Discrete-time ARMA processes can be placed in a one-to-one correspondence with a set of continuous-time processes that are bounded in frequency by the Nyquist value of π radians per sample period. It is well known that, if data are sampled from a continuous process of which the maximum frequency exceeds the Nyquist value, then there will be a problem of aliasing. However, if the sampling is too rapid, then other problems will arise that will cause the ARMA estimates to be severely biased. The paper reveals the nature of these problems and it shows how they may be overcome. It is argued that the estimation of macroeconomic processes may be compromised by a failure to take account of their limits in frequency
In this paper a method for the rejection of frequency domain outliers is proposed. The algorithm is ...
Aliasing gives a lower bound for the sampling rate in ordinary spectral analysis of a time series. I...
In this thesis, we construct ARMA model for random periodic processes. We stress on the mixed period...
Discrete-time ARMA processes can be placed in a one-to-one correspondence with a set of continuous-t...
In the theory of stochastic differential equations, it is commonly assumed that the forcing function...
The problem of estimating continuous-domain autoregressive moving-average processes from sampled dat...
This paper treats identification of continuous-time output error (OE) models based on sampled data. ...
This paper explores the representation and estimation of mixed continuous time ARMA (autoregressive ...
In this paper is discussed how to estimate irregularly sampled continuous-time ARMA models in the fr...
This paper explores the representation and estimation of mixed continuous time ARMA (autoregressive ...
This paper derives exact discrete time representations for data generated by a continuous time autor...
This paper derives exact discrete time representations for data generated by a continuous time autor...
This paper addresses the issue of quantifying the frequency domain accuracy of ARMA spectral estimat...
International audienceWe consider the problem of reconstructing a wide sense stationary band-limited...
Abstract. There exists a widespread belief that a discrete series of samples does not contain enough...
In this paper a method for the rejection of frequency domain outliers is proposed. The algorithm is ...
Aliasing gives a lower bound for the sampling rate in ordinary spectral analysis of a time series. I...
In this thesis, we construct ARMA model for random periodic processes. We stress on the mixed period...
Discrete-time ARMA processes can be placed in a one-to-one correspondence with a set of continuous-t...
In the theory of stochastic differential equations, it is commonly assumed that the forcing function...
The problem of estimating continuous-domain autoregressive moving-average processes from sampled dat...
This paper treats identification of continuous-time output error (OE) models based on sampled data. ...
This paper explores the representation and estimation of mixed continuous time ARMA (autoregressive ...
In this paper is discussed how to estimate irregularly sampled continuous-time ARMA models in the fr...
This paper explores the representation and estimation of mixed continuous time ARMA (autoregressive ...
This paper derives exact discrete time representations for data generated by a continuous time autor...
This paper derives exact discrete time representations for data generated by a continuous time autor...
This paper addresses the issue of quantifying the frequency domain accuracy of ARMA spectral estimat...
International audienceWe consider the problem of reconstructing a wide sense stationary band-limited...
Abstract. There exists a widespread belief that a discrete series of samples does not contain enough...
In this paper a method for the rejection of frequency domain outliers is proposed. The algorithm is ...
Aliasing gives a lower bound for the sampling rate in ordinary spectral analysis of a time series. I...
In this thesis, we construct ARMA model for random periodic processes. We stress on the mixed period...