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