INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 4e illustrates how empirical researchers think about and apply econometric methods in real-world practice. The text's unique approach reflects the fact that undergraduate econometrics has moved beyond just a set of abstract tools to being genuinely useful for answering questions in business, policy evaluation, and forecasting environments. The systematic approach, which reduces clutter by introducing assumptions only as they are needed, makes absorbing the material easier and leads to better econometric practices. Its unique organization separates topics by the kinds of data being analyzed, leading to an appreciation for the important issues that arise in drawing conclusions from the different kinds of data economists use. Packed with relevant applications, INTRODUCTORY ECONOMETRICS offers a wealth of interesting data sets that can be used to reproduce the examples in the text or as the starting point for original research projects.
"The most important strength of this text is its close connection (in terms of both style/structure and content) to the way empirical research is actually conducted. In particular, the emphasis on whether or not an estimated relationship can be considered causal is central to econometric practice today, and this book, unlike many others, makes that clear. The book is very comprehensive, with a wide range of topics that other introductory econometrics books do not always include but that are very common in research (e.g. panel data)."
| The Nature of Econometrics and Economic Data | |
| Regression Analysis With Cross-Sectional Data | |
| The Simple Regression Model | |
| Multiple Regression Analysis: Estimation | |
| Multiple Regression Analysis: Inference | |
| Multiple Regression Analysis: OLS Asymptotics | |
| Multiple Regression Analysis: Further Issues | |
| Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables | |
| Heteroskedasticity | |
| More on Specification and Data Problems | |
| Regression Analysis With Time Series Data | |
| Basic Regression Analysis with Time Series Data | |
| Further Issues in Using OLS with Time Series Data | |
| Serial Correlation and Heteroskedasticity in Time Series Regressions | |
| Advanced Topics | |
| Pooling Cross Sections across Time: Simple Panel Data Methods | |
| Advanced Panel Data Methods | |
| Instrumental Variables Estimation and Two Stage Least Squares | |
| Simultaneous Equations Models | |
| Limited Dependent Variable Models and Sample Selection Corrections | |
| Advanced Time Series Topics | |
| Carrying out an Empirical Project | |
| Appendices | |
| Basic Mathematical Tools | |
| Fundamentals of Probability | |
| Fundamentals of Mathematical Statistics | |
| Summary of Matrix Algebra | |
| The Linear Regression Model in Matrix Form | |
| Answers to Chapter Questions | |
| Statistical Tables | |
| References | |
| Glossary | |
| Index | |
| Table of Contents provided by Publisher. All Rights Reserved. |
ISBN: 9780324581621
ISBN-10: 0324581629
Audience:
Tertiary; University or College
Format:
Hardcover
Language:
English
Number Of Pages: 888
Published: 1st April 2008
Dimensions (cm): 24.2 x 19.1
x 3.8
Weight (kg): 1.497