TY - JOUR
T1 - A Comparative Note about Estimation of the Fractional Parameter under Additive Outliers
AU - Rodríguez, Gabriel
N1 - Publisher Copyright:
© 2016 Taylor & Francis Group, LLC.
PY - 2016/1/2
Y1 - 2016/1/2
N2 - In recent articles, Fajardo et al. (2009) and Reisen and Fajardo (2012) propose an alternative semiparametric estimator of the fractional parameter in ARFIMA models which is robust to the presence of additive outliers. The results are very interesting, however, they use samples of 300 or 800 observations which are rarely found in macroeconomics. In order to perform a comparison, I estimate the fractional parameter using the procedure of Geweke and Porter-Hudak (1983) augmented with dummy variables associated with the (previously) detected outliers using the statistic τd suggested by Perron and Rodríguez (2003). Comparing with Fajardo et al. (2009) and Reisen and Fajardo (2012), I found better results for the mean and bias of the fractional parameter when T = 100 and the results in terms of the standard deviation and the MSE are very similar. However, for higher sample sizes such as 300 or 800, the robust procedure performs better. Empirical applications for seven monthly Latin-American inflation series with very small sample sizes contaminated by additive outliers are discussed.
AB - In recent articles, Fajardo et al. (2009) and Reisen and Fajardo (2012) propose an alternative semiparametric estimator of the fractional parameter in ARFIMA models which is robust to the presence of additive outliers. The results are very interesting, however, they use samples of 300 or 800 observations which are rarely found in macroeconomics. In order to perform a comparison, I estimate the fractional parameter using the procedure of Geweke and Porter-Hudak (1983) augmented with dummy variables associated with the (previously) detected outliers using the statistic τd suggested by Perron and Rodríguez (2003). Comparing with Fajardo et al. (2009) and Reisen and Fajardo (2012), I found better results for the mean and bias of the fractional parameter when T = 100 and the results in terms of the standard deviation and the MSE are very similar. However, for higher sample sizes such as 300 or 800, the robust procedure performs better. Empirical applications for seven monthly Latin-American inflation series with very small sample sizes contaminated by additive outliers are discussed.
KW - ARFIMA Errors
KW - Additive Outliers
KW - Inflation
KW - Long Memory
KW - Semiparametric estimation
UR - http://www.scopus.com/inward/record.url?scp=84947753031&partnerID=8YFLogxK
U2 - 10.1080/03610918.2014.892322
DO - 10.1080/03610918.2014.892322
M3 - Article
AN - SCOPUS:84947753031
SN - 0361-0918
VL - 45
SP - 207
EP - 221
JO - Communications in Statistics: Simulation and Computation
JF - Communications in Statistics: Simulation and Computation
IS - 1
ER -