Existing methods for data interpolation or backdating are either univariate or based on a very limited number of series, due to data and computing constraints that were binding until the recent past. Nowadays large datasets are readily available, and models with hundreds of parameters are fastly...

The decomposition of a given time series into trend, seasonal component, and irregular component is formulated as a minimization problem. The trend is chosen such that it is as smooth as possible; the seasonal component is chosen such that it exhibits a seasonal pattern as stable as possible; and...

The aim of this paper is to develop a model-based seasonal adjustment method which will yield the same decomposition formulas as the descriptive seasonal adjustment procedures proposed in Schlicht/Pauly (1984) and Schlicht (1981). Hence the duality between the descriptive and the model-based...

The seasonal adjustment method proposed by Schlicht (1981) can be viewed as a method that minimizes non-stochastic deviations (perturbations). This interpretation gives rise to a critique of the seasonality criterion used there. A new seasonality criterion is proposed that avoids these...

The paper discusses a new seasonality hypothesis which is one part of a weighted regression approach for the decomposition of a time series into a trend, a seasonal component and an irregular component. It is shown that there exists a regression formulation leading, as in the descriptive...

Pooling forecasts obtained from different procedures typically reduces the mean square forecast error and more generally improves the quality of the forecast. In this paper we evaluate whether pooling interpolated or backdated time series obtained from different procedures can also improve the...

Trend extraction from time series is often performed by using the filter proposed by Leser (1961), also known as the Hodrick-Prescott filter. A practical problem arises, however, when some data points are missing. This note proposes a method for coping with this problem.