This paper studies the estimation of time series regression when both regressors and disturbances have long memory. In contrast with the frequency domain estimation as in Robinson and Hidalgo (1997), we propose to estimate the same regression model with discrete wavelet transform (DWT) of the original series. Due to the approximate de-correlation property of DWT, the transformed series can be estimated using the traditional least squares techniques. We consider both the ordinary least squares and feasible generalized least squares estimator. Finite sample Monte Carlo simulation study is performed to examine the relative efficiency of the wavelet estimation.Discrete Wavelet Transform
ACL-3International audienceIn this article, we propose two new semiparametric estimators in the wave...
This thesis deals with the applications of wavelet theory to time series data. We first focus on sta...
We consider the problem of testing for homogeneity of variance in a time series with long memory str...
This paper studies the estimation of time series regression when both regressors and disturbances ha...
Long memory models have received a significant amount of attention in the theoretical literature as ...
Semi-parametric estimation methods of the long-memory exponent of a time series have been studied in...
International audienceLong-memory noise is common to many areas of signal processing and can serious...
Two wavelet based estimators are considered in this paper for the two parameters that characterize l...
The objective of this dissertation is to study ways of modeling a long memory process using wavelet ...
Summary. We present and study the performance of the semiparametric wavelet estimator for the long{m...
In this paper we consider trend to be smooth deterministic changes over long scales, and tackle the ...
There have been a number of papers written on semi-parametric estimation methods of the long-memory ...
In the general setting of long-memory multivariate time series, the long-memory characteristics are ...
ACL-3International audienceIn this article, we propose two new semiparametric estimators in the wave...
This thesis deals with the applications of wavelet theory to time series data. We first focus on sta...
We consider the problem of testing for homogeneity of variance in a time series with long memory str...
This paper studies the estimation of time series regression when both regressors and disturbances ha...
Long memory models have received a significant amount of attention in the theoretical literature as ...
Semi-parametric estimation methods of the long-memory exponent of a time series have been studied in...
International audienceLong-memory noise is common to many areas of signal processing and can serious...
Two wavelet based estimators are considered in this paper for the two parameters that characterize l...
The objective of this dissertation is to study ways of modeling a long memory process using wavelet ...
Summary. We present and study the performance of the semiparametric wavelet estimator for the long{m...
In this paper we consider trend to be smooth deterministic changes over long scales, and tackle the ...
There have been a number of papers written on semi-parametric estimation methods of the long-memory ...
In the general setting of long-memory multivariate time series, the long-memory characteristics are ...
ACL-3International audienceIn this article, we propose two new semiparametric estimators in the wave...
This thesis deals with the applications of wavelet theory to time series data. We first focus on sta...
We consider the problem of testing for homogeneity of variance in a time series with long memory str...