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Probability Theory and Statistical Inference

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  • Spanos,Aris
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    Abstract

    This major new 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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    Bibliographic Info

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    This book is provided by Cambridge University Press in its series Cambridge Books with number 9780521424080 and published in 1999.

    Order: http://www.cambridge.org/uk/catalogue/catalogue.asp?isbn=9780521424080
    Handle: RePEc:cup:cbooks:9780521424080

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    Web page: http://www.cambridge.org

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    Cited by:
    1. Sarabia, José María & Guillén, Montserrat, 2008. "Joint modelling of the total amount and the number of claims by conditionals," Insurance: Mathematics and Economics, Elsevier, vol. 43(3), pages 466-473, December.
    2. Tunaru, Radu & Clark, Ephraim & Viney, Howard, 2005. "An option pricing framework for valuation of football players," Review of Financial Economics, Elsevier, vol. 14(3-4), pages 281-295.
    3. Spanos, Aris, 2008. "The 'Pre-Eminence of Theory' versus the 'General-to-Specific' Cointegrated VAR Perspectives in Macro-Econometric Modeling," Economics Discussion Papers 2008-25, Kiel Institute for the World Economy.
    4. Bergtold, Jason S. & Spanos, Aris, 2005. "Bernoulli Regression Models: Re-examining Statistical Models with Binary Dependent Variables," 2005 Annual meeting, July 24-27, Providence, RI 19282, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. McGuirk, Anya M. & Spanos, Aris, 2004. "Revisiting Error Autocorrelation Correction: Common Factor Restrictions And Granger Causality," 2004 Annual meeting, August 1-4, Denver, CO 20176, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. Dominique Guegan & Philippe De Peretti, 2012. "An Omnibus Test to Detect Time-Heterogeneity in Time Series," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00721327, HAL.
    7. Aris Spanos, 2006. "Revisiting the omitted variables argument: Substantive vs. statistical adequacy," Journal of Economic Methodology, Taylor and Francis Journals, vol. 13(2), pages 179-218.
    8. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
    9. Konstantinos Eleftheriou, 2008. "Matching, Specialties and Wage Inequality," Economics Bulletin, AccessEcon, vol. 10(11), pages 1-12.
    10. McGuirk, Anya M. & Spanos, Aris, 2002. "The Linear Regression Model With Autocorrelated Errors: Just Say No To Error Autocorrelation," 2002 Annual meeting, July 28-31, Long Beach, CA 19905, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    11. Dominique Guegan & Philippe De Peretti, 2012. "An Omnibus Test to Detect Time-Heterogeneity in Time Series," Working Papers halshs-00721327, HAL.

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