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Aris Spanos

Aris Spanos
Wilson Schmidt Professor of Economics

Pamplin 3025
Email Dr. Spanos

Current Research Interests:

My current research interests include econometrics; the philosophy and methodology of statistical inference and modeling; the foundations of statistics; data mining, pre-test bias and other methodological issues pertaining to empirical modeling; statistical adequacy, Mis-Specification (M-S) testing and respecification; resampling techniques and statistical adequacy; parametric vs. nonparametric modeling; Bayesian criticisms of frequentist inference; reliability and precision of statistical inference and the trustworthiness of empirical evidence; modeling speculative prices; revisiting the statistical foundations of cross-section and panel data models; Dynamic Stochastic General Equilibrium (DSGE) models and their statistical adequacy; the replication crisis and untrustworthy evidence; Big Data and Data Science, Machine Learning, Statistical Learning Theroy, and Graphical Causal Modeling.

Selected Bibliography:

Books: (Linked Above)
1. Statistical Foundations of Econometric Modeling, Cambridge University Press, Cambridge, 1986.
2. Probability Theory and Statistical Inference: Econometric Modeling with Observational Data, Cambridge University Press, Cambridge, 1999.
3. Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science [with D. G. Mayo], Cambridge University Press, Cambridge, 2010.
4. Probability Theory and Statistical Inference:  Empirical Modeling with Observational Data, Cambridge University Press, Cambridge, 2019.

Selected Publicatons:
1. “The Simultaneous Equations Model Revisited: Statistical Adequacy and Identification,” Journal of Econometrics, 1990, 44, 87-105.
2. “On Modeling Heteroskedasticity: the Student’s t and Elliptical Linear Regression Models,” Econometric Theory, 1994, 10, 286-315.
3. “On Theory Testing in Econometrics: Modeling with Non-experimental Data,” Journal of Econometrics, 1995, 67, 189-226.
4. “On Normality and the Linear Regression Model”, Econometric Reviews, 1995, 14(2), 195-203.
5. “Towards a Unifying Methodological Framework for Econometric Modelling”, Economic Notes, 1988, pp. 107-34. Reprinted in Modelling Economic Series: Readings on the Methodology of Econometric Modeling, pp. 335-64, edited by C.W.J. Granger, Oxford University Press, Oxford, 1990.
6. “Early Empirical Findings on the Consumption Function, Stylized Facts or Fiction: a Retrospective View,” Oxford Economic Papers, 1989, 41, pp. 150-169.
7. “On Re-reading Haavelmo: a Retrospective View of Econometric Modeling,” Econometric Theory 1989, 5, pp. pp. 405-429.
8. “Revisiting Data Mining: ‘Hunting’ with or without a License,” Journal of Economic Methodology, 2000, 7, pp. 231-264.
9. “On Modeling Speculative Prices: the Empirical Literature”, [with Elena Andreou and Nikitas Pittis], Journal of Economic Surveys, 15, 187-220, 2001.
10. “The Model Specification Problem from a Probabilistic Reduction Perspective,” [with Anya McGuirk], Journal of the American Agricultural Association, 2001, 83(5), pp. 1168-1176.
11 “The Problem of Near-Multicollinearity Revisited: Erratic vs. Systematic Volatility”, [with Anya McGuirk], Journal of Econometrics, 2002, 108, 365-393.
12. “Statistical Adequacy and the Testing of Trend versus Difference Stationarity,” [with Elena Andreou], Econometric Reviews, 2003, 22, 217-237.
13. “Methodology in Practice: Statistical Misspecification Testing” [with D. G. Mayo], Philosophy of Science, 2004, 71, 1007-1025.
14. “Severe Testing as a Basic Concept in a Neyman-Pearson Philosophy of Induction,” [with D. G. Mayo] The British Journal of the Philosophy of Science, 2006, 57: 323-357.
15. “Where Do Statistical Models Come From? Revisiting the Problem of Specification,”
pp. 98-119, The Second Erich L. Lehmann Symposium, Lecture Notes-Monograph Series, vol. 49, Institute of Mathematical Statistics, 2006.
16. “Econometrics in Retrospect and Prospect,” pp. 3-58 in Mills, T.C. and K. Patterson, New Palgrave Handbook of Econometrics, vol. 1, MacMillan, London, 2006.
17. “The Student’s t Dynamic Linear Regression: Re-examining Volatility Modeling,” [with M. Heracleous], Advances in Econometrics, 2006, 20, 289-319.
18. “Revisiting the Omitted Variables Argument: Substantive vs. Statistical Adequacy,” Journal of Economic Methodology, 2006, 13: 179-218.
19. “The Instrumental Variables Method revisited: On the Nature and Choice of Optimal Instruments,” pp. 34-59 in Refinement of Econometric Estimation and Test Procedures, ed. by G. D. A. Phillips and E. Tzavalis, Cambridge University Press, Cambridge, 2007.
20. “Philosophical Scrutiny of Evidence of Risks: From Bioethics to Bioevidence,” [with D. G. Mayo], Philosophy of Science, 2006, 73 (5), 803-816.
21. “Curve-Fitting, the Reliability of Inductive Inference and the Error-Statistical Approach,” Philosophy of Science, 2007, 74: 1046-1066.
22. “Testing for Nonstationarity Using Maximum Entropy Resampling: A Misspecification Testing Perspective,” [with A. Koutris and M. Heracleous], Econometric Reviews, 2008, 27, 363-384.
23. “Statistics and Economics,” pp. 1129-1162 in the New Palgrave Dictionary of Economics, 2nd ed., 2008, Eds. S. N. Durlauf and L. E. Blume. Palgrave Macmillan, London.
24. “Linear vs. Log-linear Unit-Root Specification: An Application of Mis-specification Encompassing,” [with D. F. Hendry and J. J. Reade], Oxford Bulletin of Economics and Statistics, 2008, 70: 829-847.
25. “Revisiting Error-Autocorrelation Correction: Common Factor Restrictions and Granger Non-Causality,” [with A. McGuirk], Oxford Bulletin of Economics and Statistics, 2009, 71: 259-282.
26. “The Pre-Eminence of Theory versus the European CVAR Perspective in Macroeconometric Modeling,” Economics: The Open-Access, Open-Assessment E-Journal, Vol. 3, 2009-10. Special issue on “Using Econometrics for Assessing Economic Models”
27. “Statistical Misspecification and the Reliability of Inference: the simple t-test in the presence of Markov dependence,” The Korean Economic Review, 2009, 25, 165-213.
28. “Akaike-type Information Criteria and the Reliability of Inference: Statistical Model Specification vs. Model Selection,” Journal of Econometrics, 158: 204-220, 2010.
29. The Discovery of Argon: A Case for Learning from Data? Philosophy of Science, 77: 359-380, 2010.
30. Is Frequentist Testing Vulnerable to the Base-Rate Fallacy? Philosophy of Science, 77: 565-583, 2010.
31. “Statistical Adequacy and the Trustworthiness of Empirical Evidence: Statistical vs. Substantive Information,” Economic Modelling, 27: 1436–1452, 2010.
32. “Error Statistics,” [with D. G. Mayo], pp. 173-208 in the Handbook of Philosophy of Science, vol. 7: Philosophy of Statistics, D. Gabbay, P. Thagard, and J. Woods (editors), Elsevier, 2011.
33. “Revisiting the Welch Uniform Model: A case for Conditional Inference?” Advances and Applications in Statistical Science, 5: 33-52, 2011.
34. “Mispaced Criticisms of Neyman-Pearson (N-P) Testing in the Case of Two Simple Hypotheses,” Advances and Applications in Statistical Science, 6: 229-242, 2011.
35. “Foundational Issues in Statistical Modeling: Statistical Model Specification and Validation,” Rationality, Markets and Morals, Vol. 2, 2011, 146–178, Special Topic: Statistical Science and Philosophy of Science
36 “Revisiting the Berger location model: Fallacious Confidence Interval or a Rigged Example?” Statistical Methodology, 9: 555-561, 2012
37. “A Frequentist Interpretation of Probability for Model-Based Inductive Inference,” Synthese, 190: 1555-1585, 2013.
38. “Philosophy of Econometrics,” pp. 329-393 the Handbook of Philosophy of Science, vol. 12: Philosophy of Economics, editor, U. Maki, general editors D. Gabbay, P. Thagard, and J. Woods, Elsevier, 2012.
39. “Who should be Afraid of the Jeffreys-Lindley paradox?” Philosophy of Science, 80: 73-93, 2013.
40. “The ‘Mixed Experiment’’ Example Revisited: Fallacious Frequentist Inference or an Improper Statistical Model?’’ Advances and Applications of Statistical Science, 6: 29-47, 2013.
41. “Revisiting the Likelihoodist Evidential Account,” Journal of Statistical Theory and Practice, 7: 187-195, 2013.
42. “Recurring Controversies about P values and Confidence Intervals Revisited,” Ecology, 95(3), 2014, pp. 645-651.
43. “Reflections on the LSE Tradition in Econometrics: a Student’s Perspective,” (Economia. History, Methodology, Philosophy, 4(3): 343-380, 2014.
44. “Revisiting Haavelmo’s Structural Econometrics: Bridging the Gap between Theory and Data,” Journal of Economic Methodology, 22: 171-196, 2015.
45. "Error Statistical Modeling and Inference: where Methodology Meets Ontology" [with D.G. Mayo], Synthese, 192: 3533-3555, 2015.
46. “Why the Decision-Theoretic Perspective Misrepresents Frequentist Inference”, pp. 3-28 in Advances in Statistical Methodologies and Their Applications to Real Problems, ISBN 978-953-51-4962-0. [2017]
47. “Mis-Specification Testing in Retrospect”, Journal of Economic Surveys, Vol. 32, No. 2, pp. 541–577. [2018].
48. “Near-collinearity in Linear Regression Revisited: the Numerical vs. the Statistical Perspective”, Communications in Statistics, Theory and Methods, 2019 48(22): 5492-5516.
49. “On Modeling Heterogeneity in Linear Models using Trend Polynomials”, (with M. Michaelides), Economic Modelling, 2019, 85: 74-86;
50. “What Foundations for Statistical Modeling and Inference?’ Œconomia ­­– History / Methodology / Philosophy, 2019, 9(4): 832-860. URL :
51. “Yule-Simpson's paradox: the probabilistic versus the empirical conundrum’’, Statistical Methods & Applications, 30: 605-635., 2020.
52. “Methodology of Macroeconometrics”, in the Oxford Research Encyclopedia of Economics and Finance, Oxford University Press. doi: 10.1093/acrefore/9780190625979.013.175, 2021, Edited by Avinash Dixit, Sebastian Edwards, and Kenneth Judd.
53. “Philosophy of Econometrics” in The Routledge Handbook of Philosophy of Economics, (Routledge Handbooks in Philosophy) 1st Edition, edited by Conrad Heilmann, Julian Reiss, 2021.
54. “Bernoulli's golden theorem in retrospect: error probabilities and trustworthy evidence", Synthese, 2021, 1-28.
55. “Revisiting noncentrality-based confidence intervals, error probabilities and estimation-based effect sizes”, Journal of Mathematical Psychology, 2021, 104, p. 102580.
56. “Statistical Modeling and Inference in the Era of Data Science and Graphical Causal Modeling”, Journal of Economic Surveys, 2021, DOI: 10.1111/joes.12483
57. “Severity and Trustworthy Evidence: Foundational Problems versus Misuses of Frequentist Testing”, Philosophy of Science, 2022, DOI: