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Cover

Introduction to Econometrics

Fourth Edition

Christopher Dougherty

Publication Date - April 2011

ISBN: 9780199567089

592 pages
Paperback
9.7 x 7.4 inches

In Stock

Retail Price to Students: $72.95

Taking a modern approach to the subject, this text provides students with a solid grounding in econometrics, using non-technical language wherever possible

Description

Retaining the student-friendly approach of previous editions, Introduction to Econometrics, Fourth Edition, uses clear and simple mathematics notation and step-by step explanations of mathematical proofs to help students thoroughly grasp the subject. Extensive practical exercises throughout--including fifty exercises on the same dataset--build students' confidence and provide them with hands-on practice in applying techniques.

NEW TO THE FOURTH EDITION:

* An expanded review section at the beginning of the book offers a more comprehensive guide to all of the statistical concepts needed to study econometrics

* Additional exercises provide students with even more opportunities to put theory into practice

* More Monte Carlo simulations help students use visualization to understand the math

* New final sections at the end of each chapter contain summaries and non-technical introductions to more advanced topics

An updated and expanded Companion Website contains resources for students and instructors:

For students:

* Data sets
* Gretl, a free econometrics software application
* PowerPoint-based slides with explanations
* A study guide

For instructors:

* Instructor manuals for the text and data sets that detail the exercises and their solutions
* PowerPoint-based slides
* A "Contact the Author" link

About the Author(s)

Dr. Christopher Dougherty is a Senior Lecturer in Economics at the London School of Economics.

Previous Publication Date(s)

November 2006
February 2002
April 1992

Table of Contents

    Review: Random variables, sampling, and estimation
    1. Simple regression analysis
    2. Properties of regression coefficients and hypothesis testing
    3. Multiple regression analysis
    4. Transformation of variables
    5. Dummy variables
    6. Specification regression variables: a preliminary skirmish
    7. Heteroscedasticity
    8. Stochastic regressors and measurement errors
    9. Simultaneous equations estimation
    10. Binary choice models and maximum likelihood estimation
    11. Models using time series data
    12. Autocorrelation
    13. Introduction to nonstationary time series
    14. Introduction to panel data models

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