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Nonlinear cointegrating power function regression with endogeneity

编辑: 时间: 2020年08月27日 访问次数:0

报告人: Prof. Wang Qiying(The University of Sydney) 

时间: 2020年9月1日(星期二)下午3: 00开始 

地点: 钉钉群(群号: 31935074) 

摘要: 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. A new test for linear cointegration against nonlinear departures is developed based on a simple linearized pseudo-model that is very convenient for practical implementation and has standard normal limit theory in the strictly exogenous regressor case. 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. 

          (This is a joint work with Peter Phillips and Zhishui Hu.)

  
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