Time series modeling and decomposition
DOI:
https://doi.org/10.6092/issn.1973-2201/3597Abstract
The paper provides an overview of techniques and methods in time series modeling and decomposition with focus on the business cycle, models for seasonality, the moving holiday component, the trading-day component and the irregular component.References
ALEXANDROV, T.A.; BIANCONCINI, S.; DAGUM, E.B.; MAAS, P. AND MCELROY, S.T. (2010): “Review of some modern approaches to the problem of trend extraction”, Econometric Reviews, forthcoming.
BELL, W.R. (1984): “Signal Extraction for Non-stationary Time Series”, Annals of Statistics, 646-664.
BELL W.H., HILLMER, S.C., (1984): “Issues Involved with the Seasonal Adjustment of Economic Time Series”, Journal of Business and Economic Statistics, 2, 3, pp. 291-320.
BOLLERSLEV, T. (1986): “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 31:307-327.
BOX, G.E.P., JENKINS, G.M. (1970): Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, U.S.A (seconda edizione, 1976).
BOX, G.E.P., TIAO, G.C. (1975): “Intervention Analysis with Applications to Economic and Environmental Problems”, Journal of the American Statistical Association, 70, pp. 70-79.
BURMAN, J.P. (1980): “Seasonal adjustment by signal extraction”, Journal of the Royal Statistical Society, Series A, 143, pp. 321-337.
CAMPBEL, J.Y., LO A.W., AND MACKINLAY, A.C. (1997): The Econometrics of Financial Markets, Princeton University Press, New Jersey.
CHAAB, N., MORRY, M., DAGUM, E.B. (1999): “Further Results on Alternative Trend-Cycle Estimators for Current Economic Analysis”, Estadistica, 6, pp. 3-73.
CLEVELAND, R.B., CLEVELAND, W.S., MCRAE, J.E., TERPENNING, I.J. (1990): “A Seasonal Trend Decomposition Procedure Based on Loess”, Journal of Official Statistics, 6, pp. 3-73.
CLEVELAND, W.P., TIAO, G.C. (1976): “Decomposition of Seasonal Time Series: a Model for Census X11 Program”, Journal of the American Statistical Association, 71, pp. 581-587.
DAGUM, C AND DAGUM, E.B. (2006): “Stochastic and Deterministic trend Models”, Statistica, 3, 269-280.
DAGUM, E.B. (1980): The X-11-ARIMA Seasonal Adjustment Method, Statistics Canada, Ottawa, Canada.
DAGUM, E.B. (1982a): “Revisions of Time Varying Seasonal Filters”, Journal of Forecasting, 1, pp. 173-187.
DAGUM, E.B. (1982b): “The Effects of Asymmetric Filters of Seasonal Factor Revisions”, Journal of the American Statistical Association, 77, pp. 732-738.
DAGUM, E.B. (1982c): “Revisions of Seasonally Adjusted Data Due to Filter Changes”, Proceedings of the Business and Economic Section, American Statistical Association, pp. 39-45.
DAGUM, E.B. (1983): “Spectral Properties of the Concurrent and Forecasting Seasonal Linear Filters of the X11ARIMA Method”, The Canadian Journal of Statistics, 11, pp. 73-90.
DAGUM, E.B. (1988): The X11ARIMA/88 Seasonal Adjustment Method – Foundations and User’s Manual, Time-series research and analysis centre, Statistics Canada, Ottawa, Canada.
DAGUM, E.B. (1996): “A New Method to Reduce Unwanted Ripples and Revisions in Trend-Cycle Estimates from X11ARIMA”, Survey Methodology, 22, 1, pp. 77-83.
DAGUM, E.B. (2001): “Time Series: Seasonal Adjustment”, in International Encyclopedia of the Social and Behavioral Sciences, vol. II - Methodology, Fienberg, S. e Kadane, J. Editors, Elsevier Sciences Pub.
DAGUM, E.B. AND BIANCONCINI, S. (2006): “Local Polynomial Trend-cycle Predictors in Reproducing Kernel Hilbert Spaces for Current Economic Analysis. Anales de Economía Aplicada, pp. 1-22.
DAGUM, E.B AND BIANCONCINI, S. (2008): “The Henderson Smoother in Reproducing Kernel Hilbert Space”, Journal of Business and Economic Statistics, 26 (4), pp. 536-545.
DAGUM, E.B. AND BIANCONCINI, S. (2009): “Recent Developments in Short-term Trend Prediction for Real Time Analysis”, Proceedings of the American Statistical Association, Business and Economic Statistics Section, Washington D.C., August (invited paper).
DAGUM, E.B. AND BIANCONCINI, S. (2010): “A Unified Probabilistic View of Nonparametric Predictors via Reproducing Kernel Hilbert Spaces”, under review.
DAGUM, E.B. AND LUATI, A. (2000): “Predictive Performance of some Nonparametric Linear and Nonlinear Smoothers for Noisy data”, Statistica, Anno LX, vol. 4, pp. 635-654.
DAGUM, E.B. AND LUATI, A. (2009): “A Cascade Linear Filter to Reduce Revisions and False Turning Points in Real Time Trend- cycle Estimation”, Econometric Reviews, vol. 28, 1-3, pp. 40-59.
DAGUM, E.B., QUENNEVILLE, B., SUTRADHAR, B. (1992): “Trading-Day Variations Multiple Regression Models with Random Parameters”, International Statistical Review, 60, 1, pp. 57-73.
EHGLEN, J. (1998), ‘Distortionary effects of the optimal Hodrick–Prescott filter’, Economics Letters 61, 345-349.
ENGLE, R.F. (1982): “Heteroscedasticity with Estimates of Variance of United Kingdom Inflation”, Econometrica 50: 987-1008.
FINDLEY, D.F., MONSELL, B.C., BELL, W.R., OTTO, M.C., CHEN B. (1998): “New Capabilities and Methods of the X12-ARIMA Seasonal Adjustment Program”, Journal of Business and Economic Statistics, 16, 2.
GERSCH, W., KITAGAWA, G. (1983): “The Prediction of Time Series with Trends and Seasonalities”, Journal of Business and Economic Statistics, 1, pp. 253-263.
GOMEZ, V., MARAVALL, A. (1996): TRAMO-SEATS, Bank of Spain, Madrid.
HANNAN, E.J. (1967): “Measuring of Wandering Signal Amid Noise”, J. of Applied Probability, 4, 90-102.
HARVEY, A.C. (1981, 1993): Time Series Models, Harvester- Wheatsheaf, Great Britain.
HARVEY, A.C. (1985): “Trends and Cycles in Macroeconomic Time Series”, Journal of Business and Economic Statistics, 3, pp. 216-227.
HARVEY, A.C. (1989): Forecasting Structural Time Series Models and the Kalman Filter, Cambridge University Press, Cambridge, England.
HARVEY, A.C. AND JAEGER, A. (1993), Detrending, Stylized Facts and the Business Cycle, Journal of Applied Econometrics, 8, 231-247.
HENDERSON, R. (1916): “Note on Graduation by Adjusted Average”, Transaction of the Actuarial Society of America, 17, pp. 43-48.
HILLMER, S.C., TIAO, G.C. (1982): “An ARIMA Model Based Approach to Seasonal Adjustment”, Journal of the American Statistical Association, 77, pp. 63-70.
KAISER, R. AND MARAVALL A. (1999), “Estimation of the Business-Cycle: A Modified Hodrick-Prescott filter”, Spanish Economic Review, 1, 175-206.
KING, R.G. AND REBELO, S.T. (1993), “Low Frequency Filtering and Real Business Cycles”, Journal of Economic Dynamics and Control, 17, 207-233.
KITAGAWA, G., GERSCH, W. (1984): “A Smoothness Priors-State Space Modeling of Time Series with Trend and Seasonality”, Journal of the American Statistical Association, 79, pp. 378-389.
KOLMOGOROV, A.N., (1939), Sur l’interpolation et l’extrapolation des suites stationnairres: C.R. Acad.Sci., 208, 2043-2045.
KOLMOGOROV A.N., (1941). “The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers, “Proceedings of the USSR Academy of Sciences 30: 299-303. (Russian), translated into English in Proceedings of the Royal Society of London, Series A: Mathematical and Physical Sciences 434 (1991): 9-13.
KOOPMAN, S.J., HARVEY, A.C., DOORNIK, J.A., SHEPHARD, N. (1998): Structural Time Series Analyser, STAMP (5.0), International Thomson Business Press, London, England.
HODRICK, R.J. AND PRESCOTT, E.C. (1997): “Post war U.S. Business Cycles: An Empirical Investigation”, Journal of Money, Credit and Banking,. 29, nº 1, 1-16.
HILLMER, S.C., TIAO, G.C. (1982): “An ARIMA Model Based Approach to Seasonal Adjustment”, Journal of the American Statistical Association, 77, pp. 63-70.
LADIRAY, D., QUENNEVILLE, B. (2001): “Seasonal Adjustment with the X11 Method”, Lecture Notes in Statistics no. 158, Springer-Verlag, New York.
MACAULAY, F.R. (1931): The Smoothing of Time Series, National Bureau of Economic Research, Washington, D.C.
MARAVALL, A. (1993). “Stochastic and Linear Trends: Models and Estimators”, Journal of Econometrics, 56, pp. 5-37.
PERSONS, W.M. (1919): “Indices of Business Conditions”, Review of Economic Statistics, 1, pp. 5-107.
SHISKIN, J., YOUNG, A.H., MUSGRAVE, J.C. (1967): “The X-11 Variant of Census Method II Seasonal Adjustment”, Technical Paper n. 15, Washington, D.C.: U.S. Bureau of Census.
SOBEL, E.L. (1967): “Prediction of Noise-distorted Multivariate Non-stationary Signal” J. of Applied Probability, 4, 330-342.
TONG, H. AND LIM, K. S. (1980) “Threshold Autoregression, Limit Cycles and Cyclical Data (with discussion)”, Journal of the Royal Statistical Society, Series B, 42, 245-292.
TONG H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press.
WHITTAKER, E.T. (1923), “On a New Method of Graduation”, Proceedings of the Edinburgh Mathematical Society 41, 63-75.
WHITTAKER, E.T. AND ROBINSON, G. (1924), Calculus of Observations: a Treasure on Numerical Calculations, Blackie and Son, London.
WIENER, N. (1949): Extrapolation, Interpolation and Smoothing of Stationary Time Series, John Wiley and Sons, New York.
WHITTLE, P. (1963): Prediction and regulation by linear least-square methods, English Universities Press, London.
WOLD, H.O. (1938): A Study in the Analysis of Stationary Time Series, Almquist and Wicksell, Uppsala, (second edition 1954).
YOUNG, A.H. (1965): “Estimating Trading-Day Variation in Monthly Economic Time Series”, Technical Paper 12, Washington, D.C.: U.S. Bureau of Census.
ZELLNER, A. HONG,C. AND MIN, C. (1991): “Forecasting Turning Points in International Output Growth Rates using Bayesian Exponentially Weighted Autoregression, Time Varying Parameters and Pooling Techniques”, Journal of Econometrics, 48, 275-304.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2010 Statistica
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
