Polynomial Regressions and Nonsense Inference
AbstractPolynomial 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 specifications are time series that may exhibit stochastic nonstationary behavior. We extend Phillips’ results (Phillips, P. Understanding spurious regressions in econometrics. J. Econom. 1986, 33, 311–340.) by proving that 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.
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Ventosa-Santaulària, D.; Rodríguez-Caballero, C.V. Polynomial Regressions and Nonsense Inference. Econometrics 2013, 1, 236-248.
Ventosa-Santaulària D, Rodríguez-Caballero CV. Polynomial Regressions and Nonsense Inference. Econometrics. 2013; 1(3):236-248.Chicago/Turabian Style
Ventosa-Santaulària, Daniel; Rodríguez-Caballero, Carlos V. 2013. "Polynomial Regressions and Nonsense Inference." Econometrics 1, no. 3: 236-248.