Previous-30 treatments of multivariate non-causal time series have assumed stationarity. In this article, we consider integrated processes in a non-causal setting. We generalize the Johansen–Granger representation for causal vector autoregressive (VAR) models to allow for dependence on future errors and discuss how the parameters can be estimated. The asymptotic distribution of the trace statistic is also considered. Some Monte Carlo simulations are presented
Abstract. Vector autoregressive (VAR) models are capable of capturing the dynamic struc-ture of many...
For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA he...
The concept of a near-integrated vector random process is introduced. Such processes help us to work...
This paper introduces the notion of common non-causal features and proposes tools to detect them in ...
The standard linear Granger non-causality test is effective only when time series are stationary. In...
This paper introduces the notion of common non-causal features and proposes tools to detect them in ...
In the presented work vector autoregression (VAR) models of finite order are examined. The main part...
We propose methods for testing hypothesis of non-causality at various horizons, as defined in Dufour...
For non-stationary vector autoregressive models (var hereafter, or var with moving average, varma he...
An introduction to vector autoregressive (VAR) analysis is given with special emphasis on cointegrat...
Vector autoregression model VAR belongs to the most used multiple time series models mainly in field...
We analyze Granger causality (GC) testing in mixed-frequency vector autoregressions (MF-VARs) with p...
This dissertation consists of three chapters that contribute to different multivariate time series m...
For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA he...
The thesis deals with the concept of cointegration which represents appropriate tool in the analysis...
Abstract. Vector autoregressive (VAR) models are capable of capturing the dynamic struc-ture of many...
For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA he...
The concept of a near-integrated vector random process is introduced. Such processes help us to work...
This paper introduces the notion of common non-causal features and proposes tools to detect them in ...
The standard linear Granger non-causality test is effective only when time series are stationary. In...
This paper introduces the notion of common non-causal features and proposes tools to detect them in ...
In the presented work vector autoregression (VAR) models of finite order are examined. The main part...
We propose methods for testing hypothesis of non-causality at various horizons, as defined in Dufour...
For non-stationary vector autoregressive models (var hereafter, or var with moving average, varma he...
An introduction to vector autoregressive (VAR) analysis is given with special emphasis on cointegrat...
Vector autoregression model VAR belongs to the most used multiple time series models mainly in field...
We analyze Granger causality (GC) testing in mixed-frequency vector autoregressions (MF-VARs) with p...
This dissertation consists of three chapters that contribute to different multivariate time series m...
For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA he...
The thesis deals with the concept of cointegration which represents appropriate tool in the analysis...
Abstract. Vector autoregressive (VAR) models are capable of capturing the dynamic struc-ture of many...
For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA he...
The concept of a near-integrated vector random process is introduced. Such processes help us to work...