Econometrics For Dummies
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Comment from the Stata technical group
Econometrics for Dummies is an ideal companion for an introductory course in econometrics. The book is written for people that want to learn how to use econometrics in their research and complements the discussion of theory with advice about how to move from data and economic theory to estimation. All the computational examples and output in the book use Stata. The book assumes some previous knowledge of statistics and economics but does offer a comprehensive review of the basic concepts needed to understand the concepts in the text.
The first part of the book is a review of basic statistics and probability, an introduction to Stata, and a discussion of the different types of data commonly encountered by researchers. The book then delves into the ordinary least-squares and the Gauss-Markov theorems. After presenting the Gauss-Markov theorem the author discusses the most common violations of the assumptions of the theorem — heteroskedasticity, collinearity, and autocorrelation — and how to diagnose and deal with them. The book also discusses binary outcome models, models for censored and truncated outcomes, sample selection, time-series models, and panel-data models.
Econometrics for Dummies presents theoretical econometric results and provides an intuitive interpretation of them. The book is a good reference for those wanting to get an insight into basic econometric concepts encountered in an introductory econometrics course.
Table of contents
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Part I: Getting Started with Econometrics
Chapter 1: Econometrics: The Economist's Approach to Statistical Analysis
Chapter 2: Getting the Hang of Probability
Chapter 3: Making Inferences and Testing Hypotheses
Part II: Building the Classical Linear Regression Model
Chapter 4: Understanding the Objectives of Regression Analysis
Chapter 5: Going Beyond Ordinary with the Ordinary Least Squares Technique
Chapter 6: Assumptions of OLS Estimation and the Gauss-Markov Theorem
Chapter 7: The Normality Assumption and Inference with OLS
Part III: Working with the Classical Regression Model
Chapter 8: Functional Form, Specification, and Structural Stability
Chapter 9: Regression with Dummy Explanatory Variables
Part IV: Violations of Classical Regression Model Assumptions
Chapter 10: Multicollinearity
Chapter 11: Heteroskedasticity
Chapter 12: Autocorrelation
Part V: Discrete and Restricted Dependent Variables in Econometrics
Chapter 13: Qualitative Dependent Variables
Chapter 14: Limited Dependent Variable Models
Part VI: Extending the Basic Econometric Model
Chapter 15: Static and Dynamic Models
Chapter 16: Diving into Pooled Cross-Section Analysis
Chapter 17: Panel Econometrics
Part VII: The Part of Tens
Chapter 18: Ten Components of a Good Econometrics Research Project
Chapter 19: Ten Common Mistakes in Applied Econometrics
Appendix: Statistical Tables