EXACT LOCAL WHITTLE ESTIMATION IN LONG MEMORY TIME SERIES WITH MULTIPLE POLES

A generalization of the Exact Local Whittle estimator in Shimotsu and Phillips (2005, Annals of Statistics 33, 1890–1933) is proposed for jointly estimating all the memory parameters in general long memory time series that possibly display standard, seasonal, and/or other cyclical strong persistence. Consistency and asymptotic normality are proven for stationary, nonstationary, and noninvertible series, permitting straightforward standard inference of interesting hypotheses such as the existence of unit roots and equality of memory parameters at some or all seasonal frequencies, which can be used as a prior test for the application of seasonal differencing filters. The effects of unknown deterministic terms are also discussed. Finally, the finite sample performance is analyzed in an extensive Monte Carlo exercise and an application to an U.S. Industrial Production index.

Long-memory in asset returns and volatility: evidence from West Africa

This paper measures the degree of long-memory or long-range dependence in asset returns and volatility of two stock indices in Ghana and Nigeria. The presence of long-memory opens up opportunities for abnormal returns to be made by analyzing price history of a particular market. The authors employ the Hurst exponent to measure the degree of long-memory which is evaluated by a semiparametric method, the Local Whittle estimator. The findings show strong evidence of the presence of long-memory in both returns and volatility of the indices studied, suggesting that neither of the markets in Ghana and Nigeria is weak-form efficient

EXACT LOCAL WHITTLE ESTIMATION OF FRACTIONAL INTEGRATION WITH UNKNOWN MEAN AND TIME TREND

Recently, Shimotsu and Phillips (2005, Annals of Statistics 33, 1890–1933) developed a new semiparametric estimator, the exact local Whittle (ELW) estimator, of the memory parameter (d) in fractionally integrated processes. The ELW estimator has been shown to be consistent, and it has the same $N(0,{\textstyle{1 \over 4}})$ asymptotic distribution for all values of d, if the optimization covers an interval of width less than 9/2 and the mean of the process is known. With the intent to provide a semiparametric estimator suitable for economic data, we extend the ELW estimator so that it accommodates an unknown mean and a polynomial time trend. We show that the two-step ELW estimator, which is based on a modified ELW objective function using a tapered local Whittle estimator in the first stage, has an $N(0,{\textstyle{1 \over 4}})$ asymptotic distribution for $d \in (- {\textstyle{1 \over 2}},2)$ (or $d \in (- {\textstyle{1 \over 2}},{\textstyle{7 \over 4}})$ when the data have a polynomial trend). Our simulation study illustrates that the two-step ELW estimator inherits the desirable properties of the ELW estimator.