Econometrics For Dummies

Learn to: Grasp the techniques and applications of econometrics Make sense of complex concepts and material Use specialized software to apply your acquired econometrics skills Understand econometric theory and apply econometric techniques Econometrics can prove challenging for many students. Thankfully, Econometrics For Dummies eliminates that confusion with easy-to-understand explanations of important topics in the study of econometrics. This hands-on, friendly guide breaks down the complex subject of econometrics and provides you with an easy-to-follow course supplement to further refine your understanding of how econometrics works and how it can be applied in real-world situations. The nuts and bolts — get familiar with the various characteristics of the classical linear regression model (CLRM), which is the foundation of traditional econometric analysis Form and function — discover how econometric analysis is able to accommodate a considerable amount of flexibility with different mathematical functions and types of data Don't assume — find out how econometricians use some bizarre titles to identify problems that violate CLRM assumptions Be discrete — get to know how discrete and restricted dependent variables can create problems for traditional regression analysis and discover the techniques econometricians have developed to modify traditional regression analysis Make special note — delve deeper into a few specialized areas of econometrics, namely time-series analysis, pooled cross sections, and panel econometrics Open the book and find: Plain-English explanations of complex material Computer outputs from STATA for many of the examples The economist's approach to statistical analysis The various characteristics of the classical linear regression model The most common problems encountered when performing regression analysis Lots of helpful figures Common mistakes in applied econometrics

Introduction 1 Part I: Getting Started with Econometrics 5 Chapter 1: Econometrics: The Economist’s Approach to Statistical Analysis 7 Chapter 2: Getting the Hang of Probability 21 Chapter 3: Making Inferences and Testing Hypotheses 39 Part II: Building the Classical Linear Regression Model 59 Chapter 4: Understanding the Objectives of Regression Analysis 61 Chapter 5: Going Beyond Ordinary with the Ordinary Least Squares Technique 75 Chapter 6: Assumptions of OLS Estimation and the Gauss–Markov Theorem 93 Chapter 7: The Normality Assumption and Inference with OLS 111 Part III: Working with the Classical Regression Model 135 Chapter 8: Functional Form, Specifi cation, and Structural Stability 137 Chapter 9: Regression with Dummy Explanatory Variables 153 Part IV: Violations of Classical Regression Model Assumptions 173 Chapter 10: Multicollinearity 175 Chapter 11: Heteroskedasticity 191 Chapter 12: Autocorrelation 209 Part V: Discrete and Restricted Dependent Variables in Econometrics 229 Chapter 13: Qualitative Dependent Variables 231 Chapter 14: Limited Dependent Variable Models 253 Part VI: Extending the Basic Econometric Model 265 Chapter 15: Static and Dynamic Models 267 Chapter 16: Diving into Pooled Cross–Section Analysis 281 Chapter 17: Panel Econometrics 291 Part VII: The Part of Tens 305 Chapter 18: Ten Components of a Good Econometrics Research Project 307 Chapter 19: Ten Common Mistakes in Applied Econometrics 315 Appendix: Statistical Tables 321 Index 327

Roberto Pedace, PhD, is an associate professor in the Department of Economics at Scripps College. His published work has appeared in Economic Inquiry, Industrial Relations, the Southern Economic Journal , Contemporary Economic Policy , the Journal of Sports Economics , and other outlets.