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Full-Text Articles in Social and Behavioral Sciences
Asymptotic Theory For Zero Energy Density Estimation With Nonparametric Regression Applications, Qiying Wang, Peter C.B. Phillips
Asymptotic Theory For Zero Energy Density Estimation With Nonparametric Regression Applications, Qiying Wang, Peter C.B. Phillips
Cowles Foundation Discussion Papers
A local limit theorem is given for the sample mean of a zero energy function of a nonstationary time series involving twin numerical sequences that pass to infinity. The result is applicable in certain nonparametric kernel density estimation and regression problems where the relevant quantities are functions of both sample size and bandwidth. An interesting outcome of the theory in nonparametric regression is that the linear term is eliminated from the asymptotic bias. In consequence and in contrast to the stationary case, the Nadaraya-Watson estimator has the same limit distribution (to the second order including bias) as the local linear …
Mean And Autocovariance Function Estimation Near The Boundary Of Stationarity, Liudas Giraitis, Peter C.B. Phillips
Mean And Autocovariance Function Estimation Near The Boundary Of Stationarity, Liudas Giraitis, Peter C.B. Phillips
Cowles Foundation Discussion Papers
We analyze the applicability of standard normal asymptotic theory for linear process models near the boundary of stationarity. The concept of stationarity is refined, allowing for sample size dependence in the array and paying special attention to the rate at which the boundary unit root case is approached using a localizing coefficient around unity. The primary focus of the present paper is on estimation of the mean, autocovariance and autocorrelation functions within the broad region of stationarity that includes near boundary cases which vary with the sample size. The rate of consistency and the validity of the normal asymptotic approximation …
Bootstrapping I(1) Data, Peter C.B. Phillips
Bootstrapping I(1) Data, Peter C.B. Phillips
Cowles Foundation Discussion Papers
A functional law for an I(1) sample data version of the continuous-path block bootstrap of Paparoditis and Politis (2001) is given. The results provide an alternative demonstration that continuous-path block bootstrap unit root tests are consistent under the null.