Polynomial Regressions and Nonsense Inference
Daniel Ventosa-Santaulària and
Carlos Vladimir Rodríguez-Caballero ()
Additional contact information
Carlos Vladimir Rodríguez-Caballero: Aarhus University and CREATES, Postal: Department of Economics and Business, Fuglesangs Allé 4, 8210 Aarhus V, Denmark
Authors registered in the RePEc Author Service: Carlos Vladimir Rodríguez Caballero
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
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.
Keywords: Polynomial Regression; misleading Inference; Integrated Processes (search for similar items in EconPapers)
JEL-codes: C12 C15 C22 (search for similar items in EconPapers)
Pages: 16 Daniel Ventosa-Santaulària and Carlos Vladimir Rodríguez-Caballero
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Journal Article: Polynomial Regressions and Nonsense Inference (2013)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2013-40
Access Statistics for this paper
More papers in CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
Bibliographic data for series maintained by ().