Introductory Econometrics: A Modern Approach is an essential resource for anyone seeking a comprehensive understanding of econometrics. The book takes a practical approach, emphasizing how econometrics is used to answer real-world questions in business, policy evaluation, and forecasting. Unlike traditional econometrics texts, this book focuses on the application of econometrics across various disciplines, demonstrating how it has evolved from abstract theory to a powerful tool for answering important questions. The book is organized around the type of data being analyzed, making the material more approachable. It introduces assumptions gradually, ensuring that students understand the reasoning behind them. This systematic approach enhances comprehension and ultimately leads to better econometric practices. Whether you are studying for an exam or applying econometrics to real-world problems, this book offers both the depth and practical perspective necessary for mastering the subject. Its blend of theory, application, and up-to-date research makes it an indispensable guide for students and professionals alike. This textbook presents econometrics as a powerful set of data-analytic techniques central to empirical research in economics and related disciplines. Designed with clarity and accessibility in mind, the book integrates theoretical foundations with practical applications to help undergraduate and postgraduate students grasp both the conceptual and computational aspects of econometrics. The text covers a broad spectrum of econometric techniques applicable to cross-sectional, time series, and panel data. Beginning with the classical linear regression model, it systematically introduces extensions that relax standard assumptions, handle qualitative variables, and explore issues of model specification and estimation.
Dennis Becker is professor of Econometrics and Data Science at the Econometric Institute of the Erasmus School of Economics, Erasmus University Rotterdam. He obtained his PhD in 2003 on advanced econometric marketing models. He combines state-of-the-art quantitative techniques to relevant marketing topics. His research interests include applied econometrics in general, with a particular focus on combining new econometric methods with marketing questions. More specifically, he focuses on modelling unobserved heterogeneity, non-linear models, Bayesian statistics, and modeling consumer-level decisions. Recently, his research interests also concern models for high-dimensional data. One example is the development of methods and efficient estimation techniques for modeling choices from a very large set of alternatives. His work has been published in the top marketing journals such as Journal of Marketing Research, Marketing Science, International Journal of Research in Marketing and in top econometrics journals such as Journal of Econometrics, Journal of Applied Econometrics, and Journal of Business and Economic Statistics.
Preface 1. INTRODUCTION, DEFINITION AND SCOPE OF ECONOMETRICS....................................................................1 1.1 Introduction 1.2 Definition And Scope of Econometrics 1.3 Methodology of Econometric Research 1.4 Basic Concepts of Estimation 1.5 Properties of Estimators 1.6 Characteristics of Estimators 1.7 Unbiasedness 1.8 Consistency 1.9 Efficiency 2. THE ECONOMETRIC APPROACH.........................................................31 2.1 Introduction 2.2 The Econometric Approaches or Methods 2.3 Elements of Statistical Inferences 2.4 Test of Hypothesis 3. MULTIPLE REGRESSION ANALYSIS: ESTIMATION............................90 3.1 Motivation For Multiple Regression 3.2 Mechanics And Interpretation of Ordinary Least Squares 3.3 The Expected Value of The Ols Estimators 3.4 Summary 4. MULTIPLE REGRESSION ANALYSIS: INFERENCE .............................................................................................115 4.1 Sampling Distributions of The OLS Estimators 4.2 Testing Hypotheses About A Single Population Parameter: The T Test 4.3 Testing Hypotheses About A Single Linear Combination of The Parameters 4.4 Testing Multiple Linear Restrictions: The F Test 4.5 Reporting Regression Results 4.6 Revisiting Causal Effects And Policy Analysis 4.7 Summary 5. DISTRIBUTIVE LAG MODELS...............................................................149 5.1 Structured Estimation 5.2 Estimation of Distribution Log Models Koyck’s Approach 5.3 Simultaneous Equations Methods 6. PANEL DATA.............................................................................................165 6.1 Introduction – What Are Panel Techniques And Why Are They Used? 6.2 What Panel Techniques Are Available? 6.3 The Fixed Effects Model 6.4 Time-Fixed Effects Models 6.5 Investigating Banking Competition Using A Fixed Effects Model 6.6 The Random Effects Model 6.7 Panel Data Application To Credit Stability of Banks In Central And Eastern Europe 6.8 Panel Data With Eviews 7. FURTHER ISSUES IN USING OLS WITH TIME SERIES DATA..................................................................................186 7.1 Stationary And Weakly Dependent Time Series 7.2 Asymptotic Properties of OLS 7.3 Using Highly Persistent Time Series In Regression Analysis 8. POOLING CROSS SECTIONS ACROSS TIME.....................................205 8.1 Pooling Independent Cross Sections Across Time 8.2 Policy Analysis With Pooled Cross Sections 8.3 Two-Period Panel Data Analysis 8.4 Policy Analysis With Two-Period Panel Data 8.5 Differencing With More Than Two Time Periods 8.6 Summary 9. INSTRUMENTAL VARIABLES ESTIMATION AND TWO STAGE LEAST SQUARES ....................................................................................242 9.1 Motivation: Omitted Variables In A Simple Regression Model 9.2 IV Estimation of The Multiple Regression Model 9.3 Two Stage Least Squares 9.4 Summary 10. CARRYING OUT AN EMPIRICAL PROJECT .....................................266 10.1 Posing A Question
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