Cayton, Peter Julian and Bersales, Lisa Grace (2012): Median-based seasonal adjustment in the presence of seasonal volatility.
Download (699kB) | Preview
Philippine seasonal time series data tends to have unstable seasonal behavior, called seasonal volatility. Current Philippine seasonal adjustment methods use X-11-ARIMA, which has been shown to be poor in the presence of seasonal volatility. A modification of the Census X-11 method for seasonal adjustment is devised by changing the moving average filters into median-based filtering procedures using Tukey repeated median smoothing techniques. To study the ability of the new procedure, simulation experiments and application to real Philippine time series data were conducted and compared to Census X-11-ARIMA methods. The seasonal adjustment results will be evaluated based on their revision history, smoothness and accuracy in estimating the non-seasonal component. The results of research open the idea of using robust nonlinear filtering methods as an alternative in seasonal adjustment when moving average filters tend to fail under unfavorable conditions of time series data.
|Item Type:||MPRA Paper|
|Original Title:||Median-based seasonal adjustment in the presence of seasonal volatility|
|Keywords:||Tukey Median Smoothing; Unstable Seasonality; Seasonal Filtering; Census X-11-ARIMA; Robust Filtering|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General
C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C82 - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data ; Data Access
C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes
C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C49 - Other
|Depositing User:||Peter Julian Cayton|
|Date Deposited:||07. Mar 2012 03:16|
|Last Modified:||21. Oct 2015 08:25|
Arce, Gonzalo R. (2005), Nonlinear Signal Processing: A Statistical Approach. Hoboken, New Jersey: John Wiley & Sons, Inc.
Bell, William R. and Steven C. Hillmer (2002), Issues Involved with the Seasonal Adjustment of Economic Time Series. Journal of Business and Economic Statistics, Vol. 20, No. 1, Twentieth Anniversary Commemorative Issue, pp. 98-127.
Bersales, Lisa Grace (2010), Enhancing Seasonal Adjustment in Philippine Time Series: Procedures under Seasonal Volatilities, Lecture 7, delivered in the Annual Bangko Sentral ng Pilipinas- University of the Philippines Professorial Chair Lectures at the Bangko Sentral ng Pilipinas, Malate Manila, 15-17 February 2010.
Bollerslev, Tim (1986), Generalized Autoregressive Conditional Heteroskedasticy. Journal of Econometrics Vol. 31, pp. 307-327.
Bouman, Charles A. (2010), Course Notes on Nonlinear Filtering for Digital Image Processing, Purdue University School of Electrical and Computer Engineering, Indiana, USA.
Branch, E. Raphael; and Lowell Mason (2006), Seasonal Adjustment in the ECI and the Conversion to NAICs and SOC, in Monthly Labor Review, April 2006, pp. 12-21.
Gallagher, Neal C. Jr.; and Gary L. Wise (1981), A Theoretical Analysis of the Properties of Median Filters, in IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-29, No. 6, December 1981, pp. 1138-1141.
Ghysels, Eric; Clive Granger and Pierre Siklos(1997), Seasonal Adjustment and Volatility Dynamics, CIRANO Working Papers.
Ghysels, Eric and Denise Osborn (2001), The Econometric Analysis of Seasonal Time Series. United Kingdom: Cambridge University Press.
Granger, C. W. J. (1979), Seasonality: Causation, Interpretation, and Implications, in Seasonal Analysis of Economic Time Series, ed. Arnold Zellner, Washington, D. C.: US Dept of Commerce, Bureau of Census, 33-46.
Higginson, John (1975), An F-Test for the Presence of Moving Seasonality When Using Census Method II-X-11Variant, Statistics Canada Publications.
Huber, William A. (2004), Tukey’s 3RSSH Smoother. A Microsoft Excel Software Add-in, Merion, PA.: Quantitative Decisions, Inc.
Hungarian Central Statistics Office (2007), Seasonal Adjustment Methods and Practices, Final Version 3.1, by Author.
Hyndman, Rob J., Anne B. Koehler, J. Keith Ord, and Ralph D. Snyder (2008), Forecasting with Exponential Smoothing: The State Space Approach. Germany: Springer-Verlag.
Lothian, J. and M. Morry (1978), A Set of Quality Control Statistics for the X-11-ARIMA Seasonal Adjustment Method, Statistics Canada.
National Statistical Coordination Board [NSCB] (2011), Technical Notes on the Computation of the Composite Leading Economic Indicator: The Philippines Leading Economic Indicators System (LEIS) Fourth Quarter 2011 Release. A Website Accessed on November 14, 2011, http://www.nscb.gov.ph/technotes/lei/lei_tech4q11.asp
Pierce, David A. (1980), A Survey of Recent Developments in Seasonal Adjustment, The American Statistician, Vol. 34, No. 3, pp. 125-134.
Quantitative Decisions © (2004), Smoothing, A Website accessed on January 3, 2011, http://www.quantdec.com/Excel/smoothing.htm.
Rabiner, Lawrence R.; Marvin R. Sambur, and Carolyn E. Schmidt (1975), A Nonlinear Smoothing Algorithm to Speech Processing, in IEEE Transactions in Acoustics, Speech, and Signal Processing, Vol. ASSP-23, No. 6, December 1975.
Redoblado, Jade Eric T. (2005), Seasonal Properties of Selected Philippine Economic Time Series, an Unpublished Thesis in Fulfillment of the Requirements of the Masters of Statistics Program, UP School of Statistics.
Singapore Department of Statistics (2006), Seasonal Adjustment of Economic Time Series, Information Paper on Economic Statistics.
Tukey, John W. (1977), Exploratory Data Analysis. Reading, Mass.: Addison-Wesley.
Tsay, Ruey S. (2002), Analysis of Financial Time Series, New York: John Wiley & Sons, Inc.