The effects of systematic sampling and temporal aggregation on discrete time long memory processes and their finite sample properties
Hwang, Soosung (1999) The effects of systematic sampling and temporal aggregation on discrete time long memory processes and their finite sample properties. Working Paper. University of Warwick: Warwick Business School Financial Econometrics Research Centre. (Working Papers Series).
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This study investigates the effects of varying sampling intervals on the long memory characteristics of certain stochastic processes. We find that although different sampling intervals do not affect the decay rate of discrete time long memory autocorrelation functions in large lags, the autocorrelation functions in short lags are affected significantly. The level of the autocorrelation functions moves upward for temporally aggregated processes and downward for systematically sampled processes, and these effects result in a bias in the long memory parameter. For the ARFIMA(0,d,0) process, the absolute magnitude of the long memory parameter, |d|, of the temporally aggregated process is greater than the |d| of the true process, which is greater than the |d| of the systematically sampled process. We also find that the true long memory parameter can be obtained if we use a decay rate that is not affected by different sampling intervals.
|Item Type:||Working or Discussion Paper (Working Paper)|
|Subjects:||Q Science > QA Mathematics|
|Divisions:||Faculty of Social Sciences > Warwick Business School > Financial Econometrics Research Centre
Faculty of Social Sciences > Warwick Business School
|Library of Congress Subject Headings (LCSH):||Stochastic processes, Sampling (Statistics), Time-series analysis|
|Series Name:||Working Papers Series|
|Publisher:||Warwick Business School Financial Econometrics Research Centre|
|Place of Publication:||University of Warwick|
|Status:||Not Peer Reviewed|
|Access rights to Published version:||Open Access|
|References:||Agiakloglou, C., P. Newbold, and M. Wohar (1993) Bias in an Estimator of the Fractional Difference Parameter. Journal of Time Series Analysis 14, 235-246. Chambers, M. (in press) Long Memory and Aggregation in Macroeconomic Time Series. International Economic Review. Chambers, M. (1996) The Estimation of Continuous Parameter Long-memory Time Series Models. Econometric Theory 12, 374-390. Cheung, Y. (1993) Long Memory in Foreign-Exchange Rates. Journal of Business and Economic statistics 11, 93-101. Cheung, Y., and F. Diebold (1994) On Maximum Likelihood Estimation of the Differencing Parameter of Fractionally-Integrated Noise with Unknown Mean. Journal of Econometrics 62, 301-316. Dahlhaus, R. (1989) Efficient Parameter Estimation for Self-Similar Processes. The Annals of Statistics 17, 1749-1766. Diebold, F. X., and G. D. Rudebusch (1989) Long Memory and Persistence in Aggregate Output. Journal of Monetary Economics 24, 189-209. Ding, Z., C. Granger, and R. F. Engle (1992) A Long memory Property of Stock Market Returns and a New Model. Discussion paper 92-21, Department of Economics, University of California, San Diego. Fox, R., and M. S. Taqqu (1986) Large-Sample Properties of Parameter Estimates for Strongly Dependent Stationary Gaussian Time Series. The Annals of Statistics 14, 517-532. Geweke, J., and S. Porter-Hudak (1983) The Estimation and Application of Long Memory Time Series Models. Journal of Time Series Analysis 4, 221-238 Gradshteyn, I. S., and I. M. Ryzhik (1994) Table of Integrals, Series, and Products. Fifth Edition, Academic Press. Granger, C., and R. Joyeux (1980) An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis 1, 15-29 Hauser, M. A. (1992) Long Range Dependence in International Output Series: A Reexamination. Working Paper, Department of Statistics, University of Economics and Business Administration. Hidalgo, J., and Y. Yajima (1996) Semiparametric Estimation of the Long-range Parameter. Unpublished manuscript. Hosking, J. (1981) Fractional Differencing. Biometrika 68, 165-76 Hurvich, C. M., and K. I. Beltrao (1993) Asymptotics for the Low-Frequency Ordinates of the Periodogram of Long-Memory Time Series. Journal of Time Series Analysis 14, 455-472. Hurvich, C. M., and B. K. Ray (1994) Estimation of the Memory Parameter for Nonstationary or Noninvertible Fractionally Integrated Processes. Journal of Time Series Analysis 16, 17-41. Lee, D. (1994) Asymptotic Theory for Long-Memory Time Series. Michigan State University PhD Dissertation. Mandelbrot, B., and J. Van Ness (1968) Fractional Brownian Motions, Fractional Noises and Applications. SIAM Review 10, 422-437 Mcleod, A. I., and K. W. Hipel (1978) Preservation of the Rescaled Adjusted range, 1, A Reassessment of the Hurst Phenomenon. Water Resource Research 14, 491-508. Robinson, P. M. (1994) Semiparametric Analysis of Long-memory Time Series. Annals of Statistics 22, 515-539. Robinson, P. M. (1995) Log-periodogram Regression of Time Series with Long Range Dependence. Annals of Statistics 23, 1048-1072. Sowell, F. (1990) The Fractional Unit Root Distribution. Econometrica 58, 495-505. Sowell, F. (1992a) Maximum Likelihood Estimation of Stationary Univariate Fractionally Integrated Time Series Models. Journal of Econometrics 53, 165-188 Sowell, F. (1992b) Modeling Long-Run Behavior with the Fractional ARIMA Model. Journal of Monetary Economics 29, 277-302.|
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