This outstanding text by a foremost econometrician combines
instruction in probability and statistics with econometrics in a
rigorous but relatively nontechnical manner. Unlike many statistics
texts, it discusses regression analysis in depth. And unlike many
econometrics texts, it offers a thorough treatment of statistics.
Although its only mathematical requirement is multivariate
calculus, it challenges the student to think deeply about basic
The coverage of probability and statistics includes best
prediction and best linear prediction, the joint distribution of a
continuous and discrete random variable, large sample theory, and
the properties of the maximum likelihood estimator. Exercises at
the end of each chapter reinforce the many illustrative examples
and diagrams. Believing that students should acquire the habit of
questioning conventional statistical techniques, Takeshi Amemiya
discusses the problem of choosing estimators and compares various
criteria for ranking them. He also evaluates classical hypothesis
testing critically, giving the realistic case of testing a
composite null against a composite alternative. He frequently
adopts a Bayesian approach because it provides a useful pedagogical
framework for discussing many fundamental issues in statistical
Turning to regression, Amemiya presents the classical bivariate
model in the conventional summation notation. He follows with a
brief introduction to matrix analysis and multiple regression in
matrix notation. Finally, he describes various generalizations of
the classical regression model and certain other statistical models
extensively used in econometrics and other applications in social
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