TY - JOUR
T1 - Semi-strong linearity testing in linear models with dependent but uncorrelated errors
AU - Boubacar Maïnassara, Yacouba
AU - Raïssi, Hamdi
N1 - Publisher Copyright:
© 2015 Elsevier B.V.
PY - 2015/8/1
Y1 - 2015/8/1
N2 - The covariance estimation of multivariate nonlinear processes is studied. The heteroscedasticity autocorrelation consistent (HAC) and White (1980) estimators are commonly used in the literature to take into account nonlinearities. Noting that the more general HAC estimation procedures may be sometimes viewed too sophisticated in applications, we propose tests for determining whether the simple White estimation could be used or if HAC estimation is necessary to ensure a correct statistical analysis of time series. The theoretical results are illustrated by mean of Monte Carlo experiments.
AB - The covariance estimation of multivariate nonlinear processes is studied. The heteroscedasticity autocorrelation consistent (HAC) and White (1980) estimators are commonly used in the literature to take into account nonlinearities. Noting that the more general HAC estimation procedures may be sometimes viewed too sophisticated in applications, we propose tests for determining whether the simple White estimation could be used or if HAC estimation is necessary to ensure a correct statistical analysis of time series. The theoretical results are illustrated by mean of Monte Carlo experiments.
KW - HAC matrix estimation
KW - White matrix estimation
UR - http://www.scopus.com/inward/record.url?scp=84928999264&partnerID=8YFLogxK
U2 - 10.1016/j.spl.2015.04.004
DO - 10.1016/j.spl.2015.04.004
M3 - Article
AN - SCOPUS:84928999264
SN - 0167-7152
VL - 103
SP - 110
EP - 115
JO - Statistics and Probability Letters
JF - Statistics and Probability Letters
ER -