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 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.
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Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2013-40.
Length: 16 Daniel Ventosa-Santaulària and Carlos Vladimir Rodríguez-Caballero
Date of creation: 11 2013
Date of revision:
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Web page: http://www.econ.au.dk/afn/
Polynomial Regression; misleading Inference; Integrated Processes;
Other versions of this item:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-11-22 (All new papers)
- NEP-ECM-2013-11-22 (Econometrics)
- NEP-ETS-2013-11-22 (Econometric Time Series)
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