A seasonal periodic long memory model for monthly river flows

Based on simple time series plots and periodic sample autocorrelations, we document that monthly river flow data displays long memory, in addition to pronounced seasonality. In fact, it appears that the long memory characteristics vary with the season. To describe these two properties jointly, we propose a seasonal periodic long memory model and fit it to the well-known Fraser river data (to be obtained from Statlib at http://lib.stat.cm.edu/datasets/). We provide a statistical analysis and provide impulse response functions to show that shocks in certain months of the year have a longer lasting impact than those in other months.

[1]  J. Beran A Goodness‐Of‐Fit Test for Time Series with Long Range Dependence , 1992 .

[2]  Marcello Pagano,et al.  On Periodic and Multiple Autoregressions , 1978 .

[3]  Venkata K. Jandhyala,et al.  A search for the source of the nile's change-points , 1991 .

[4]  S. Porter-Hudak An Application of the Seasonal Fractionally Differenced Model to the Monetary Aggregates , 1990 .

[5]  A. I. McLeod DIAGNOSTIC CHECKING OF PERIODIC AUTOREGRESSION MODELS WITH APPLICATION , 1994 .

[6]  A. I. McLeod,et al.  Preservation of the rescaled adjusted range: 3. Fractional Gaussian noise algorithms , 1978 .

[7]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[8]  Mitsuhiro Odaki On the invertibility of fractionally differenced ARIMA processes , 1993 .

[9]  Marius Ooms,et al.  Inference and Forecasting for Fractional Autoregressive Integrated Moving Average Models, with an application to US and UK inflation , 1999 .

[10]  Aris Spanos,et al.  Statistical Foundations of Econometric Modelling , 1986 .

[11]  Keith W. Hipel,et al.  Forecasting annual geophysical time series , 1988 .

[12]  R. Dahlhaus Efficient Location and Regression Estimation for Long Range Dependent Regression Models , 1995 .

[13]  Fallaw Sowell Maximum likelihood estimation of stationary univariate fractionally integrated time series models , 1992 .

[14]  A. I. McLeod,et al.  Preservation of the rescaled adjusted range: 1. A reassessment of the Hurst Phenomenon , 1978 .

[15]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[16]  Mike Smith Search for the Source , 1999 .

[17]  Keith W. Hipel,et al.  Forecasting monthly riverflow time series , 1985 .

[18]  Jan Beran,et al.  Statistics for long-memory processes , 1994 .

[19]  G. C. Tiao,et al.  Hidden Periodic Autoregressive-Moving Average Models in Time Series Data, , 1980 .

[20]  Marius Ooms,et al.  A Package for Estimating, Forecasting and Simulating Arfima Models: Arfima package 1.0 for Ox , 1999 .

[21]  N. T. Kottegoda,et al.  Stochastic Modelling of Riverflow Time Series , 1977 .

[22]  Marius Ooms,et al.  Flexible Seasonal Long Memory and Economic Time Series , 1995 .

[23]  Helmut Lütkepohl,et al.  Introduction to multiple time series analysis , 1991 .

[24]  D. Cox,et al.  An Analysis of Transformations , 1964 .

[25]  Jurgen A. Doornik,et al.  Givewin: An Interface for Empirical Modelling , 1999 .

[26]  Siem Jan Koopman,et al.  Detecting shocks: Outliers and breaks in time series , 1997 .

[27]  Richard T. Baillie,et al.  Analysing inflation by the fractionally integrated ARFIMA–GARCH model , 1996 .

[28]  P. Franses,et al.  On Periodic Correlations between Estimated Seasonal and Nonseasonal Components in German and U.S. Unemployment , 1997 .

[29]  P. Robinson Efficient Tests of Nonstationary Hypotheses , 1994 .

[30]  David A. Dickey,et al.  Testing for Unit Roots in Seasonal Time Series , 1984 .

[31]  C. Granger,et al.  AN INTRODUCTION TO LONG‐MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING , 1980 .

[32]  A. V. Vecchia,et al.  Testing for periodic autocorrelations in seasonal time series data , 1991 .

[33]  Uwe Hassler,et al.  On the power of unit root tests against fractional alternatives , 1994 .

[34]  Jan Beran,et al.  Testing for a change of the long-memory parameter , 1996 .

[35]  Jan Beran,et al.  Maximum Likelihood Estimation of the Differencing Parameter for Invertible Short and Long Memory Autoregressive Integrated Moving Average Models , 1995 .

[37]  Philip Hans Franses,et al.  RECENT ADVANCES IN MODELLING SEASONALITY , 1996 .

[38]  Bonnie K. Ray,et al.  MODELING LONG‐MEMORY PROCESSES FOR OPTIMAL LONG‐RANGE PREDICTION , 1993 .

[39]  G. C. Tiao,et al.  An introduction to multiple time series analysis. , 1993, Medical care.

[40]  A. I. McLeod,et al.  Preservation of the rescaled adjusted range: 2. Simulation studies using Box‐Jenkins Models , 1978 .

[41]  John B. Carlin,et al.  On models and methods for Bayesian time series analysis , 1985 .

[42]  Byung Sam Yoo,et al.  Seasonal integration and cointegration , 1990 .

[43]  Marshall E. Moss,et al.  Autocorrelation structure of monthly streamflows , 1974 .

[44]  Philip Hans Franses,et al.  A periodic long memory model for quarterly UK inflation , 1997 .