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Probability Theory and Statistical Inference
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  • 136 tables
  • Page extent: 844 pages
  • Size: 247 x 174 mm
  • Weight: 1.32 kg
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 (ISBN-13: 9780521424080 | ISBN-10: 0521424089)

DOI: 10.2277/0521424089

  • There was also a Hardback of this title but it is no longer available | Adobe eBook
  • Published September 1999

Manufactured on demand: supplied direct from the printer

 (Stock level updated: 02:06 GMT, 03 September 2015)


This major textbook from a distinguished econometrician is intended for students taking introductory courses in probability theory and statistical inference. No prior knowledge other than a basic familiarity with descriptive statistics is assumed. The primary objective of this book is to establish the framework for the empirical modelling of observational (non-experimental) data. This framework known as 'Probabilistic Reduction' is formulated with a view to accommodating the peculiarities of observational (as opposed to experimental) data in a unifying and logically coherent way. Probability Theory and Statistical Inference differs from traditional textbooks in so far as it emphasizes concepts, ideas, notions and procedures which are appropriate for modelling observational data. Aimed at students at second-year undergraduate level and above studying econometrics and economics, this textbook will also be useful for students in other disciplines which make extensive use of observational data, including finance, biology, sociology and psychology and climatology.

• A major new textbook with global adoption potential on a central social scientific and statistical topic • An easy to follow, up-to-date exposition including numerous examples, case studies and pathways designed to allow rigorous and intuitive study • Spanos is a leading figure in econometrics teaching and research, with a very successful track record as an author


Preface; 1. An introduction to empirical modelling; 2. Probability theory: a modelling framework; 3. The notion of a probability model; 4. The notion of a random sample; 5. Theoretical concepts and real data; 6. The notion of a non-random sample; 7. Regression and related notions; 8. Stochastic processes; 9. Limit theorems; 10. From probability theory to statistical inference; 11. An introduction to statistical inference; 12. Estimation I: properties of estimators; 13. Estimation II: methods of estimation; 14. Hypothesis testing; 15. Misspecification testing; References; Index.


'… a useful complement to a full introductory course in probabilistic foundations of econometrics.' The Times Higher Education Supplement

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