For Vector Autoregressive models, the problem of dimensionality, associated with an increasing dimension of the model, can affect the power of noncausality tests. In this paper, by a Monte Carlo study, we analyze the impact of high dimensionality on the power of noncausality test and we proposed a testing strategy that, under certain conditions, limit the negative effects of high dimensionality in the causality analysis.

Dimensionality problem in testing for non causality between time series. A partial solution / DI IORIO, Francesca; Triacca, U.. - (2004), pp. 911-918. (Intervento presentato al convegno COMPSTAT 2004).

Dimensionality problem in testing for non causality between time series. A partial solution

DI IORIO, FRANCESCA;
2004

Abstract

For Vector Autoregressive models, the problem of dimensionality, associated with an increasing dimension of the model, can affect the power of noncausality tests. In this paper, by a Monte Carlo study, we analyze the impact of high dimensionality on the power of noncausality test and we proposed a testing strategy that, under certain conditions, limit the negative effects of high dimensionality in the causality analysis.
2004
9783790815542
Dimensionality problem in testing for non causality between time series. A partial solution / DI IORIO, Francesca; Triacca, U.. - (2004), pp. 911-918. (Intervento presentato al convegno COMPSTAT 2004).
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/118201
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact