) • Matrix (mathematics) • maximum likelihood estimation • Mean squared error • Moment (mathematics) • Multicollinearity • multiplication • Multivariate normal distribution • Non-linear least squares • Normal distribution • Nuisance parameter • null hypothesis • Observational error • orthogonality • Parameter • partial derivative • Point Estimation • Positive-definite matrix • Probability • Purchasing Power Parity • Quasi-maximum likelihood estimate • Random Variable • Regression Analysis • Regression model • sampling error • scientific notation • Special case • square root • standard deviation • standard error • stationary process • Statistic • Statistical hypothesis testing • Subset • Summation • test statistic • Theorem • Time Series • unit root • Variable (mathematics) • Variance • Without loss of generality
• ISBN-10: 1-4008-2383-8 / 1400823838
• ISBN-13: 978-1-4008-2383-3 / 9781400823833