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Probability Theory and Statistical Inference: Econometric Modeling with Observational Data
Abstract
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.
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Cited by
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Metadata
- Authors
- Spanos, A.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 1984-02-29
- OpenAlex
- W623761935
- Cited by
- 328
- References
- 0
- FWCI
- 0.00
- Type
- book
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Probability Theory and Statistical Inference: Econometric Modeling with Observational Data. https://app.cochat.ai/papers/10.1017/cbo9780511754081
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- Spanos, A. (1984). Probability Theory and Statistical Inference: Econometric Modeling with Observational Data. Cambridge University Press eBooks. https://doi.org/10.1017/cbo9780511754081
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.