CFDP 2211

Nonlinear Cointegrating Power Function Regression with Endogeneity


Publication Date: December 2019

Pages: 42


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

JEL Classification Codes: C13, C22

JEL Classification Codes: C13C22

See CFP: CFP 1712

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