Testing cointegration relationship in a semiparametric varying coefficient model

Gu, Jingping; Liang, Zhongwen

doi:10.1016/j.jeconom.2013.08.006

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…We next turn to the proof of Theorem 3.2. Similar to Lemma 2 in Xiao (2009) and Lemma 2 in Gu and Liang (2013), we have the following joint convergence result.…”

Section: Appendix: Proofs Of the Main Resultsmentioning

confidence: 62%

Estimation of semi-varying coefficient models with nonstationary regressors

Liang

et al. 2015

Econometric Reviews

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We study a semi-varying coefficient model where the regressors are generated by the multivariate unit root I(1) processes. The influence of the explanatory vectors on the response variable satisfies the semiparametric partially linear structure with the nonlinear component being functional coefficients. A semiparametric estimation methodology with the first-stage local polynomial smoothing is applied to estimate both the constant coefficients in the linear component and the functional coefficients in the nonlinear component. The asymptotic distribution theory for the proposed semiparametric estimators is established under some mild conditions, from which both the parametric and nonparametric estimators are shown to enjoy the well-known superconsistency property. Furthermore, a simulation study is conducted to investigate the finite sample performance of the developed methodology and results.

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“…We next turn to the proof of Theorem 3.2. Similar to Lemma 2 in Xiao (2009) and Lemma 2 in Gu and Liang (2013), we have the following joint convergence result.…”

Section: Appendix: Proofs Of the Main Resultsmentioning

confidence: 62%

Estimation of semi-varying coefficient models with nonstationary regressors

Liang

et al. 2015

Econometric Reviews

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“…Surveys of PLMs are given by Härdle et al (2000); Liang (2008). When γ 0 = 0, model (1.1) reduces to varying coefficient models (VCMs), which have been applied to parsimoniously describe the data structure and to uncover scientific features, see Cai, Fan and Li (2000); Fan and Zhang (2008); Gu and Liang (2014); Hastie and Tibshirani (1993); Li et al (2013); Wang and Xia (2009); Wei et al (2011); Yuan et al (2013); Zhu et al (2012).…”

Section: Introductionmentioning

confidence: 99%

Semiparametric quantile estimation for varying coefficient partially linear measurement errors models

Zhang¹,

Zhou²,

Cui³

et al. 2018

Braz. J. Probab. Stat.

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We study varying coefficient partially linear models when some linear covariates are error-prone, but their ancillary variables are available. After calibrating the error-prone covariates, we study quantile regression estimates for parametric coefficients and nonparametric varying coefficient functions, and we develop a semiparametric composite quantile estimation procedure. Asymptotic properties of the proposed estimators are established, and the estimators achieve their best convergence rate with proper bandwidth conditions. Simulation studies are conducted to evaluate the performance of the proposed method, and a real data set is analyzed as an illustration.

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“…Nonstationarity is a very important empirical feature in many economic and financial time series. Over the past decade, there has been great interests in nonparametric and semiparametric models with non stationary covariates, existing literature includes Cai, Li, and Park (2009), Chan and Wang (2015), Chen, Fang, and Li (2015), Chen, Gao, and Li (2012), Dong, Gao, and Tjøstheim (2016), Gu and Liang (2014), Gao and Phillips (2013), Juhl and Xiao (2005), Karlsen, Myklebust, and Tjøstheim (2007), Karlsen and Tjostheim (2001), Liang, Lin, and Hsiao (2015), Li et al (2017), Sun, Cai, and Li (2013), Sun and Li (2011), Wang (2014), Wang (2015), Wang and Phillips (2009a), Wang and Phillips (2009b), Wang and Phillips (2016), Xiao (2009), Zhou and Lin (2018). As we know, compared with nonparametric regression model, semiparametric regression models have the advantage of attenuating the problem of "curse of dimensionality. "…”

Section: Introductionmentioning

confidence: 99%

Adaptive estimation for varying coefficient models with nonstationary covariates

Zhou

2018

Communications in Statistics - Theory and Methods

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In this paper, the adaptive estimation for varying coefficient models proposed by Chen, Wang, and Yao (2015) is extended to allowing for non stationary covariates. The asymptotic properties of the estimator are obtained, showing different convergence rates for the integrated covariates and stationary covariates. The nonparametric estimator of the functional coefficient with integrated covariates has a faster convergence rate than the estimator with stationary covariates, and its asymptotic distribution is mixed normal. Moreover, the adaptive estimation is more efficient than the least square estimation for non normal errors. A simulation study is conducted to illustrate our theoretical results.

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Testing cointegration relationship in a semiparametric varying coefficient model

Cited by 7 publications

References 34 publications

Estimation of semi-varying coefficient models with nonstationary regressors

Estimation of semi-varying coefficient models with nonstationary regressors

Semiparametric quantile estimation for varying coefficient partially linear measurement errors models

Adaptive estimation for varying coefficient models with nonstationary covariates

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