Season. Mathematica Packages for Seasonal Adjustment

Schlicht, Ekkehart (2017): Season. Mathematica Packages for Seasonal Adjustment.

Abstract

This Zip-Archive provides Mathematica packages and documentation for the seasonal adjustment method proposed by Schlicht and Pauly (1983) and Schlicht (1984) covering Mathematica versions 5 to 11. The method makes use of non-parametric splines. It decomposes a time series into a trend, a seasonal component, and an irregular component. The method combines the trend filter proposed by Leser (1961) (also known as the HP-Filter), the seasonal filter proposed by Schlicht and Pauly (1983) and the orthogonal parametrization proposed by Schlicht (1984). In contrast to prevailing methods, it is based on an explicit statistical model (state-space) and estimates the smoothing parameters by a maximum-likelihood method.

• Dokumententyp: Software
• Keywords: Seasonal adjustment; flexible seasonal adjustment; splines; non-parametric splines; state-space; HP filter; Mathematica
• Fakultät: Volkswirtschaft ; Volkswirtschaft > Munich Discussion Papers in Economics ; Volkswirtschaft > Munich Discussion Papers in Economics > Statistische Methoden ; Volkswirtschaft > Lehrstühle > Seminar für Theorie und Politik der Einkommensverteilung (aufgelöst)
• Themengebiete: 300 Sozialwissenschaften > 300 Sozialwissenschaft, Soziologie ; 300 Sozialwissenschaften > 330 Wirtschaft
• JEL Classification: C01, C14, C22
• Sprache: Englisch

Literaturliste

Leser, C. E. V. (1961): 'A Simple Method of Trend Construction.' Journal of the Royal Statistical Society Series B (Methodological) Vol. 23, pp. 91-107.

Ludsteck, Johannes (2005) HPFilter, Mathematica package, Wolfram Library Archive Mathsource.

Schlicht, Ekkehart and Pauly, Ralf (1983): 'Descriptive Seasonal Adjustment by Minimizing Perturbations.' Empirica No. 1, pp. 15 - 28.

Schlicht, Ekkehart (1984): 'Seasonal Adjustment in a Stochastic Model.' Statistische Hefte Vol. 25, pp. 1-12.

Schlicht, Ekkehart (2005): 'Estimating the Smoothing Parameter in the So-called Hodrick-Prescott Filter', Journal of the Japan Statistical Society, 35(1), 2005, pp. 99-119.

Schlicht, Ekkehart (2009): 'Trend Extraction from Time Series With Missing Observations and Structural Breaks, Journal of the Japan Statistical Society 2009, 38(2), 285-92.