A Comparative Note about Estimation of the Fractional Parameter under Additive Outliers

In recent articles, Fajardo et al. (2009 Fajardo Molinares, F., Reisen, V.A., Cribari-Neto, F. (2009). Robust estimation in long-memory processes under additive outliers. Journal of Statistical Planning and Inference 139:2511–2525.[Crossref], [Web of Science ®] , [Google Scholar]) 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 Geweke, J., Porter-Hudak, S. (1983). The estimation and application of long memory time series model. Journal of Time Series Analysis 4:221–238.[Crossref] , [Google Scholar]) augmented with dummy variables associated with the (previously) detected outliers using the statistic τ_d suggested by Perron and Rodríguez (2003 Perron, P., Rodríguez, G. (2003). Searching for additive outliers in nonstationary time series. Journal of Time Series Analysis 24(2):193–220.[Crossref], [Web of Science ®] , [Google Scholar]). Comparing with Fajardo et al. (2009 Fajardo Molinares, F., Reisen, V.A., Cribari-Neto, F. (2009). Robust estimation in long-memory processes under additive outliers. Journal of Statistical Planning and Inference 139:2511–2525.[Crossref], [Web of Science ®] , [Google Scholar]) 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.