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A Guide to Modern Econometrics

by Marno Verbeek
John Wiley and Sons, 2001

List Price $70.00, Estima's Price $58.00

In his Preface, Verbeek notes that modern software tools have made it relatively easy for researchers to apply the latest econometrics techniques, without necessarily understanding them well enough to be aware of potential problems inherent in these techniques. He notes that a gap exists between introductory econometrics texts, which tend not cover these techniques at all (or only deal with idealized cases), and more advanced texts, which are often too advanced to be useful to many practitioners. This book is designed to help fill that gap.

According to Verbeek, "the goal of this book is to familiarize the reader with a wide range of topics in modern econometrics, focusing on what is important for doing and understanding empirical work. This means that the text is a guide to (rather than an overview of) alternative techniques. Consequently, it does not concentrate on the formulae behind each technique (although the necessary ones are given) nor on formal proofs, but on the intuition behind the approaches and their practical relevance. This book covers a wide range of topics that is usually not found in textbooks at this level. In particular, attention is paid to cointegration, the generalized method of moments, models with limited dependent variables and panel data models. As a result, the book discusses developments in time series analysis, cross-sectional methods as well as panel data modelling. Throughout, a few dozen full-scale empirical examples and illustrations are provided, taken from fields like labour economics, finance, international economics, consumer behavior, environmental economics and macro-economics. In addition, a number of exercises are of an empirical nature and require the use of actual data."

Contents

  1. An Introduction to Linear Regression
  2. Interpreting and Comparing Regression Models
  3. Heteroskedasticity and Autocorrelation
  4. Stochastic Regressors
  5. Maximum Likelihood Estimation
  6. Models with Limited Dependent Variables
  7. Univariate Time Series Modelling
  8. Multivariate Time Series Analysis
  9. Models Based on Panel Data

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