Special Issue "Towards a New Paradigm for Statistical Evidence"
A special issue of Econometrics (ISSN 2225-1146).
Deadline for manuscript submissions: closed (30 September 2019).
A printed edition of this Special Issue is available here.
Interests: econometrics; empirical finance; statistical inference
Interests: oil prices; stocks; forecasting; copula; DCC models; wavelets; financial econometrics
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In many areas of science including business disciplines, statistical decisions are often made almost exclusively using the “p-value < 0.05” criterion, regardless of sample size, statistical power, or expected loss.
Serious concerns about this practice have grown, with the warnings that false or distorted scientific findings are widespread as a result. They include the statement made by the American Statistical Association (Wasserstein and Lazar, 2016) and Presidential address given by American Finance Association (Harvey, 2017). Past and recent studies that document the empirical evidence on this practice include Keuzenkamp and Magnus (1995) and McCloskey and Ziliak (1996) for economics, Kim et al. (2018) for accounting, and Kim and Ji (2016) for finance, among others.
The problem has particularly become more serious in recent times, with increasing availability of large or massive data sets. On this point, Rao and Lovric (2016) propose that the 21st century researchers work towards a “paradigm shift” in testing statistical hypothesis. There are also calls that the researchers conduct more extensive exploratory data analysis before inferential statistics are considered for decision-making (see, for example, Leek and Peng, 2015; Soyer and Hogarth 2012).
In light of these concerns and proposals, it is important that the new research efforts should be directed to
- developing a new criterion for statistical evidence;
- providing modifications to the p-value criterion; and
- adopting more sensible alternatives to the p-value criterion including graphical methods.
This special issue invites theoretical or empirical papers on these issues. Possible topics include, but not limited to,
- New or alternative methods of hypothesis testing such as estimation-based method (e.g. confidence interval), predictive inference, and equivalence testing;
- Application of adaptive or optimal level of significance to business decisions
- Decision-theoretic approach to hypothesis testing and its applications
- Compromise between the classical and Bayesian methods of hypothesis testing
- Exploratory data analysis for large or massive data sets
Critical review papers on the current practice of hypothesis testing and future directions in the business or related disciplines may also be considered.
Harvey, C. R. (2017), ‘Presidential Address: The Scientific Outlook in Financial Economics’, Journal of Finance, Vol. 72, No. 4, pp. 1399-1440.
Kim J. H., Ji, P., Ahmed, K., 2018, Significance Testing in Accounting Research: A Critical Evaluation based on Evidence, Abacus: a Journal of Accounting, Finance and Business Studies, forthcoming.
Kim J. H. Choi, I, 2017, Unit Roots in Economic and Financial Time Series: A Re-evaluation at the Decision-based Significance Levels. Econometrics, 5(3), 41, Special Issue “Celebrated Econometricians: Peter Phillips”.
Kim, J. H., P. Ji, 2015, Significance Testing in Empirical Finance: A Critical Review and Assessment, Journal of Empirical Finance, 34. 1-14.
Keuzenkamp, H.A. and Magnus, J. 1995, On tests and significance in econometrics, Journal of Econometrics, 67, 1, 103–128.
Leek, J. T., & Peng, R. D. (2015). Statistics: P values are just the tip of the iceberg, Nature. 2015 Apr 30, 520-612 (7549): doi: 10.1038/520612a
McCloskey, D. and Ziliak, S. 1996, The standard error of regressions, Journal of Economic Literature, 34, 97–114.
Rao, C. R. and Lovric, M. M., 2016, Testing Point Null Hypothesis of a Normal Mean and the Truth: 21st Century Perspective, Journal of Modern Applied Statistical Methods, 15 (2), 2-21.
Soyer, E., Hogarth, R.M., 2012. The illusion of predictability: how regression statistics mislead experts. International Journal of Forecasting. 28, 695–711.
Wasserstein, R. L. and N. A. Lazar (2016), ‘The ASA's statement on p-values: Context, process, and purpose’, The American Statistician, Vol. 70, No. 2, pp. 129-133.
Prof. M. Ishaq Bhatti
Prof. Jae H. Kim
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