A vector time series model with long-memory dependence is introduced. It is assumed that, at each time point, the observations are equi-correlated. The model is based on a fractionally differenced autoregressive process (long-memory) adjoined to a Gaussian sequence with constant autocorrelation. The maximum likelihood estimators for the parameters in the model are derived and their asymptotic distributions are obtained.Time series Long-memory dependence Maximum likelihood estimation Asymptotic inference
International audienceIn this paper we discuss the properties of most important estimators of long-r...
In this paper, we extend the well-known Sims, Stock and Watson (SSW)(Sims et al. 1990; Econometrica ...
[[abstract]]We develop an asymptotic theory for the first two sample moments of a stationary multiva...
This chapter reviews semiparametric methods of inference on different aspects of long memory time se...
This chapter reviews semiparametric methods of inference on different aspects of long memory time s...
This article revises semiparametric methods of inference on different aspects of long mem-ory time s...
Abstract This paper deals with the problem of estimating the unknown parameters in a long-memory pro...
An exact maximum likelihood method is developed for the estimation of parameters in a non-Gaussian n...
This paper proposes an M-estimator for the fractional parameter of stationary long-range dependent p...
We study problems of semiparametric statistical inference connected with long-memory covariance stat...
In this article we first revisit some earlier work on fractionally differenced white noise and corre...
This paper develops a general asymptotic theory for the estimation of strictly stationary and ergodi...
This paper develops a general asymptotic theory for the estimation of strictly stationary and ergodi...
This paper develops a general asymptotic theory for the estimation of strictly stationary and ergodi...
This paper considers estimation and inference in some general non lin-ear time series models which a...
International audienceIn this paper we discuss the properties of most important estimators of long-r...
In this paper, we extend the well-known Sims, Stock and Watson (SSW)(Sims et al. 1990; Econometrica ...
[[abstract]]We develop an asymptotic theory for the first two sample moments of a stationary multiva...
This chapter reviews semiparametric methods of inference on different aspects of long memory time se...
This chapter reviews semiparametric methods of inference on different aspects of long memory time s...
This article revises semiparametric methods of inference on different aspects of long mem-ory time s...
Abstract This paper deals with the problem of estimating the unknown parameters in a long-memory pro...
An exact maximum likelihood method is developed for the estimation of parameters in a non-Gaussian n...
This paper proposes an M-estimator for the fractional parameter of stationary long-range dependent p...
We study problems of semiparametric statistical inference connected with long-memory covariance stat...
In this article we first revisit some earlier work on fractionally differenced white noise and corre...
This paper develops a general asymptotic theory for the estimation of strictly stationary and ergodi...
This paper develops a general asymptotic theory for the estimation of strictly stationary and ergodi...
This paper develops a general asymptotic theory for the estimation of strictly stationary and ergodi...
This paper considers estimation and inference in some general non lin-ear time series models which a...
International audienceIn this paper we discuss the properties of most important estimators of long-r...
In this paper, we extend the well-known Sims, Stock and Watson (SSW)(Sims et al. 1990; Econometrica ...
[[abstract]]We develop an asymptotic theory for the first two sample moments of a stationary multiva...