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A Nonlinear Algorithm for Seasonal Adjustment in Multiplicative Component Decompositions
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Tucker S McElroy
Veröffentlicht/Copyright:
13. September 2010
We propose a new model-based, nonlinear method for seasonally adjusting time series in a multiplicative components model. The method seeks to reduce the bias inherent in linear model-based approaches, while at the same time preserving the flexibility of parametric methods. We discuss the problem of bias and the concept of recovery, and demonstrate the favorable properties of the proposed algorithm on several synthetic series.
Published Online: 2010-9-13
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Artikel in diesem Heft
- Article
- Skew-Normal Mixture and Markov-Switching GARCH Processes
- Covariate Measurement Error: Bias Reduction under Response-Based Sampling
- Detection of Stationarity in Nonlinear Processes: A Comparison between Structural Breaks and Three-Regime TAR Models
- Fundamental and Behavioural Drivers of Electricity Price Volatility
- Bayesian Estimation and Model Selection in the Generalized Stochastic Unit Root Model
- A Nonlinear Algorithm for Seasonal Adjustment in Multiplicative Component Decompositions
