Open AccessAsymptotic inference results for multivariate long‐memory processes
Author(s) -
Dolado Juan J.,
Marmol Francesc
Publication year - 2004
Publication title -
the econometrics journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.861
H-Index - 36
eISSN - 1368-423X
pISSN - 1368-4221
DOI - 10.1111/j.1368-423x.2004.00126.x
Subject(s) - autoregressive model , mathematics , multivariate statistics , monte carlo method , inference , econometrics , statistics , computer science , artificial intelligence
Summary In this paper, we extend the well‐known Sims, Stock and Watson (SSW) (Sims et al. 1990; Econometrica 56 , 113–44), analysis on estimation and testing in vector autoregressive process (VARs) with integer unit roots and deterministic components to a more general set‐up where non‐stationary fractionally integrated (NFI) processes are considered. In particular, we focus on partial VAR models where the conditioning variables are NFI since this is the only finite‐lag VAR model compatible with such processes. We show how SSW's conclusions remain valid. This means that whenever a block of coefficients in the partial VAR can be written as coefficients on zero‐mean I (0) regressors in models including a constant term, they will have a joint asymptotic normal distribution. Monte Carlo simulations and an empirical application of our theoretical results are also provided.