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Doubt over the trustworthiness of published empirical results is not unwarranted and is often a result of statistical mis-specification: invalid probabilistic assumptions imposed on data. Now in its second edition, this bestselling textbook offers a comprehensive course in empirical research methods, teaching the probabilistic and statistical foundations that enable the specification and validation of statistical models, providing the basis for an informed implementation of statistical procedure to secure the trustworthiness of evidence. Each chapter has been thoroughly updated, accounting for developments in the field and the author's own research. The comprehensive scope of the textbook has been expanded by the addition of a new chapter on the Linear Regression and related statistical models. This new edition is now more accessible to students of disciplines beyond economics and includes more pedagogical features, with an increased number of examples as well as review questions and exercises at the end of each chapter.
List of contents
1. An introduction to empirical modeling; 2. Probability theory as a modeling framework; 3. The concept of a probability model; 4. A simple statistical model; 5. Chance regularities and probabilistic concepts; 6. Statistical models and dependence; 7. Regression models; 8. Introduction to stochastic processes; 9. Limit theorems in probability; 10. From probability theory to statistical inference; 11. Estimation I: properties of estimators; 12. Estimation II: methods of estimation; 13. Hypothesis testing; 14. Linear regression and related models; 15. Mis-specification (M-S) testing.
About the author
Aris Spanos is Wilson E. Schmidt Professor of Economics at Virginia Polytechnic Institute and State University. He is the author of Statistical Foundations of Econometric Modelling (Cambridge, 1986) and, with D. G. Mayo, Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science (Cambridge, 2010).
Summary
Doubt over the trustworthiness of published empirical results is often a result of statistical mis-specification or invalid probabilistic assumptions. This course in empirical research methods enables the specification and validation of statistical models, facilitating their informed implementation and giving rise to trustworthy evidence.
