Probability, Econometrics and Truth

Keuzenkamp, Hugo A.

doi:10.1017/cbo9780511493300

Cited by 41 publications

(5 citation statements)

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“…Freedman( 2009), E Leamer (1983), and H. Keuzenkamp (2000),if it became dominant in econometric practice ,would go along way toward satisfying Keynes's original objections about the use of econometrics in attempting to quantify business cycle research over time.…”

Section: Discussionmentioning

confidence: 99%

The Role of Ignorance about Keynes’s Inexact, Approximation Approach to Measurement in the A Treatise on Probability in the Keynes-Tinbergen Exchanges of 1938-1940 in the Economic Journal

Brady¹

2019

APE

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J.Tinbergen and J. M. Keynes held diametrically opposed positions on measurement. Tinbergen’s physics background led him to deploy an exact approach to measurement based on the specification of probability distributions, like the normal and log normal, with exact and precisely probabilities that were linear, additive and definite. All probabilities for Tinbergen were assumed to be well defined, precise, exact, determinate, definite, additive, linear, independent single number answers, whether the field was physics or economics. Keynes’s approach was an inexact one. Probabilities for Keynes were, with a few exceptions, partially defined, imprecise, inexact, indefinite, indeterminate ,non additive, non linear, and dependent. Probability estimates for Keynes required two numbers to specify the probability within a lower and upper bound (limit), and not one single number like Tinbergen’s approach. Keynes called this approach Approximation. Keynesian probabilities are interval valued.The problem, from Keynes’s perspective, was that Tinbergen was trying to apply to economic data techniques which were only sound when applied in physics ,where laboratory controlled environments with detailed experimental design could generate data and replicate/duplicate the experiments. Keynes had always argued that economics was not a physical or life science like physics, engineering, biology or chemistry and that it could never be like physics.

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Section: Discussionmentioning

confidence: 99%

The Role of Ignorance about Keynes’s Inexact, Approximation Approach to Measurement in the A Treatise on Probability in the Keynes-Tinbergen Exchanges of 1938-1940 in the Economic Journal

Brady¹

2019

APE

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“…The two approaches are thus fundamentally incompatible, cf. Keuzenkamp (2000), Berger (2003), Hubbard and Bayarri (2003), Christensen (2005), and Schneider (2015). 7 The NP methodology is based on long-run error control in a repeated sampling setup with experimental data, and does not allow inductive inferential statements for a spe-ci…c sample, whether experimental or observational.…”

Section: Frequentist Testsmentioning

confidence: 99%

“…Note, however, that the testable restrictions of rational expectations models are typically formulated as a null hypothesis with no explicit alternative hypothesis. Such formulations are more in line with Fisher's framework than with the NP framework 27 Keuzenkamp (2000). presents a detailed critique of Haavelmo's experimental methodology and his notion of 'experimental design'.…”

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confidence: 99%

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Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective

Engsted

Schneider

2023

SSRN Journal

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“…Yet the issue touches a fundamental nerve-centre in econometric modeling and affects the interpretation of the statistical methods used in econometrics. Some dimensions of this complex subject have been thoughtfully addressed in recent studies by Keuzenkamp (2000) and Cartwright (2001).…”

Section: Robustification and Har Inferencementioning

confidence: 99%

Automated Discovery in Econometrics

Phillips

2005

Econ. Theory

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Our subject is the notion of automated discovery in econometrics. Advances in computer power, electronic communication, and data collection processes have all changed the way econometrics is conducted. These advances have helped to elevate the status of empirical research within the economics profession in recent years and they now open up new possibilities for empirical econometric practice. Of particular significance is the ability to build econometric models in an automated way according to an algorithm of decision rules that allow for (what we call here) heteroskedastic and autocorrelation robust (HAR) inference. Computerized search algorithms may be implemented to seek out suitable models, thousands of regressions and model evaluations may be performed in seconds, statistical inference may be automated according to the properties of the data, and policy decisions can be made and adjusted in real time with the arrival of new data. We discuss some aspects and implications of these exciting, emergent trends in econometrics.

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Probability, Econometrics and Truth

Cited by 41 publications

References 0 publications

The Role of Ignorance about Keynes’s Inexact, Approximation Approach to Measurement in the A Treatise on Probability in the Keynes-Tinbergen Exchanges of 1938-1940 in the Economic Journal

The Role of Ignorance about Keynes’s Inexact, Approximation Approach to Measurement in the A Treatise on Probability in the Keynes-Tinbergen Exchanges of 1938-1940 in the Economic Journal

Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective

Automated Discovery in Econometrics

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