Long- versus medium-run identification in fractionally integrated VAR models

Fractionally Integrated VAR Models with a Fractional Lag Operator and Deterministic Trends: Finite Sample Identification and Two-step Estimation

Non - causality in VAR - ECM models with purely exogenous long - run paths *

We propose a canonical representation of the long run matrix, which can constitute a basis for non-causality testing. This representation requires to determine a sub-matrix rank, which can be done using a test procedure, whose properties are analysed with Monte Carlo experiments.  2000 Elsevier Science S.A. All rights reserved.

Non - causality in VAR - ECM models with purely exogeneous long - run paths *

We propose a canonical representation of the long run matrix, which can constitute a basis for non-causality testing. This representation requires to determine a sub-matrix rank, which can be done using a test procedure, whose properties are analysed with Monte-Carlo experiments.  2000 Elsevier Science S.A. All rights reserved.

The importance of long run structure for impulse response analysis in VAR models

This paper examines the finite sample accuracy of impulse response functions in VAR models when cointegration is present but not imposed in estimation. It finds that in typical applications there can be substantial biases at the short horizon, as well as the long. These results suggest that, in contrast to common practice, before conducting impulse response analysis researchers should analyse t...

Dierential Geometry of Autoregressive Fractionally Integrated Moving Average Models

The di erential geometry of autoregressive fractionally integrated moving average processes is developed. Properties of Toeplitz forms associated with the spectral density functions of these long memory processes are used to compute the geometric quantities. The role of these geometric quantities on the asymptotic bias of the maximum likelihood estimates of the model parameters and on the Bartl...