Band 13, Heft 2

In this paper a new method for estimation of all the parameters of a k-factor Gegenbauer process is developed using a broadband nonlinear least-squares regression technique in the frequency-domain, with similarities to a Whittle estimator. Simulation studies where the underlying distribution is symmetric suggest that while the new method may have a slightly lower level of accuracy than existing methods (Whittle, conditional sum-of-squares), it can improve the accuracy in determining the values for the short-memory parameters of highly skewed non-Gaussian data (e.g., χ 2 ), while having the added advantage of being evaluated considerably faster. In a supplementary addendum we provide some theoretical results under a Gaussian assumption.

This paper proposes an exact discrete time error correction model for co-integrated systems in continuous time and outlines a computationally efficient algorithm that leads to the Gaussian estimates of the model’s parameters. Its performance in estimation is assessed by contrasting our estimates with those obtained after applying Johansen’s discrete time approach to cointegrated systems. The data, for analysis, consist of two simulated systems; one comprised entirely of stock variables and another one formed by flow variables. In the results, we show that for the system with stock variables Johansen’s approach and ours perform similarly. For the system with flow variables, however, Johansen’s estimates show a persistent estimation bias with negligible improvements in larger samples, while ours yields a smaller bias that lowers as the sample size increases. As our model incorporates a moving average component in the error term that permits full dynamics, we argue that Johansen’s bias reflects the cost of ignoring aggregation in the specification.

Global sensitivity analysis is primarily used to investigate the effects of uncertainties in the input variables of physical models on the model output. This work investigates the use of global sensitivity analysis tools in the context of variable selection in regression models. Specifically, a global sensitivity measure is applied to a criterion of model fit, hence defining a ranking of regressors by importance; a testing sequence based on the ‘Pantula-principle’ is then applied to the corresponding nested submodels, obtaining a novel model-selection method. The approach is demonstrated on a growth regression case study, and on a number of simulation experiments, and it is found competitive with existing approaches to variable selection.

Currently, the methods used by producers of official statistics do not facilitate the seasonal and calendar adjustment of daily time series, even though an increasing number of series with daily observations are available. The aim of this paper is the development of a procedure to estimate and adjust for periodically recurring systematic effects and the influence of moving holidays in time series with daily observations. To STL based seasonal adjustment routine is combined with a RegARIMA model for the estimation of calendar and outlier effects. The procedure is illustrated and validated using a set of daily time series with different seasonal characteristics as well as simulated data. The developed procedure closes a gap by facilitating the seasonal and calendar adjustment of daily time series.