Aris Spanos
Biographic Data
| ID | 1102459 |
|---|---|
| NAME | Aris Spanos |
| GIVEN NAMES | Aris |
| FAMILY NAME | Spanos |
| SIGNATURE | SPANOS A |
| AFFILIATIONS | Virginia Tech |
| ORCID | 0000-0002-9229-424X |
| VERIFIED | Yes |
| TOTAL WORKS | 17 |
| TOTAL CITATIONS | 90 |
| AUTHOR COUNT | 17 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1988 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 5 |
Revisiting the Phillips Curve: The Empirical Relationship Yet to be Validated
The Phillips curve began life in 1958 as a simple curve‐fitted relationship between the rates of wage inflation and unemployment and went on to play a crucial role in the broader development of macroeconomics, giving rise to several controversies about its interpretation and role in policy‐making. Recently, the traditional narrative about its theoretical underpinnings has been called into question as a sequence of ‘stories’ to provide support for…
Severity and Trustworthy Evidence: Foundational Problems versus Misuses of Frequentist Testing
For model-based frequentist statistics, based on a parametric statistical model ${{\cal M}_\theta }({\bf{x}})$ , the trustworthiness of the ensuing evidence depends crucially on (i) the validity of the probabilistic assumptions comprising ${{\cal M}_\theta }({\bf{x}})$ , (ii) the optimality of the inference procedures employed, and (iii) the adequateness of the sample size ( n ) to learn from data by securing (i)–(ii). It is argued that the criti…
Bernoulli’s golden theorem in retrospect: Error probabilities and trustworthy evidence
What Foundations for Statistical Modeling and Inference
The primary aim of this article is to review the above books in a comparative way from the standpoint of my perspective on empirical modeling and inference. These two books pertaining to the nature and justification of statistical inference were written by two philosophers of science more than 50 years apart. What they have in common is the critical eye of a philosopher of science scrutinizing the statistical reasoning employed by statisticians a…
Transforming structural econometrics: Substantive vs. statistical premises of inference
How could one transform structural econometrics with a view to deliver empirical models that generate reliable inferences and trustworthy evidence for or against theories or claims, as well as provide trustable guidance for economic policy makers? Nell and Errouaki, in Rational Econometric Man: Transforming Structural Econometrics, put forward their proposal on how to achieve that, by discussing the effectiveness of alternative proposals in the l…
Error statistical modeling and inference: Where methodology meets ontology
Reflections on the LSE Tradition in Econometrics: A Student’s Perspective
Since the mid 1960s the LSE tradition, led initially by Denis Sargan and later by David Hendry, has contributed several innovative techniques and modeling strategies to applied econometrics. A key feature of the LSE tradition has been its striving to strike a balance between the theory-oriented perspective of textbook econometrics and the ARIMA data-oriented perspective of time series analysis. The primary aim of this article is to provide a stud…
A frequentist interpretation of probability for model-based inductive inference
Who Should Be Afraid of the Jeffreys-Lindley Paradox
The article revisits the large n (sample size) problem as it relates to the Jeffreys-Lindley paradox to compare the frequentist, Bayesian, and likelihoodist approaches to inference and evidence. It is argued that what is fallacious is to interpret a rejection of as providing the same evidence for a particular alternative , irrespective of n ; this is an example of the fallacy of rejection. Moreover, the Bayesian and likelihoodist approaches are s…
The Discovery of Argon: A Case for Learning from Data
Rayleigh and Ramsay discovered the inert gas argon in the atmospheric air in 1895 using a carefully designed sequence of experiments guided by an informal statistical analysis of the resulting data. The primary objective of this article is to revisit this remarkable historical episode in order to make a case that the error-statistical perspective can be used to bring out and systematize (not to reconstruct) these scientists't resourceful ways and…
Is Frequentist Testing Vulnerable to the Base-Rate Fallacy
This article calls into question the charge that frequentist testing is susceptible to the base-rate fallacy. It is argued that the apparent similarity between examples like the Harvard Medical School test and frequentist testing is highly misleading. A closer scrutiny reveals that such examples have none of the basic features of a proper frequentist test, such as legitimate data, hypotheses, test statistics, and sampling distributions. Indeed, t…
Curve Fitting, the Reliability of Inductive Inference, and the Error-Statistical Approach
The main aim of this paper is to revisit the curve fitting problem using the reliability of inductive inference as a primary criterion for the ‘fittest’ curve. Viewed from this perspective, it is argued that a crucial concern with the current framework for addressing the curve fitting problem is, on the one hand, the undue influence of the mathematical approximation perspective, and on the other, the insufficient attention paid to the statistical…
Philosophical Scrutiny of Evidence of Risks: From Bioethics to Bioevidence
We argue that a responsible analysis of today's evidence-based risk assessments and risk debates in biology demands a critical or metascientific scrutiny of the uncertainties, assumptions, and threats of error along the manifold steps in risk analysis. Without an accompanying methodological critique, neither sensitivity to social and ethical values, nor conceptual clarification alone, suffices. In this view, restricting the invitation for philoso…
Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction
Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive…
Methodology in Practice: Statistical Misspecification Testing
The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve around whether the assumptions are actually met in the studies available, e.g., in psycholog…
Statistical Foundations of Econometric Modelling
Statistical Foundations of Econometric Modelling
Journal Article Statistical Foundations of Econometric Modelling Get access Statistical Foundations of Econometric Modelling. By ARIS SPANOS. (Cambridge: Cambridge University Press, 1986. Pp. xxiii + 695. £40.00 hardback, £15.00 paperback.) Hedley Rees Hedley Rees University of Bristol Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 98, Issue 389, 1 March 1988, Pages 202–204, https://doi.org/1…
Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction
Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive…
Methodology in Practice: Statistical Misspecification Testing
The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve around whether the assumptions are actually met in the studies available, e.g., in psycholog…
Is Frequentist Testing Vulnerable to the Base-Rate Fallacy
This article calls into question the charge that frequentist testing is susceptible to the base-rate fallacy. It is argued that the apparent similarity between examples like the Harvard Medical School test and frequentist testing is highly misleading. A closer scrutiny reveals that such examples have none of the basic features of a proper frequentist test, such as legitimate data, hypotheses, test statistics, and sampling distributions. Indeed, t…
Who Should Be Afraid of the Jeffreys-Lindley Paradox
The article revisits the large n (sample size) problem as it relates to the Jeffreys-Lindley paradox to compare the frequentist, Bayesian, and likelihoodist approaches to inference and evidence. It is argued that what is fallacious is to interpret a rejection of as providing the same evidence for a particular alternative , irrespective of n ; this is an example of the fallacy of rejection. Moreover, the Bayesian and likelihoodist approaches are s…
Error statistical modeling and inference: Where methodology meets ontology
Curve Fitting, the Reliability of Inductive Inference, and the Error-Statistical Approach
The main aim of this paper is to revisit the curve fitting problem using the reliability of inductive inference as a primary criterion for the ‘fittest’ curve. Viewed from this perspective, it is argued that a crucial concern with the current framework for addressing the curve fitting problem is, on the one hand, the undue influence of the mathematical approximation perspective, and on the other, the insufficient attention paid to the statistical…
A frequentist interpretation of probability for model-based inductive inference
The Discovery of Argon: A Case for Learning from Data
Rayleigh and Ramsay discovered the inert gas argon in the atmospheric air in 1895 using a carefully designed sequence of experiments guided by an informal statistical analysis of the resulting data. The primary objective of this article is to revisit this remarkable historical episode in order to make a case that the error-statistical perspective can be used to bring out and systematize (not to reconstruct) these scientists't resourceful ways and…
Philosophical Scrutiny of Evidence of Risks: From Bioethics to Bioevidence
We argue that a responsible analysis of today's evidence-based risk assessments and risk debates in biology demands a critical or metascientific scrutiny of the uncertainties, assumptions, and threats of error along the manifold steps in risk analysis. Without an accompanying methodological critique, neither sensitivity to social and ethical values, nor conceptual clarification alone, suffices. In this view, restricting the invitation for philoso…
Bernoulli’s golden theorem in retrospect: Error probabilities and trustworthy evidence
Statistical Foundations of Econometric Modelling
Journal Article Statistical Foundations of Econometric Modelling Get access Statistical Foundations of Econometric Modelling. By ARIS SPANOS. (Cambridge: Cambridge University Press, 1986. Pp. xxiii + 695. £40.00 hardback, £15.00 paperback.) Hedley Rees Hedley Rees University of Bristol Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 98, Issue 389, 1 March 1988, Pages 202–204, https://doi.org/1…
Statistical Foundations of Econometric Modelling
Statistical Foundations of Econometric Modelling
Journal Article Statistical Foundations of Econometric Modelling Get access Statistical Foundations of Econometric Modelling. By ARIS SPANOS. (Cambridge: Cambridge University Press, 1986. Pp. xxiii + 695. £40.00 hardback, £15.00 paperback.) Hedley Rees Hedley Rees University of Bristol Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 98, Issue 389, 1 March 1988, Pages 202–204, https://doi.org/1…
Methodology in Practice: Statistical Misspecification Testing
The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve around whether the assumptions are actually met in the studies available, e.g., in psycholog…
Philosophical Scrutiny of Evidence of Risks: From Bioethics to Bioevidence
We argue that a responsible analysis of today's evidence-based risk assessments and risk debates in biology demands a critical or metascientific scrutiny of the uncertainties, assumptions, and threats of error along the manifold steps in risk analysis. Without an accompanying methodological critique, neither sensitivity to social and ethical values, nor conceptual clarification alone, suffices. In this view, restricting the invitation for philoso…
Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction
Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive…
Curve Fitting, the Reliability of Inductive Inference, and the Error-Statistical Approach
The main aim of this paper is to revisit the curve fitting problem using the reliability of inductive inference as a primary criterion for the ‘fittest’ curve. Viewed from this perspective, it is argued that a crucial concern with the current framework for addressing the curve fitting problem is, on the one hand, the undue influence of the mathematical approximation perspective, and on the other, the insufficient attention paid to the statistical…
The Discovery of Argon: A Case for Learning from Data
Rayleigh and Ramsay discovered the inert gas argon in the atmospheric air in 1895 using a carefully designed sequence of experiments guided by an informal statistical analysis of the resulting data. The primary objective of this article is to revisit this remarkable historical episode in order to make a case that the error-statistical perspective can be used to bring out and systematize (not to reconstruct) these scientists't resourceful ways and…
Is Frequentist Testing Vulnerable to the Base-Rate Fallacy
This article calls into question the charge that frequentist testing is susceptible to the base-rate fallacy. It is argued that the apparent similarity between examples like the Harvard Medical School test and frequentist testing is highly misleading. A closer scrutiny reveals that such examples have none of the basic features of a proper frequentist test, such as legitimate data, hypotheses, test statistics, and sampling distributions. Indeed, t…
A frequentist interpretation of probability for model-based inductive inference
Who Should Be Afraid of the Jeffreys-Lindley Paradox
The article revisits the large n (sample size) problem as it relates to the Jeffreys-Lindley paradox to compare the frequentist, Bayesian, and likelihoodist approaches to inference and evidence. It is argued that what is fallacious is to interpret a rejection of as providing the same evidence for a particular alternative , irrespective of n ; this is an example of the fallacy of rejection. Moreover, the Bayesian and likelihoodist approaches are s…
Reflections on the LSE Tradition in Econometrics: A Student’s Perspective
Since the mid 1960s the LSE tradition, led initially by Denis Sargan and later by David Hendry, has contributed several innovative techniques and modeling strategies to applied econometrics. A key feature of the LSE tradition has been its striving to strike a balance between the theory-oriented perspective of textbook econometrics and the ARIMA data-oriented perspective of time series analysis. The primary aim of this article is to provide a stud…
Error statistical modeling and inference: Where methodology meets ontology
Transforming structural econometrics: Substantive vs. statistical premises of inference
How could one transform structural econometrics with a view to deliver empirical models that generate reliable inferences and trustworthy evidence for or against theories or claims, as well as provide trustable guidance for economic policy makers? Nell and Errouaki, in Rational Econometric Man: Transforming Structural Econometrics, put forward their proposal on how to achieve that, by discussing the effectiveness of alternative proposals in the l…
What Foundations for Statistical Modeling and Inference
The primary aim of this article is to review the above books in a comparative way from the standpoint of my perspective on empirical modeling and inference. These two books pertaining to the nature and justification of statistical inference were written by two philosophers of science more than 50 years apart. What they have in common is the critical eye of a philosopher of science scrutinizing the statistical reasoning employed by statisticians a…
Bernoulli’s golden theorem in retrospect: Error probabilities and trustworthy evidence
Severity and Trustworthy Evidence: Foundational Problems versus Misuses of Frequentist Testing
For model-based frequentist statistics, based on a parametric statistical model ${{\cal M}_\theta }({\bf{x}})$ , the trustworthiness of the ensuing evidence depends crucially on (i) the validity of the probabilistic assumptions comprising ${{\cal M}_\theta }({\bf{x}})$ , (ii) the optimality of the inference procedures employed, and (iii) the adequateness of the sample size ( n ) to learn from data by securing (i)–(ii). It is argued that the criti…
Revisiting the Phillips Curve: The Empirical Relationship Yet to be Validated
The Phillips curve began life in 1958 as a simple curve‐fitted relationship between the rates of wage inflation and unemployment and went on to play a crucial role in the broader development of macroeconomics, giving rise to several controversies about its interpretation and role in policy‐making. Recently, the traditional narrative about its theoretical underpinnings has been called into question as a sequence of ‘stories’ to provide support for…
Computer Science (13 works) · Econometrics (12 works) · Mathematics (12 works) · Statistics (12 works) · Epistemology (11 works) · Philosophy (11 works) · Inference (10 works) · Philosophy and History of Science (10 works) · Statistical inference (8 works) · Artificial Intelligence (7 works)