Moss Mathematical Statistics for Applied Econometrics

1. Auflage 2014
ISBN: 978-1-4665-9410-4
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 363 Seiten

An Introductory Econometrics Text

Mathematical Statistics for Applied Econometrics covers the basics of statistical inference in support of a subsequent course on classical econometrics. The book shows students how mathematical statistics concepts form the basis of econometric formulations. It also helps them think about statistics as more than a toolbox of techniques.

Uses Computer Systems to Simplify Computation

The text explores the unifying themes involved in quantifying sample information to make inferences. After developing the necessary probability theory, it presents the concepts of estimation, such as convergence, point estimators, confidence intervals, and hypothesis tests. The text then shifts from a general development of mathematical statistics to focus on applications particularly popular in economics. It delves into matrix analysis, linear models, and nonlinear econometric techniques.

Students Understand the Reasons for the Results

Avoiding a cookbook approach to econometrics, this textbook develops students’ theoretical understanding of statistical tools and econometric applications. It provides them with the foundation for further econometric studies.

Zielgruppe

Senior undergraduate and graduate students in economics; researchers in economics and statistics.

Autoren/Hrsg.

Moss, Charles B

Fachgebiete

• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines

Inhaltsverzeichnis

DEFINING RANDOM VARIABLES
Introduction to Statistics, Probability and Econometrics
Relating Mathematical Statistics and Economics
Basics of Probability

Random Variables and Probability Distributions
Uniform Probability Measure
Random Variables and Distributions
Basic Concept of Random Variables
Univariate Continuous Random Variables
Some Common Univariate Distribution Functions
Multivariate Random Variables
Change of Variables
Derivation of the Normal Distribution Function
An Applied Sabbatical

Moments and Moment Generating Functions
Expected Values
Moments
Covariance and Correlation
Conditional Mean and Variance
Moment Generating Functions

Binomial and Normal Random Variables
Bernoulli Random Variables
Binomial Random Variables
Univariate Normal Distribution
Linking the Normal Distribution to the Binomial
Bivariate and Multivariate Normal Random Variables

ESTIMATION
Large Sample Theory
Basic Sample Theory
Modes of Convergence
Laws of Large Numbers
Asymptotic Normality
Characteristic Functions
Wrapping Up Loose Ends

Point Estimation
What Is an Estimator?
Mean Squared Error
Sufficient Statistics
Concentrated Likelihood Functions
Normal Equations
Properties of Maximum Likelihood Estimators

Interval Estimation
Confidence Intervals
Bayesian Estimation
Bayesian Confidence Intervals

Testing Hypothesis
Type I and Type II Errors
Neyman-Pearson Lemma
Simple Tests against a Composite
Composite against a Composite
Testing Hypothesis about Vectors

ECONOMETRIC APPLICATIONS
Elements of Matrix Analysis
Review of Elementary Matrix Algebra
Projection Matrices
Idempotent Matrices
Eigenvalues and Eigenvectors
Kronecker Products

Regression Applications in Econometrics
Simple Linear Regression
Multivariate Regression
Linear Restrictions
Exceptions to Ordinary Least Squares

Notes

Bibliography

Index