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 -