"An introductory PhD-level textbook for one of the first and most foundational courses every economics graduate student must take"-- Provided by publisher

• Preface Introduction
• 1 Uncertainty
• 2 Regression
• 3 Regularization
• 4 Classification
• 5 Experiments
• 6 Controls
• 7 Factorization
• 8 Text as Data
• 9 Nonparametrics
• 10 Artificial Intelligence Bibliography Index.
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• Part I: The Linear Regression Model
• Chapter 1: Econometrics
• Chapter 2: The Linear Regression Model
• Chapter 3: Least Squares
• Chapter 4: The Least Squares Estimator
• Chapter 5: Hypothesis Tests and Model Selection
• Chapter 6: Functional Form and Structural Change
• Chapter 7: Nonlinear, Semiparametric, and Nonparametric Regression Models
• Chapter 8: Endogeneity and Instrumental Variable Estimation Part II: Generalized Regression Model and Equation Systems
• Chapter 9: The Generalized Regression Model and Heteroscedasticity
• Chapter 10: Systems of Equations
• Chapter 11: Models for Panel Data Part III: Estimation Methodology
• Chapter 12: Estimation Frameworks in Econometrics
• Chapter 13: Minimum Distance Estimation and the Generalized Method of Moments
• Chapter 14: Maximum Likelihood Estimation
• Chapter 15: Simulation-Based Estimation and Inference
• Chapter 16: Bayesian Estimation and Inference Part IV: Cross Sections, Panel Data, and Microeconometrics
• Chapter 17: Discrete Choice
• Chapter 18: Discrete Choices and Event Counts
• Chapter 19: Limited Dependent Variables--Truncation, Censoring, and Sample Selection Part V: Time Series and Macroeconometrics
• Chapter 20: Serial Correlation
• Chapter 21: Models with Lagged Variables
• Chapter 22: Time-Series Models
• Chapter 23: Nonstationary Data Part VI: Appendices Appendix A: Matrix Algebra Appendix B: Probability and Distribution Theory Appendix C: Estimation and Inference Appendix D: Large-Sample Distribution Theory Appendix E: Computation and Optimization Appendix F: Data Sets Used in Applications Appendix G: Statistical Tables.
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Econometric Analysis serves as a bridge between an introduction to the field of econometrics and the professional literature for social scientists and other professionals in the field of social sciences, focusing on applied econometrics and theoretical background. This book provides a broad survey of the field of econometrics that allows the reader to move from here to practice in one or more specialized areas. At the same time, the reader will gain an appreciation of the common foundation of all the fields presented and use the tools they employ.
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• Ch. 1. The nature of econometrics and economic data
• pt. 1. Regression analysis with cross-sectional data
• Ch. 2. The simple regression model
• Ch. 3. Mutiple regression analysis: estimation
• Ch. 4. Mutiple regression analysis: inference
• Ch. 5. Mutiple regression analysis: OLS asymptotics
• Ch. 6. Mutiple regression analysis: further issues
• Ch. 7. Mutiple regression analysis with qualitative information: binary (or dummy) variables
• Ch. 8. Hetroskedasticity
• Ch. 9. More on specification and data issues
• pt. 2. Regression analysis with time series data
• Ch. 10. Basic regression analysis with time series data
• Ch. 11. Further issues in using OLS with time series data
• Ch. 12. Serial correlation and heteroskedasticity in time series regressions
• Ch. 13. Pooling cross sections across time: simple panel data methods
• Ch. 14. Advanced panel data methods
• Ch. 15. Instrumental variables estimation and two stage least squares
• Ch. 16. Simulatensous equations models
• Ch. 17. Limited dependent variable models and sample selection corrections
• Ch. 18. Advanced time series topics
• Ch. 19. Carrying out an empirical project
• Appendices.

Discover how empirical researchers today actually think about and apply econometric methods with the practical, professional approach in Wooldridge's INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 5E. Unlike traditional books on the subject, INTRODUCTORY ECONOMETRICS' unique presentation demonstrates how econometrics has moved beyond just a set of abstract tools to become a genuinely useful tool for answering questions in business, policy evaluation, and forecasting environments. Organized around the type of data being analyzed, the book uses a systematic approach that only introduces assumptions as they are needed, which makes the material easier to understand and ultimately leads to better econometric practices. Packed with timely, relevant applications, the text emphasizes incorporates close to 100 intriguing data sets in six formats and offers updates that reflect the latest emerging developments in the field.
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• List of Figures vii List of Tables ix Preface xi Acknowledgments xv Organization of This Book xvii
• PART I: PRELIMINARIES 1 Chapter 1: Questions about Questions 3 Chapter 2: The Experimental Ideal 11 2.1 The Selection Problem 12 2.2 Random Assignment Solves the Selection Problem 15 2.3 Regression Analysis of Experiments 22
• PART II: THE CORE 25 Chapter 3: Making Regression Make Sense 27 3.1 Regression Fundamentals 28 3.2 Regression and Causality 51 3.3 Heterogeneity and Nonlinearity 68 3.4 Regression Details 91 3.5 Appendix: Derivation of the Average Derivative Weighting Function 110
• Chapter 4: Instrumental Variables in Action: Sometimes You Get What You Need 113 4.1 IV and Causality 115 4.2 Asymptotic 2SLS Inference 138 4.3 Two-Sample IV and Split-Sample IV 147 4.4 IV with Heterogeneous Potential Outcomes 150 4.5 Generalizing LATE 173 4.6 IV Details 188 4.7 Appendix 216
• Chapter 5: Parallel Worlds: Fixed Effects, Differences-in-Differences, and Panel Data 221 5.1 Individual Fixed Effects 221 5.2 Differences-in-Differences 227 5.3 Fixed Effects versus Lagged Dependent Variables 243 5.4 Appendix: More on Fixed Effects and Lagged Dependent Variables 246
• PART III: EXTENSIONS 249 Chapter 6: Getting a Little Jumpy: Regression Discontinuity Designs 251 6.1 Sharp RD 251 6.2 Fuzzy RD Is IV 259
• Chapter 7: Quantile Regression 269 7.1 The Quantile Regression Model 270 7.2 IV Estimation of Quantile Treatment Effects 283
• Chapter 8: Nonstandard Standard Error Issues 293 8.1 The Bias of Robust Standard Error Estimates 294 8.2 Clustering and Serial Correlation in Panels 308 8.3 Appendix: Derivation of the Simple Moulton Factor 323
• Last Words 327 Acronyms and Abbreviations 329 Empirical Studies Index 335 References 339 Index 361.
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The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages?"Mostly Harmless Econometrics" shows how the basic tools of applied econometrics allow the data to speak. In addition to econometric essentials, "Mostly Harmless Econometrics" covers important new extensions - regression-discontinuity designs and quantile regression - as well as how to get standard errors right. Joshua Angrist and Jorn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science. This book features: an irreverent review of econometric essentials; focus on tools that applied researchers use most; chapters on regression-discontinuity designs, quantile regression, and standard errors; many empirical examples; and, a clear and concise resource with wide applications.
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This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.
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• Preface 1. Introduction 1.1 What Is Probability? 1.2 What Is Statistics?
• 2. Probability 2.1 Introduction 2.2 Axioms of Probability 2.3 Counting Techniques 2.4 Conditional Probability and Independence 2.5 Probability Calculations Exercises 3. Random Variables And Probability Distributions 3.1 Definitions of a Random Variable 3.2 Discrete Random Variables 3.3 Univariate Continuous Random Variables 3.4 Bivariate Continuous Random Variables 3.5 Distribution Function 3.6 Change of Variables 3.7 Joint Distribution of Discrete and Continuous Random Variables Exercises 4. Moments 4.1 Expected Value 4.2 Higher Moments 4.3 Covariance and Correlation 4.4 Conditional Mean and Variance Exercises 5. Binomial And Normal Random Variables 5.1 Binomial Random Variables 5.2 Normal Random Variables 5.3 Bivariate Normal Random Variables 5.4 Multivariate Normal Random Variables Exercises 6. Large Sample Theory 6.1 Modes of Convergence 6.2 Laws of Large Numbers and Central Limit Theorems 6.3 Normal Approximation of Binomial 6.4 Examples Exercises 7. Point Estimation 7.1 What Is an Estimator? 7.2 Properties of Estimators 7.3 Maximum Likelihood Estimator: Definition and Computation 7.4 Maximum Likelihood Estimator: Properties Exercises 8. Interval Estimation 8.1 Introduction 8.2 Confidence Intervals 8.3 Bayesian Method Exercises 9. Tests Of Hypotheses 9.1 Introduction 9.2 Type I and Type II Errors 9.3 Neyman-Pearson Lemma 9.4 Simple against Composite 9.5 Composite against Composite 9.6 Examples of Hypothesis Tests 9.7 Testing about a Vector Parameter Exercises 10. Bivariate Regression Model 10.1 Introduction 10.2 Least Squares Estimators 10.3 Tests of Hypotheses Exercises 11. Elements Of Matrix Analysis 11.1 Definition of Basic Terms 11.2 Matrix Operations 11.3 Determinants and Inverses 11.4 Simultaneous Linear Equations 11.5 Properties of the Symmetric Matrix Exercises 12. Multiple Regression Model 12.1 Introduction 12.2 Least Squares Estimators 12.3 Constrained Least Squares Estimators 12.4 Tests of Hypotheses 12.5 Selection of Regressors Exercises 13. Econometric Models 13.1.
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Turning to regression, Amemiya presents the classical bivariate model in the conventional summation notation. He follows with a brief introduction to matrix analysis and multiple regression in matrix notation. Finally, he describes various generalizations of the classical regression model and certain other statistical models extensively used in econometrics and other applications in social science.
(source: Nielsen Book Data)