Polynomial Regressions and Nonsense Inference
Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis, and psychology, just to mention a few examples. In many cases, the data employed to estimate such estimations are time series that may exhibit stochastic nonstationary behavior. We extend Phillips’ (1986) results by proving an inference drawn from polynomial specifications, under stochastic nonstationarity, is misleading unless the variables cointegrate. We use a generalized polynomial specification as a vehicle to study its asymptotic and finite-sample properties. Our results, therefore, lead to a call to be cautious whenever practitioners estimate polynomial regressions.
|Length:||16 Daniel Ventosa-Santaulària and Carlos Vladimir Rodríguez-Caballero|
|Date of creation:||11 2013|
|Contact details of provider:|| Web page: http://www.econ.au.dk/afn/|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Wagner, Martin, 2012. "The Phillips unit root tests for polynomials of integrated processes," Economics Letters, Elsevier, vol. 114(3), pages 299-303.
- Labson B. Stephen & Crompton Paul L., 1993. "Common Trends in Economic Activity and Metals Demand: Cointegration and the Intensity of Use Debate," Journal of Environmental Economics and Management, Elsevier, vol. 25(2), pages 147-161, September.
- Kellenberg, Derek, 2012. "Trading wastes," Journal of Environmental Economics and Management, Elsevier, vol. 64(1), pages 68-87.
- Zsolt Darvas, 2008.
"Estimation Bias and Inference in Overlapping Autoregressions: Implications for the Target-Zone Literature,"
Oxford Bulletin of Economics and Statistics,
Department of Economics, University of Oxford, vol. 70(1), pages 1-22, 02.
- Zsolt Darvas, 2007. "Estimation Bias and Inference in Overlapping Autoregressions: Implications for the Target Zone Literature," Working Papers 0701, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
- Lee, Young-Sook & Kim, Tae-Hwan & Newbold, Paul, 2005. "Spurious nonlinear regressions in econometrics," Economics Letters, Elsevier, vol. 87(3), pages 301-306, June.
- Young-Sook Lee & Tae-Hwan Kim & Paul Newbold, 2004. "Spurious Nonlinear Regressions In Econometrics," Royal Economic Society Annual Conference 2004 27, Royal Economic Society.
- Maximilian Auffhammer & Ryan Kellogg, 2011. "Clearing the Air? The Effects of Gasoline Content Regulation on Air Quality," American Economic Review, American Economic Association, vol. 101(6), pages 2687-2722, October.
- Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
- Peter C.B. Phillips, 1985. "Understanding Spurious Regressions in Econometrics," Cowles Foundation Discussion Papers 757, Cowles Foundation for Research in Economics, Yale University.
- Marco Leonardi & Giovanni Pica, 2013. "Who Pays for it? The Heterogeneous Wage Effects of Employment Protection Legislation," Economic Journal, Royal Economic Society, vol. 123(12), pages 1236-1278, December.
- Marco Leonardi & Giovanni Pica, 2010. "Who Pays for it? The Heterogeneous Wage Effects of Employment Protection Legislation," CSEF Working Papers 265, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy, revised 13 May 2012.
- Marco Leonardi & Giovanni Pica, 2012. "Who pays for it? The Heterogeneous Wage Effects of Employment Protection Legislation," Working Papers 436, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
- Leonardi, Marco & Pica, Giovanni, 2010. "Who Pays for It? The Heterogeneous Wage Effects of Employment Protection Legislation," IZA Discussion Papers 5335, Institute for the Study of Labor (IZA).
- Christos Ioannidis & David A. Peel & Michael J. Peel, 2003. "The Time Series Properties of Financial Ratios: Lev Revisited," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 30(5-6), pages 699-714.
- Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
- Pena, Daniel & Rodriguez, Julio, 2005. "Detecting nonlinearity in time series by model selection criteria," International Journal of Forecasting, Elsevier, vol. 21(4), pages 731-748.
- Green, Donald P. & Leong, Terence Y. & Kern, Holger L. & Gerber, Alan S. & Larimer, Christopher W., 2009. "Testing the Accuracy of Regression Discontinuity Analysis Using Experimental Benchmarks," Political Analysis, Cambridge University Press, vol. 17(04), pages 400-417, September.
- de Jong, Robert M., 2003. "Logarithmic spurious regressions," Economics Letters, Elsevier, vol. 81(1), pages 13-21, October.
- Román Ferrer, 2010. "Linear and nonlinear interest rate exposure in Spain," Managerial Finance, Emerald Group Publishing, vol. 36(5), pages 431-451, April. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:aah:create:2013-40. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.