Nonlinear Cointegrating Power Function Regression with Endogeneity

Zhishui Hu, Peter Phillips and Qiying Wang
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Zhishui Hu: University of Science and Technology of China
Qiying Wang: School of Mathematics and Statistics, The University of Sydney

No 2211, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: This paper develops an asymptotic theory for nonlinear cointegrating power function regression. The framework extends earlier work on the deterministic trend case and allows for both endogeneity and heteroskedasticity, which makes the models and inferential methods relevant to many empirical economic and financial applications, including predictive regression. Accompanying the asymptotic theory of nonlinear regression, the paper establishes some new results on weak convergence to stochastic integrals that go beyond the usual semi-martingale structure and considerably extend existing limit theory, complementing other recent findings on stochastic integral asymptotics. The paper also provides a general framework for extremum estimation limit theory that encompasses stochastically nonstationary time series and should be of wide applicability.

Keywords: Nonlinear power regression; Least squares estimation; Nonstationarity; Endogeneity; Heteroscedasticity (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2019-12
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: NONLINEAR COINTEGRATING POWER FUNCTION REGRESSION WITH ENDOGENEITY (2021)
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