In second-order statistical inference, an interval estimate of autocorrelation is a convenient reporting form of a popular dependence measure. Construction of such a confidence band usually requires the estimation of long-run variance (LRV), a limiting variance in the central limit theorem. It is also a fundamental part of heteroscedasticity and autocorrelation-consistent (HAC) estimates for the variance of regression coefficients. For stationary processes, these questions are well investigated. However, real phenomena require non-stationary processes for modeling. The local-stationary model allows for the adaptation of the estimate from a stationary time series. In 2015 [Zha15], Zhao proposed a confidence band construction for local autoco...
The detection of long-range dependence in time series analysis is an important task to which this pa...
The detection of long-range dependence in time series analysis is an important task to which this pa...
It is shown that the sum of the sample autocorrelation function at lag h≥1 is always for any statio...
Strict stationarity is a common assumption used in the time series literature in order to derive asy...
We consider statistical inference in the presence of serial dependence. The main focus is on use of ...
This paper shows how the sampling theorem relates with the variations along time of the second order...
When studying a real-life time series, it is frequently reasonable to assume, possibly after a suita...
This paper deals with the estimation of the long-run variance of a stationary sequence. We extend th...
The sample autocorrelation function is defined by the mean lagged products (LPs) of random observati...
We propose an asymptotically distribution-free transform of the sample autocorrelations of residuals...
We propose an asymptotically distribution-free transform of the sample autocorrelations of residuals...
The classical regular and partial autocorrelation functions are powerful tools for stationary time s...
In this study we examine in covariance stationary time series the consequences of constructing confi...
The purpose of this paper is to receive a second order expansion of the t-statistic in AR(1) model i...
Abstract. A desirable property of an autocovariance estimator is to be robust to the pres-ence of ad...
The detection of long-range dependence in time series analysis is an important task to which this pa...
The detection of long-range dependence in time series analysis is an important task to which this pa...
It is shown that the sum of the sample autocorrelation function at lag h≥1 is always for any statio...
Strict stationarity is a common assumption used in the time series literature in order to derive asy...
We consider statistical inference in the presence of serial dependence. The main focus is on use of ...
This paper shows how the sampling theorem relates with the variations along time of the second order...
When studying a real-life time series, it is frequently reasonable to assume, possibly after a suita...
This paper deals with the estimation of the long-run variance of a stationary sequence. We extend th...
The sample autocorrelation function is defined by the mean lagged products (LPs) of random observati...
We propose an asymptotically distribution-free transform of the sample autocorrelations of residuals...
We propose an asymptotically distribution-free transform of the sample autocorrelations of residuals...
The classical regular and partial autocorrelation functions are powerful tools for stationary time s...
In this study we examine in covariance stationary time series the consequences of constructing confi...
The purpose of this paper is to receive a second order expansion of the t-statistic in AR(1) model i...
Abstract. A desirable property of an autocovariance estimator is to be robust to the pres-ence of ad...
The detection of long-range dependence in time series analysis is an important task to which this pa...
The detection of long-range dependence in time series analysis is an important task to which this pa...
It is shown that the sum of the sample autocorrelation function at lag h≥1 is always for any statio...