Probability, Statistics and Econometrics provides a concise, yet rigorous, treatment of the field that is suitable for graduate students studying econometrics, very advanced undergraduate students, and researchers seeking to extend their knowledge of the trinity of fields that use quantitative data in economic decision-making. The book covers much of the groundwork for probability and inference before proceeding to core topics in econometrics. Authored by one of the leading econometricians in the field, it is a unique and valuable addition to the current repertoire of econometrics textbooks and reference books. - Synthesizes three substantial areas of research, ensuring success in a subject matter than can be challenging to newcomers - Focused and modern coverage that provides relevant examples from economics and finance - Contains some modern frontier material, including bootstrap and lasso methods not treated in similar-level books - Collects the necessary material for first semester Economics PhD students into a single text

Professor Oliver Linton (Professor of Political Economy, Trinity College, Cambridge University) has been a Co-editor of Econometric Theory since 2000, the Journal of Econometrics since 2014, was Co-Editor of Econometrics Journal from 2007-14. He is an Elected Fellow of the Econometric Society, the Institute of Mathematical Statistics, and the British Academy. He has published over 130 articles in statistics, econometrics, and in empirical finance. He is particularly interested in nonparametric and semiparametric methods and financial econometrics.

Probability, Statistics and Econometrics provides a concise, yet rigorous, treatment of the field that is suitable for graduate students studying econometrics, very advanced undergraduate students, and researchers seeking to extend their knowledge of the trinity of fields that use quantitative data in economic decision-making.

The book covers much of the groundwork for probability and inference before proceeding to core topics in econometrics. Authored by one of the leading econometricians in the field, it is a unique and valuable addition to the current repertoire of econometrics textbooks and reference books.

• Synthesizes three substantial areas of research, ensuring success in a subject matter than can be challenging to newcomers
• Focused and modern coverage that provides relevant examples from economics and finance
• Contains some modern frontier material, including bootstrap and lasso methods not treated in similar-level books
• Collects the necessary material for first semester Economics PhD students into a single text

Part I: Probability and Distribution 1. Probability Theory 2. Conditional Probability and Independence 3. Random Variables, Distribution Functions, and Densities 4. Transformations of Random Variables 5. The Expectation 6. Examples of Univariate Distributions 7. Multivariate Random Variables 8. Asymptotic Theory 9. Exercises and Complements

Part II: Statistics 10. Introduction 11. Estimation Theory 12. Hypothesis Testing 13. Confidence Intervals and Sets 14. Asymptotic Tests and the Bootstrap 15. Exercises and Complements

Part III: Econometrics 16. Linear Algebra 17. The Least Squares Procedure 18. Linear Model 19. Statistical Properties of the OLS Estimator 20. Hypothesis Testing for Linear Regression 21. Omission of Relevant Variables, Inclusion of Irrelevant Variables, and Model Selection 22. Asymptotic Properties of OLS Estimator and Test Statistics 23. Generalized Method of Moments and Extremum Estimators 24. A Nonparametric Postscript 25. A Case Study 26. Exercises and Complements

Appendix A. Some Results from Calculus Appendix B. Some Matrix Facts