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Spline-backfitted kernal smoothing of additive coefficient model

Liu, Rong; Yang, Lijian; Wang, Hui - In: Econometric theory 26 (2010) 1, pp. 29-59

Persistent link: https://www.econbiz.de/10003968457

Testing instability in a predictive regression model with nonstationary regressors

Cai, Zongwu; Wang, Yunfei; Wang, Yonggang - In: Econometric theory 31 (2015) 5, pp. 953-980

Persistent link: https://www.econbiz.de/10011545495

Non- and semiparametric identification of seasonal nonlinear autoregression models

Yang, Lijian; Tschernig, Rolf - In: Econometric theory 18 (2002) 6, pp. 1408-1448

Persistent link: https://www.econbiz.de/10001716911

Nonparametric estimation and testing of interaction in additive models

Sperlich, Stefan; Tjøstheim, Dag; Yang, Lijian - In: Econometric theory 18 (2002) 2, pp. 197-251

Persistent link: https://www.econbiz.de/10001661291

Uniform consistency of nonstationary kernel-weighted sample covariances for nonparametric regression

Li, Degui; Phillips, Peter C. B.; Gao, Jiti - In: Econometric theory 32 (2016) 3, pp. 655-685

Persistent link: https://www.econbiz.de/10011606819

Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction

Cheng, Tingting; Gao, Jiti; Linton, Oliver - 2018

In this paper, we propose three new predictive models: the multi-step nonparametric predictive regression model and the multi-step additive predictive regression model, in which the predictive variables are locally stationary time series; and the multi-step time-varying coefficient predictive...

Persistent link: https://www.econbiz.de/10011775136

Inconsistent VAR regression with common explosive roots

Phillips, Peter C. B.; Magdalinos, Tassos - In: Econometric theory 29 (2013) 4, pp. 808-837

Persistent link: https://www.econbiz.de/10010210160

Least squares and IVX limit theory in systems of predictive regressions with GARCH innovations

Magdalinos, Tassos - In: Econometric theory 38 (2022) 5, pp. 875-912

Persistent link: https://www.econbiz.de/10013469682