Structural break or long memory: An empirical survey on daily rainfall data sets across Malaysia

Hydrology and Earth System Sciences

Volumn 17, Issue 4, 2013, Pages 1311-1318

  Yusof, F ^a   Kane, I L ^a   Yusop, Z ^a  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOCORRELATION FUNCTIONS; CUMULATIVE SUMS; EMPIRICAL SURVEYS; FRACTIONAL PARAMETERS; ORDINARY LEAST SQUARES; ORIGINAL SERIES; OTHER PROPERTIES; STRUCTURAL BREAK;

EARTH SCIENCES; HYDROLOGY;

RAIN;

DATA SET; DIURNAL VARIATION; EMPIRICAL ANALYSIS; LEAST SQUARES METHOD; RAINFALL; REGIONAL CLIMATE;

MALAYSIA;

EID: 84884948295     PISSN: 10275606     EISSN: 16077938     Source Type: Journal    
DOI: 10.5194/hess-17-1311-2013     Document Type: Article

References (43)

1. Alptekin, N.: Long memory analysis of USD/trial exchange rate, Int. J. Soc. Sci., 1, 111-112, 2006.
2. Berkes, I., Horvath, L., Kokoszka, P., and Shao, Q.: On discriminating between long-range dependence and changes in mean, Ann. Stat., 34, 1140-1165, 2006. (Pubitemid 44231159)
3. Brath, A., Castellarin, A., and Montanari, A.: Detecting non stationarity in extreme rainfall data observed in Northern Italy, in: Proceedings of EGS - Plinius Conference on Mediterranean Storms, Maratea, 219-231, 1999.
4. Caporale, G. M. and Gil-Alana, L. A.: Long-run and Cyclical Dynamics in the US Stock Market, Economics Series, 155, 1-29, ISSN: 1605-7996, 2004.
5. Cappelli, C. and Angela, D.: Long memory and structural break analysis of environmental time series, available at: http://www.sis-statistica.it/files/ pdf/atti/Spontanee%202006\203-206.pdf (last access: 30 October 2012), 2006.
6. Cheung, Y. W.: Tests for fractional integration: a Monte Carlo investigation, J. Time Ser. Anal., 14, 331-345, 1993.
7. Diebold, F. X. and Inoue, A.: Long memory and regime switching, J. Econ., 105, 131-159, 2001. (Pubitemid 33645289)
8. Dueker, M. J. and Asea, P. K.: Non-Monotonic Long Memory Dynamics in Black-Market Exchange Rates, The Federal Reserve Bank of St. Louis, Working Paper 1995-003A, available at: http://research.stlouisfed.org/wp/1995/95-003.pdf (last access: 28 September 2012), 1-24, 1995.
9. Galeano, P.: The use of cumulative sums for detection of change points in the rate parameter of a Poisson Process, Journal Computational Statistics & Data Analysis archive, 51, 6151-6165, 2007.
10. Geweke, J. and Porter-Hudak, S.: The estimation and application of long memory time series models, J. Time Ser. Anal., 4, 221-237, 1983.
11. Gil-Alana, L. A.: Persistence and Time Trends in the Temperatures in Spain, Adv. Meteorol., 2009, 415290, doi:10.1155/2009/415290, 2009.
12. Gil-Alana, L. A.: U.K. Rainfall Data: A Long-Term Persistence Approach, J. Appl. Meteorol. Clim., 51, 1904-1913, 2012.
13. Granger, C. W. J. and Ding, Z.: Varieties of long memory models, J. Econometrics, 73, 61-77, 1996. (Pubitemid 126373956)
14. Granger, C. W. J. and Hyung, N.: Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns, J. Empir. Financ., 11, 213-228, 2004.
15. Granger, C. W. J. and Joyeux, R.: An introduction to long-range time series models and fractional differencing, J. Time Ser. Anal., 1, 15-30, 1980.
16. Hosking, J. R. M.: Fractional differencing, Biometrika, 68, 165-176, 1981.
17. Hosking, J. R. M.: Modeling persistence in hydrological time series using fractional differencing, Water Resour. Res., 20, 1898-1908, doi:10.1029/WR020i012p01898, 1984.
18. Hurst, H. E.: Long term storage capacities of reservoirs, Trans. Am. Soc. Civ. Eng., 116, 776-808, 1951.
19. Hurvich, C. M. and Deo, R. S.: Plug-in selection of the number of frequencies in regression estimates of the memory parameter of a long memory time series, J. Time Ser. Anal., 3, 1-3, 1998.
20. Janssen, J. and Laatz, W.: Statistische Datenanalyse mit SPSS fur Windows, 2nd Edn., Springer-Verlag, Heidelberg, 636 pp., 1997.
21. Koscielny-Bunde, E., Kantelhardt, J. W., Braun, P., Bunde, A., and Havlin, S.: Long-term persistence and multifractality of river runoff records: Detrended fluctuation studies, J. Hydrol., 322, 120-137, 2006.
22. Koutsoyiannis, D.: The Hurst phenomenon and fractional Gaussian noise made easy, Hydrolog. Sci. J., 47, 573-595, 2002.
23. Koutsoyiannis, D.: Climate change, the Hurst phenomenon, and hydrological statistics, Hydrolog. Sci. J., 48, 3-24, 2003.
24. Koutsoyiannis, D.: Uncertainty, entropy, scaling and hydrological stochastics, 1, Marginal distributional properties of hydrological processes and state scaling, Hydrolog. Sci. J., 50, 381-404, 2005a.
25. Koutsoyiannis, D.: Uncertainty, entropy, scaling and hydrological stochastics, 2, Time dependence of hydrological processes and time scaling, Hydrolog. Sci. J., 50, 405-426, 2005b.
26. Koutsoyiannis, D.: Nonstationarity versus scaling in hydrology, J. Hydrol., 324, 239-254, 2006.
27. Langousis, A. and Koutsoyiannis, D.: A stochastic methodology for generation of seasonal time series reproducing overyear scaling behaviour, J. Hydrol., 322, 138-154, 2006.
28. Mahdi, O. and Petra, S.: A Preliminary Assessment of Characteristics and Long-term Rainfall Variability of Rainfall in Ethiopia- Basis for Sustainable Land Use and Resources Management, Conference on International Agricultural Research for Development, Witzenhausen, 9-11 October, available at: http://www. tropentag.de/2002/abstracts/full/11.pdf (last access: 30 October 2012), 2002.
29. Montanari, A., Rosso, R., and Taqqu, M. S.: Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation, and simulation,Water Resour. Res., 33, 1035-1044, doi:10.1029/97WR00043, 1997.
30. Mudelsee, M.: Long memory of rivers from spatial aggregation, Water Resour. Res., 43, W01202, doi:10.1029/2006WR005721, 2007.
31. Ploberger, W. and Kramer, W.: The CUSUM test with OLS residuals, Econometrica, 60, 271-285, 1992.
32. Rao, A. R. and Bhattacharya, D.: Hypothesis testing for long-term memory in hydrological series, J. Hydrol., 216, 183-196, 1999. (Pubitemid 29141610)
33. Rea, W., Oxley, L., and Reale, M.: A New Procedure for Discriminating Between Long Memory and Shifting Means Alternatives, 18th World IMACS/MODSIM Congress, Cairns, Australia 13-17 July 2009, available at: http://mssanz.org.au/ modsim09 (last access: 22 January 2013), 2009.
34. Rybski, D., Bunde, A., Havlin, S., and von Storch, H.: Long-term persistence in climate and the detection problem, Geophys. Res. Lett., 33, L06718, doi:10.1029/2005GL025591, 2006.
35. Shao, X.: A simple test of changes in mean in the possible presence of long-range dependence, J. Time Ser. Anal., 32, 598-606, 2011.
36. Shao, X. and Zhang, X.: Testing for Change Points in Time Series, J. Am. Stat. Assoc., 105, 1228-1240, doi:10.1198/jasa.2010.tm10103, 2010.
37. Shimotsu, K.: Exact local whittle estimation of fractional integration with unknown mean and time trend, Department of Economics, University of Essex, 1-2, 2002.
38. Smith, A. D.: Level shifts and the illusion of long memory in economic time series, J. Bus. Econ. Stat., 23, 321-335, 2005.
39. Suhaila, J. and Abdul Aziz, J.: Fitting the statistical distribution for daily rainfall in peninsular Malaysia based on AIC criterion, J. Appl. Sci. Res., 4, 1846-1857, 2008.
40. Van Beusekom, D. P.: Forecasting & Detrending of Time Series Models, BWI-paper, Free University of Amsterdam, available at: www.few.vu.nl/nl/Images/ werkstuk-beusekom tcm38-91323. doc (last access: 1 December 2012), 2003.
41. Wang,W., van Gelder, P. H. A. J. M., and Vrijling, J. K.: Long memory properties of streamflow processes of the Yellow River. Proceedings of the International Conference on Water Economics, Statistics and Finance, 1, Rethymno-Crete, Greece, 481-490, 8-10 July 2005.
42. Wang, W., Van Gelder, P. H. A. J. M., Vrijling, J. K., and Chen, X.: Detecting long-memory: Monte Carlo simulations and application to daily streamflow processes, Hydrol. Earth Syst. Sci., 11, 851-862, doi:10.5194/hess-11-851-2007, 2007. (Pubitemid 46226149)
43. Zeileis, A.: p Values and Alternative Boundaries for CUSUM Tests, available at: http://www.wu-wien.ac.at/am, 1-16, 2000.