Caiado, Jorge and Crato, Nuno (2005): Discrimination between deterministic trend and stochastic trend processes. Published in: Proceedings of the XIth International Conference on Applied Stochastic Models and Data Analysis : pp. 1419-1424.
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Most of economic and financial time series have a nonstationary behavior. There are different types of nonstationary processes, such as those with stochastic trend and those with deterministic trend. In practice, it can be quite difficult to distinguish between the two processes. In this paper, we compare random walk and determinist trend processes using sample autocorrelation, sample partial autocorrelation and periodogram based metrics.
|Item Type:||MPRA Paper|
|Original Title:||Discrimination between deterministic trend and stochastic trend processes|
|Keywords:||Autocorrelation; Classification; Determinist trend; Kullback-Leibler; Periodogram; Stochastic trend; Time series|
|Subjects:||C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C32 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C19 - Other
|Depositing User:||Jorge Caiado|
|Date Deposited:||09. Mar 2007|
|Last Modified:||13. Feb 2013 12:34|
Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Springer, New York. Caiado, J., Crato, N. and Peña, D. (2005). "A periodogram-based metric for time series classification", Computational Statistics & Data Analysis (forthcoming). Enders, W. (1995). Applied Econometric Time Series, Wiley, New York. Galeano, P. and Peña, D. (2000). "Multivariate analysis in vector time series", Resenhas, 4, 383-404. Kakizawa, Y., Shumway, R. H. and Taniguchi, M. (1998). "Discrimination and clustering for multivariate time series", Journal of the American Statistical Association, 93, 328-340. Maharaj, E. A. (2000). "Clusters of time series", Journal of Classification, 17, 297-314. Maharaj, E. A. (2002). "Comparison of non-stationary time series in the frequency domain", Computational Statistics & Data Analysis, 40, 131-141. Piccolo, D. (1990). "A distance measure for classifying ARIMA models", Journal of Time Series Analysis, 11, 152-164. Shaw, C. T. e King, G. P. (1992). "Using cluster analysis to classify time series", Physica D, 58, 288-298. Tong, H e Dabas, P. (1990). "Cluster of time series models: an example", Journal of Applied Statistics, 17, 187-198. Xiong, Y. e Yeung, D. (2004). "Time series clustering with ARMA mixtures", Pattern Recognition (in press).