TY - JOUR
T1 - Estimation of fractional integration in the presence of data noise
AU - Haldrup, Niels
AU - Nielsen, Morten Ørregaard
N1 - Funding Information:
The authors are very grateful for the comments and suggestions from three anonymous referees. The authors are grateful for financial support from the Danish Social Science Research Council (Grant nos. 24-02-0181 and 275-05-0199).
PY - 2007/3/1
Y1 - 2007/3/1
N2 - A comparative study is presented regarding the performance of commonly used estimators of the fractional order of integration when data is contaminated by noise. In particular, measurement errors, additive outliers, temporary change outliers, and structural change outliers are addressed. It occurs that when the sample size is not too large, as is frequently the case for macroeconomic data, then non-persistent noise will generally bias the estimators of the memory parameter downwards. On the other hand, relatively more persistent noise like temporary change outliers and structural changes can have the opposite effect and thus bias the fractional parameter upwards. Surprisingly, with respect to the relative performance of the various estimators, the parametric conditional maximum likelihood estimator with modelling of the short run dynamics clearly outperforms the semiparametric estimators in the presence of noise that is not too persistent. However, when a non-zero mean is allowed for, it may reverse the conclusion.
AB - A comparative study is presented regarding the performance of commonly used estimators of the fractional order of integration when data is contaminated by noise. In particular, measurement errors, additive outliers, temporary change outliers, and structural change outliers are addressed. It occurs that when the sample size is not too large, as is frequently the case for macroeconomic data, then non-persistent noise will generally bias the estimators of the memory parameter downwards. On the other hand, relatively more persistent noise like temporary change outliers and structural changes can have the opposite effect and thus bias the fractional parameter upwards. Surprisingly, with respect to the relative performance of the various estimators, the parametric conditional maximum likelihood estimator with modelling of the short run dynamics clearly outperforms the semiparametric estimators in the presence of noise that is not too persistent. However, when a non-zero mean is allowed for, it may reverse the conclusion.
KW - Fractional integration
KW - Long memory
KW - Measurement errors
KW - Outliers
KW - Structural change
UR - http://www.scopus.com/inward/record.url?scp=33846622469&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2006.02.005
DO - 10.1016/j.csda.2006.02.005
M3 - Journal article
AN - SCOPUS:33846622469
SN - 0167-9473
VL - 51
SP - 3100
EP - 3114
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
IS - 6
ER -