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

Published online by Cambridge University Press:  30 September 2009

Abstract

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 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 asymptotic distribution for (or 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.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2009

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Footnotes

The author thanks the co-editor and three anonymous referees for helpful and constructive comments. The author thanks Peter C.B. Phillips and Morten Ø. Nielsen for helpful comments and the Cowles Foundation for hospitality during his stay from January 2002 to August 2003. This research was supported by ESRC under grant R000223629.

References

REFERENCES

Abadir, K.M., Distaso, W., & Giraitis, L. (2007) Nonstationarity-extended local Whittle estimation. Journal of Econometrics 141, 13531384.10.1016/j.jeconom.2007.01.020CrossRefGoogle Scholar
Adenstedt, R. (1974) On large-sample estimation for the mean of a stationary random sequence. Annals of Mathematical Statistics 2, 10951107.Google Scholar
Backus, D.K. & Zin, S.E. (1991) Long-memory inflation uncertainty: Evidence from the term structure of interest rates. Journal of Money, Credit, and Banking 25, 681700.Google Scholar
Canjels, E. & Watson, M.W. (1997) Estimating deterministic trends in the presence of serially correlated errors. Review of Economics and Statistics 79, 184200.10.1162/003465397556773CrossRefGoogle Scholar
Crato, N. & Rothman, P. (1994) Fractional integration analysis of long-run behavior for US macroeconomic time series. Economics Letters 45, 287291.10.1016/0165-1765(94)90025-6Google Scholar
Diebold, F.X. & Rudebusch, G.D. (1991) Is consumption too smooth? Long memory and the Deaton paradox. Review of Economics and Statistics 73, 19.10.2307/2109680CrossRefGoogle Scholar
Fisher, R.A. (1925) Theory of statistical estimation. Proceedings of the Cambridge Philosophical Society 22, 700725.10.1017/S0305004100009580CrossRefGoogle Scholar
Gil-Alaña, L.A. & Robinson, P.M. (1997) Testing of unit root and other nonstationary hypotheses in macroeconomic time series. Journal of Econometrics 80, 241268.10.1016/S0304-4076(97)00038-9CrossRefGoogle Scholar
Hassler, U. & Wolters, J. (1995) Long memory in inflation rates: International evidence. Journal of Business & Economic Statistics 13, 3745.Google Scholar
Haubrich, J.G. (1993) Consumption and fractional differencing: Old and new anomalies. Review of Economics and Statistics 75, 767772.10.2307/2110038Google Scholar
Henry, M. & Zaffaroni, P. (2003) The long range dependence paradigm for macroeconomics and finance. In Doukhan, P., Oppenheim, G., & Taqqu, M., eds., Long-Range Dependence: Theory and Applications, pp. 419438. Birkhauser.Google Scholar
Hurvich, C.M. & Chen, W.W. (2000) An efficient taper for potentially overdifferenced long-memory time series. Journal of Time Series Analysis 21, 155180.10.1111/1467-9892.00179Google Scholar
Janssen, P., Jureckova, J., & Veraverbeke, N. (1985) Rate of convergence of one- and two-step M-estimators with applications to maximum likelihood and Pitman estimators. Annals of Statistics 13, 12221229.Google Scholar
Kim, C.S. & Phillips, P.C.B. (2006) Log Periodogram Regression: The Nonstationary Case. Cowles Foundation Discussion paper 1587, Yale University.Google Scholar
Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., & Shin, Y. (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics 54, 159178.10.1016/0304-4076(92)90104-YCrossRefGoogle Scholar
LeCam, L. (1956) On the asymptotic theory of estimation and testing hypotheses. In Neyman, J., ed., Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability 1, pp. 129156. University of California Press.Google Scholar
Lobato, I.N. (1999) A semiparametric two-step estimator in a multivariate long memory model. Journal of Econometrics 90, 129153.Google Scholar
Lobato, I.N. & Velasco, C. (2000) Long memory in stock-market trading volume. Journal of Business & Economic Statistics 18, 410427.Google Scholar
Marinucci, D. & Robinson, P.M. (1999) Alternative forms of fractional Brownian notion. Journal of Statistical Planning and Reference 80, 111122.CrossRefGoogle Scholar
Maynard, A. & Phillips, P.C.B. (2001) Rethinking an old empirical puzzle: Econometric evidence on the forward discount anomaly. Journal of Applied Econometrics 16, 671708.CrossRefGoogle Scholar
Michelacci, C. & Zaffaroni, P. (2000) (Fractional) beta convergence. Journal of Monetary Economics 45, 129153.10.1016/S0304-3932(99)00045-8Google Scholar
Nelson, C.R. & Plosser, C.I. (1982) Trends and random walks in macroeconomic time series: Some evidence and implications. Journal of Monetary Economics 10, 139162.10.1016/0304-3932(82)90012-5CrossRefGoogle Scholar
Pfanzagl, J. (1974) Asymptotic optimum estimation and test procedures. In Hájek, J., ed., Proceedings of the Prague Symposium on Asymptotics, vol. 1, pp. 201272. Charles University.Google Scholar
Phillips, P.C.B. (2007) Unit root log periodogram regression. Journal of Econometrics 138, 104124.10.1016/j.jeconom.2006.05.017Google Scholar
Phillips, P.C.B. & Lee, C.C. (1996) Efficiency gains from quasi-differencing under nonstationarity. In Robinson, P.M. & Rosenblatt, M., eds., Essays in Memory of E.J. Hannan, pp. 300314. Springer.Google Scholar
Phillips, P.C.B. & Shimotsu, K. (2004) Local Whittle estimation in nonstationary and unit root cases. Annals of Statistics 32, 656692.10.1214/009053604000000139CrossRefGoogle Scholar
Robinson, P.M. (1988) The stochastic difference between econometric statistics. Econometrica 56, 531548.10.2307/1911699Google Scholar
Robinson, P.M. (1994) Efficient tests of nonstationary hypotheses. Journal of the American Statistical Association 89, 14201437.Google Scholar
Robinson, P.M. (1995a) Log-periodogram regression of time series with long range dependence. Annals of Statistics 23, 10481072.10.1214/aos/1176324636Google Scholar
Robinson, P.M. (1995b) Gaussian semiparametric estimation of long range dependence. Annals of Statistics 23, 16301661.Google Scholar
Robinson, P.M. (2005) The distance between rival nonstationary fractional processes. Journal of Econometrics 128, 283300.10.1016/j.jeconom.2004.08.015Google Scholar
Samarov, A. & Taqqu, M.S. (1988) On the efficiency of the sample mean in long memory noise. Journal of Time Series Analysis 9, 191200.10.1111/j.1467-9892.1988.tb00463.xCrossRefGoogle Scholar
Schotman, P. & van Dijk, H.K. (1991) On Bayesian routes to unit roots. Journal of Applied Econometrics 6, 387401.10.1002/jae.3950060407CrossRefGoogle Scholar
Shimotsu, K. & Phillips, P.C.B. (2005) Exact local Whittle estimation of fractional integration. Annals of Statistics 33, 18901933.10.1214/009053605000000309Google Scholar
Shimotsu, K. & Phillips, P.C.B. (2006) Local Whittle estimation of fractional integration and some of its variants. Journal of Econometrics 130, 209233.10.1016/j.jeconom.2004.09.014Google Scholar
Velasco, C. (1999) Gaussian semiparametric estimation of non-stationary time series. Journal of Time Series Analysis 20, 87127.10.1111/1467-9892.00127Google Scholar