IDEAS home Printed from https://ideas.repec.org/r/tpr/restat/v65y1983i1p1-12.html
   My bibliography  Save this item

Data Mining

Citations

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. [経済]計量経済学は衰退しました
    by himaginary in himaginaryの日記 on 2012-08-07 12:00:00

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Matei Demetrescu & Uwe Hassler & Vladimir Kuzin, 2011. "Pitfalls of post-model-selection testing: experimental quantification," Empirical Economics, Springer, vol. 40(2), pages 359-372, April.
  2. Stillwagon, Josh R., 2016. "Non-linear exchange rate relationships: An automated model selection approach with indicator saturation," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 84-109.
  3. Coppi, Renato, 2002. "A theoretical framework for Data Mining: the "Informational Paradigm"," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 501-515, February.
  4. Acosta-González, Eduardo & Fernández-Rodríguez, Fernando & Sosvilla-Rivero, Simón, 2012. "On factors explaining the 2008 financial crisis," Economics Letters, Elsevier, vol. 115(2), pages 215-217.
  5. Todd E. Clark, 2004. "Can out-of-sample forecast comparisons help prevent overfitting?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 115-139.
  6. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages 32-61, March.
  7. Hristos Doucouliagos & Patrice Laroche & Douglas L. Kruse & T. D. Stanley, 2020. "Is Profit Sharing Productive? A Meta‐Regression Analysis," British Journal of Industrial Relations, London School of Economics, vol. 58(2), pages 364-395, June.
  8. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
  9. Rocha, Jordano Vieira & Pereira, Pedro L. Valls, 2015. "Forecast comparison with nonlinear methods for Brazilian industrial production," Textos para discussão 397, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
  10. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
  11. Krolzig, Hans-Martin & Hendry, David F., 2001. "Computer automation of general-to-specific model selection procedures," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 831-866, June.
  12. Eduardo Acosta-González & Fernando Fernández-Rodríguez, 2014. "Forecasting Financial Failure of Firms via Genetic Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 43(2), pages 133-157, February.
  13. Pillai N., Vijayamohanan, 2008. "In Quest of Truth: The War of Methods in Economics," MPRA Paper 8866, University Library of Munich, Germany.
  14. Abel Brodeur & Mathias Lé & Marc Sangnier & Yanos Zylberberg, 2016. "Star Wars: The Empirics Strike Back," American Economic Journal: Applied Economics, American Economic Association, vol. 8(1), pages 1-32, January.
  15. Teplova, Tamara & Mikova, Evgeniya & Nazarov, Nikolai, 2017. "Stop losses momentum strategy: From profit maximization to risk control under White’s Bootstrap Reality Check," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 240-258.
  16. Kevin Hoover & Mark Siegler, 2008. "Sound and fury: McCloskey and significance testing in economics," Journal of Economic Methodology, Taylor & Francis Journals, vol. 15(1), pages 1-37.
  17. Bauwens, Luc & Sucarrat, Genaro, 2010. "General-to-specific modelling of exchange rate volatility: A forecast evaluation," International Journal of Forecasting, Elsevier, vol. 26(4), pages 885-907, October.
  18. Charemza, Wojciech W. & Strachan, Rodney & Zurawski, Piotr, 2010. "False posteriors for the long-term growth determinants," Economics Letters, Elsevier, vol. 109(3), pages 144-146, December.
  19. repec:oxf:wpaper:2000-w34.2 is not listed on IDEAS
  20. Kornstad, Tom & Nymoen, Ragnar & Skjerpen, Terje, 2013. "Macroeconomic shocks and the probability of being employed," Economic Modelling, Elsevier, vol. 33(C), pages 572-587.
  21. Barkbu, Bergljot Bjornson & Nymoen, Ragnar & Roed, Knut, 2003. "Wage coordination and unemployment dynamics in Norway and Sweden," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 32(1), pages 37-58, March.
  22. Mitesh Kataria, 2010. "The Role of Preferences in Disagreements over Scientific Hypothesis: An Empirical Inquiry into Environmental and Economic Decision Making," Jena Economics Research Papers 2010-088, Friedrich-Schiller-University Jena.
  23. Josh R. Stillwagon, 2015. "TIPS and the VIX: Non-linear Spillovers from Financial Panic to Breakeven Inflation," Working Papers 1502, Trinity College, Department of Economics.
  24. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
  25. Hendry, David F., 2001. "Achievements and challenges in econometric methodology," Journal of Econometrics, Elsevier, vol. 100(1), pages 7-10, January.
  26. Castle, Jennifer L. & Hendry, David F., 2009. "The long-run determinants of UK wages, 1860-2004," Journal of Macroeconomics, Elsevier, vol. 31(1), pages 5-28, March.
  27. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
  28. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
  29. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages C32-C61, 03.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.