Econometric Analysis, Global Edition, 8th edition

Published by Pearson (September 13, 2019) © 2020

  • William H. Greene Stern School of Business, New York University
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£44.99
Instant access

Access details

  • Instant access once purchased
  • Fulfilled by VitalSource
  • For titles accompanied by MyLab/Mastering, this eBook does NOT include access to the platform

Features

  • Add notes and highlights
  • Search by keyword or page

Title overview

For first-year graduate courses in Econometrics for Social Scientists.

Bridging the gap between social science studies and econometric analysis

Designed to bridge the gap between social science studies and field-econometrics, Econometric Analysis, 8th Edition, Global Edition presents this ever-growing area at an accessible graduate level. The book first introduces students to basic techniques, a rich variety of models, and underlying theory that is easy to put into practice. It then presents students with a sufficient theoretical background to understand advanced techniques and to recognise new variants of established models. This focus, along with hundreds of worked numerical examples, ensures that students can apply the theory to real-world application and are prepared to be successful economists in the field

Table of contents

PART I. The Linear Regression Model

  1. 1.Econometrics
  2. 2. The Linear Regression Model
  3. 3. Least Squares
  4. 4. Estimating the Regression Model by Least Squares
  5. 5. Hypothesis Tests and Model Selection
  6. 6. Functional Form, Difference in Differences and Structural Change
  7. 7. Nonlinear, Semiparametric and Nonparametric Regression Models
  8. 8. Endogeneity and Instrumental Variable Estimation

PART II. Generalized Regression Model and Systems of Equations

  1. 9. The Generalized Regression Model and Heteroscedasticity
  2. 10. Systems of Regression Equations
  3. 11. Models for Panel Data

PART III. Estimation Methodology

  1. 12. Estimation Frameworks in Econometrics
  2. 13. Minimum Distance Estimation and the Generalized Method of Moments
  3. 14. Maximum Likelihood Estimation
  4. 15. Simulation-Based Estimation and Inference and Random Parameter Models
  5. 16. Bayesian Estimation and Inference

PART IV. Cross Sections, Panel Data and Microeconometrics

  1. 17. Binary Outcomes and Discrete Choices
  2. 18. Multinomial Choices and Event Counts
  3. 19. Limited Dependent Variables, Truncation, Censoring and Sample Selection

PART V. Time Series and Macroeconometrics

  1. 20. Serial Correlation
  2. 21. Nonstationary Data

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