This paper considers the random coefficient autoregressive model with time-functional variance noises,hereafter the RCA-TFV model.We first establish the consistency and asymptotic normality of the conditional least sq...This paper considers the random coefficient autoregressive model with time-functional variance noises,hereafter the RCA-TFV model.We first establish the consistency and asymptotic normality of the conditional least squares estimator for the constant coefficient.The semiparametric least squares estimator for the variance of the random coefficient and the nonparametric estimator for the variance function are constructed,and their asymptotic results are reported.A simulation study is presented along with an analysis of real data to assess the performance of our method in finite samples.展开更多
A great deal of economic problems are related to detecting the stability of time series data,where the main interest is in the unit root test.In this paper,we consider the unit root testing problem with errors being l...A great deal of economic problems are related to detecting the stability of time series data,where the main interest is in the unit root test.In this paper,we consider the unit root testing problem with errors being long-memory processes with the LARCH structure.A new test statistic is developed by using the random weighted bootstrap method.It turns out that the proposed statistic has a chisquared distribution asymptotically regardless of the process being stationary or nonst at ionary,and with or without an intercept term.The simulation results show that the statistic has a desired finite sample performance in terms of both size and power.A real data application is also given relying on the inflation rate data of 17 countries.展开更多
In applications involving,e.g.,panel data,images,genomics microarrays,etc.,trace regression models are useful tools.To address the high-dimensional issue of these applications,it is common to assume some sparsity prop...In applications involving,e.g.,panel data,images,genomics microarrays,etc.,trace regression models are useful tools.To address the high-dimensional issue of these applications,it is common to assume some sparsity property.For the case of the parameter matrix being simultaneously low rank and elements-wise sparse,we estimate the parameter matrix through the least-squares approach with the composite penalty combining the nuclear norm and the l1norm.We extend the existing analysis of the low-rank trace regression with i.i.d.errors to exponentialβ-mixing errors.The explicit convergence rate and the asymptotic properties of the proposed estimator are established.Simulations,as well as a real data application,are also carried out for illustration.展开更多
In this paper,we consider the statistical inferences in a partially linear model when the model error follows an autoregressive process.A two-step procedure is proposed for estimating the unknown parameters by taking ...In this paper,we consider the statistical inferences in a partially linear model when the model error follows an autoregressive process.A two-step procedure is proposed for estimating the unknown parameters by taking into account of the special structure in error.Since the asymptotic matrix of the estimator for the parametric part has a complex structure,an empirical likelihood function is also developed.We derive the asymptotic properties of the related statistics under mild conditions.Some simulations,as well as a real data example,are conducted to illustrate the finite sample performance.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52338009)the National Science Fund for Distinguished Young Scholars(Grant No.52025085)+4 种基金the Graduate Research Innovation Project of Hunan Province(Grant No.CX20220952)Xiaohui Liu’s research is supported by the NSF of China(Grant No.11971208)the National Social Science Foundation of China(Grant No.21&ZD152)the Outstanding Youth Fund Project of the Science and Technology Department of Jiangxi Province(Grant No.20224ACB211003)the NSF of China(Grant No.92358303).
文摘This paper considers the random coefficient autoregressive model with time-functional variance noises,hereafter the RCA-TFV model.We first establish the consistency and asymptotic normality of the conditional least squares estimator for the constant coefficient.The semiparametric least squares estimator for the variance of the random coefficient and the nonparametric estimator for the variance function are constructed,and their asymptotic results are reported.A simulation study is presented along with an analysis of real data to assess the performance of our method in finite samples.
基金supported by the NNSF of China(Grant Nos.11971208 and 11601197)the NNSF of China(Grant No.61973145)+5 种基金the Outstanding Youth Fund Project of the Science and Technology Department of Jiangxi Province(Grant No.20224ACB211003)supported by the Science and Technology Research Project of Education Department of Jiangxi Province(Grant No.GJJ200545)the Postgraduate Innovation Project of Jiangxi Province(Grant No.YC2021–B124)NSSF of China(Grant No.21BTJ035)supported by the National Major Social Science Project of China(Grant No.21&ZD152)Natural Science Project of Jiangxi Provincial Department of Science and Technology(Grant No.jxsq2023201048)。
文摘A great deal of economic problems are related to detecting the stability of time series data,where the main interest is in the unit root test.In this paper,we consider the unit root testing problem with errors being long-memory processes with the LARCH structure.A new test statistic is developed by using the random weighted bootstrap method.It turns out that the proposed statistic has a chisquared distribution asymptotically regardless of the process being stationary or nonst at ionary,and with or without an intercept term.The simulation results show that the statistic has a desired finite sample performance in terms of both size and power.A real data application is also given relying on the inflation rate data of 17 countries.
基金supported by the NSF of China(Grant No.12201259)supported by NSF of China(Grant No.11971208)+7 种基金supported by the NSF of China(Grant No.12201260)Jiangxi Provincial NSF(Grant No.20224BAB211008)Jiangxi Provincial NSF(Grant No.20212BAB211010)Science and Technology research project of the Education Department of Jiangxi Province(Grant No.GJJ2200537)Science and Technology Research Project of the Education Department of Jiangxi Province(Grant No.GJJ200545)NSSF of China(Grant No.21&ZD152)NSSF of China(Grant No.20BTJ008)China Postdoctoral Science Foundation(Grant No.2022M711425)。
文摘In applications involving,e.g.,panel data,images,genomics microarrays,etc.,trace regression models are useful tools.To address the high-dimensional issue of these applications,it is common to assume some sparsity property.For the case of the parameter matrix being simultaneously low rank and elements-wise sparse,we estimate the parameter matrix through the least-squares approach with the composite penalty combining the nuclear norm and the l1norm.We extend the existing analysis of the low-rank trace regression with i.i.d.errors to exponentialβ-mixing errors.The explicit convergence rate and the asymptotic properties of the proposed estimator are established.Simulations,as well as a real data application,are also carried out for illustration.
基金supported by the NSF of China(Nos.11971208,11601197)the NSSF of China(Grant No.21&ZD152)+2 种基金the China Postdoctoral Science Foundation(Nos.2016M600511,2017T100475)the NSF of Jiangxi Province(Nos.2018ACB21002,20171ACB21030)the Post graduate Innovation Project of Jiangxi Province(No.YC2021CB124)。
文摘In this paper,we consider the statistical inferences in a partially linear model when the model error follows an autoregressive process.A two-step procedure is proposed for estimating the unknown parameters by taking into account of the special structure in error.Since the asymptotic matrix of the estimator for the parametric part has a complex structure,an empirical likelihood function is also developed.We derive the asymptotic properties of the related statistics under mild conditions.Some simulations,as well as a real data example,are conducted to illustrate the finite sample performance.
