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...

The complex of mathematical models of investigated indicators has been developed to study the dynamics of budget indicators, forecasttheir change and plan revenues and expenditures of state and local budgets appropriately. This is a linear dynamic model withconstant and variable coefficients,...

Trend extraction from time series is often performed by using the filter proposed by Leser (1961), also known as the Hodrick-Prescott filter. Practical problems arise, however, if the time series contains structural breaks (as produced by German unification for German time series, for instance),...

In this study we use hedonic models to measure the influence of noise nuisance on rents, costs and values of investment properties in Switzerland. Countrywide data is provided by institutional real estate investors. The effects are measured for aircraft noise, road traffic noise and railroad...

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 the time series contains structural breaks (such as produced by German unification for German time series, for...

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