Introduction to Econometric Theory
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A01=A. Ronald Gallant
Accuracy and precision
Almost surely
Asymptotic distribution
Author_A. Ronald Gallant
Bayes estimator
Bayes' theorem
Bias of an estimator
Binomial distribution
Cartesian product
Category=KCA
Category=KCH
Central limit theorem
Central moment
Change of variables
Chaos theory
Characteristic function (probability theory)
Chi-squared distribution
Chi-squared test
Commutative property
Conditional expectation
Confidence interval
Conjugate prior
Convergence of random variables
Correlation and dependence
Degrees of freedom (statistics)
Density estimation
Empirical distribution function
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Estimation
Estimation theory
Estimator
Fair coin
Generalized method of moments
Hoeffding's inequality
Hypergeometric distribution
Independence (probability theory)
Indicator function
Inference
Instrumental variable
Joint probability distribution
Likelihood-ratio test
Marginal distribution
Mathematical statistics
Maximum likelihood estimation
Method of moments (statistics)
Minimum mean square error
Minimum-variance unbiased estimator
Moment-generating function
Optimization problem
Parameter space
Power function
Probability
Probability and statistics
Probability distribution
Probability mass function
Probability space
Probability theory
Proportionality (mathematics)
Random variable
Sample space
Sampling (statistics)
Scientific notation
Score test
Slutsky's theorem
Statistic
Statistical hypothesis testing
Statistical inference
Step function
Subadditivity
Subset
Taylor's theorem
Test statistic
Variance
Wald test
Product details
- ISBN 9780691016450
- Weight: 454g
- Dimensions: 197 x 254mm
- Publication Date: 27 Jul 1997
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Hardback
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Intended primarily to prepare first-year graduate students for their ongoing work in econometrics, economic theory, and finance, this innovative book presents the fundamental concepts of theoretical econometrics, from measure-theoretic probability to statistics. A. Ronald Gallant covers these topics at an introductory level and develops the ideas to the point where they can be applied. He thereby provides the reader not only with a basic grasp of the key empirical tools but with sound intuition as well. In addition to covering the basic tools of empirical work in economics and finance, Gallant devotes particular attention to motivating ideas and presenting them as the solution to practical problems. For example, he presents correlation, regression, and conditional expectation as a means of obtaining the best approximation of one random variable by some function of another. He considers linear, polynomial, and unrestricted functions, and leads the reader to the notion of conditioning on a sigma-algebra as a means for finding the unrestricted solution. The reader thus gains an understanding of the relationships among linear, polynomial, and unrestricted solutions.
Proofs of results are presented when the proof itself aids understanding or when the proof technique has practical value. A major text-treatise by one of the leading scholars in this field, An Introduction to Econometric Theory will prove valuable not only to graduate students but also to all economists, statisticians, and finance professionals interested in the ideas and implications of theoretical econometrics.
A. Ronald Gallant is Henry A. Latané Distinguished Professor of Economics at the University of North Carolina at Chapel Hill. He is a Fellow of the Econometric Society and the American Statistical Association, and a member of the Board of Directors of the National Bureau of Economic Research and the National Institute of Statistical Science. His books include Nonlinear Statistical Models. He is coeditor of the Journal of Econometrics.
