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  1. Economic Modelling as Robustness Analysis.J. Kuorikoski, A. Lehtinen & C. Marchionni - 2010 - British Journal for the Philosophy of Science 61 (3):541-567.
    We claim that the process of theoretical model refinement in economics is best characterised as robustness analysis: the systematic examination of the robustness of modelling results with respect to particular modelling assumptions. We argue that this practise has epistemic value by extending William Wimsatt's account of robustness analysis as triangulation via independent means of determination. For economists robustness analysis is a crucial methodological strategy because their models are often based on idealisations and abstractions, and it is usually difficult to tell (...)
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  • Sound and Fury: McCloskey and Significance Testing in Economics.Kevin D. Hoover & Mark V. Siegler - 2008 - Journal of Economic Methodology 15 (1):1-37.
    For more than 20 years, Deidre McCloskey has campaigned to convince the economics profession that it is hopelessly confused about statistical significance. She argues that many practices associated with significance testing are bad science and that most economists routinely employ these bad practices: ?Though to a child they look like science, with all that really hard math, no science is being done in these and 96 percent of the best empirical economics ?? (McCloskey 1999). McCloskey's charges are analyzed and rejected. (...)
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  • Revisiting Data Mining: 'Hunting' with or Without a License.Aris Spanos - 2000 - Journal of Economic Methodology 7 (2):231-264.
    The primary objective of this paper is to revisit a number of empirical modelling activities which are often characterized as data mining, in an attempt to distinguish between the problematic and the non-problematic cases. The key for this distinction is provided by the notion of error-statistical severity. It is argued that many unwarranted data mining activities often arise because of inherent weaknesses in the Traditional Textbook (TT) methodology. Using the Probabilistic Reduction (PR) approach to empirical modelling, it is argued that (...)
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  • I Am Not, nor Have I Ever Been a Member of a Data-Mining Discipline.Clinton A. Greene - 2000 - Journal of Economic Methodology 7 (2):217-230.
    This paper argues classical statistics and standard econometrics are based on a desire to meet scientific standards for accumulating reliable knowledge. Science requires two inputs, mining of existing data for inspiration and new or 'out-of-sample' data for predictive testing. Avoidance of data-mining is neither possible nor desirable. In economics out-of-sample data is relatively scarce, so the production process should intensively exploit the existing data. But the two inputs should be thought of as complements rather than substitutes. And we neglect the (...)
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  • Teaching Philosophy of Science to Scientists: Why, What and How.Till Grüne-Yanoff - 2014 - European Journal for Philosophy of Science 4 (1):115-134.
    This paper provides arguments to philosophers, scientists, administrators and students for why science students should be instructed in a mandatory, custom-designed, interdisciplinary course in the philosophy of science. The argument begins by diagnosing that most science students are taught only conventional methodology: a fixed set of methods whose justification is rarely addressed. It proceeds by identifying seven benefits that scientists incur from going beyond these conventions and from acquiring abilities to analyse and evaluate justifications of scientific methods. It concludes that (...)
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  • Some Varieties of Robustness.Jim Woodward - 2006 - Journal of Economic Methodology 13 (2):219-240.
    It is widely believed that robustness (of inferences, measurements, models, phenomena and relationships discovered in empirical investigation etc.) is a Good Thing. However, there are many different notions of robustness. These often differ both in their normative credentials and in the conditions that warrant their deployment. Failure to distinguish among these notions can result in the uncritical transfer of considerations which support one notion to contexts in which another notion is being deployed. This paper surveys several different notions of robustness (...)
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  • A Misconception of the Semantic Conception of Econometrics?Hsiang‐Ke Chao - 2005 - Journal of Economic Methodology 12 (1):125-135.
    Davis argues that Suppe's semantic conception provides a better understanding of the problem of theory?data confrontations. Applying his semantic methodology to the LSE (London School of Economics) approach of econometrics, he concludes that the LSE approach fails to address the issue of bridging the theory?data gap. This paper suggests two other versions of the semantic view of theories in the philosophy of science, due to Suppes and van Fraassen, and argues that the LSE approach can be construed under these two (...)
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  • The Ontological Status of Shocks and Trends in Macroeconomics.Kevin D. Hoover - 2015 - Synthese 192 (11):3509-3532.
    Modern empirical macroeconomic models, known as structural autoregressions (SVARs) are dynamic models that typically claim to represent a causal order among contemporaneously valued variables and to merely represent non-structural (reduced-form) co-occurence between lagged variables and contemporaneous variables. The strategy is held to meet the minimal requirements for identifying the residual errors in particular equations in the model with independent, though otherwise not directly observable, exogenous causes (“shocks”) that ultimately account for change in the model. In nonstationary models, such shocks accumulate (...)
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