Multiple input transfer function noise modelling in the time and frequency domain:empirical evidence from Monte Carlo simulations

In the present study we have evaluated two competing methods for estimation of the impulse response weights used in the identification of transfer function models:a time domain method involving biased regression techniques and a frequency domain method utilizing a discrete Fourier transform of the cross-covariance system of the transfer function model. The algorithms were implemented on a VAX-8800 computer at the Computing Center at Åbo Akademi. The evaluation of the competing methods was carried out by simulations of different transfer function noise model structures. The models are essentially the same as those of Edlund, but we have used a far greater number of replications in the cases tested. Furthermore, we have used actually identified and estimated autoregressive integrated moving-average models of the residuals in the identification procedure of impulse response weights, in contrast with Edlund who only used theoretical noise models in filtering the input and output series. After a shot discussion of the underlying theory, we present the procedures and results of the empirical testing.     

Estimation of unimodal densities based on the <Emphasis Type="Italic">FQ</Emphasis>-System

Based on an FQ-System for continuous unimodal distributions, which was introduced by Scheffner (1998), we propose a pure data-driven method for density estimation, which provides good results even for small samples. This procedure does not involve any problems or uncertainties as e.g. bandwidth selection for kernel density estimates.     

Outlier Robust Small‐Area Estimation Under Spatial Correlation

Modern systems of official statistics require the estimation and publication of business statistics for disaggregated domains, for example, industry domains and geographical regions. Outlier robust methods have proven to be useful for small‐area estimation. Recently proposed outlier robust model‐based small‐area methods assume, however, uncorrelated random effects. Spatial dependencies, resulting from similar industry domains or geographic regions, often occur. In this paper, we propose an outlier robust small‐area methodology that allows for the presence of spatial correlation in the data. In particular, we present a robust predictive methodology that incorporates the potential spatial impact from other areas (domains) on the small area (domain) of interest. We further propose two parametric bootstrap methods for estimating the mean‐squared error. Simulations indicate that the proposed methodology may lead to efficiency gains. The paper concludes with an illustrative application by using business data for estimating average labour costs in Italian provinces.     

Weather‐related Employment Subsidies as a Remedy for Seasonal Unemployment? Evidence from Germany

Many European countries try to reduce seasonal unemployment by subsidizing short‐time employment during the winter period. Despite such costly efforts, pronounced seasonal unemployment patterns continue to exist. This puts doubts on the effectiveness of such policy interventions. This paper provides a first empirical assessment of the effectiveness of different subsidy schemes by exploiting the institutional variation in a German subsidy scheme that applies to the construction sector and the variation in local weather and business cycle conditions across 20 years. The findings confirm that generous short‐time subsidies reduce individual lay‐off probabilities in the case of poor weather conditions. However, the link between weather conditions and seasonal lay‐offs is much less strong than expected, making planned capacity reductions the main suspect for causing seasonality in unemployment patterns.     

Designing Service Level Contracts for Supply Chain Coordination

Supply contracts are used to coordinate the activities of the supply chain partners. In many industries, service level‐based supply contracts are commonly used. Under such a contract, a company agrees to achieve a certain service level and to pay a financial penalty if it misses it. The service level used in our study refers to the fraction of a manufacturer's demand filled by the supplier. We analyze two types of service level‐based supply contracts that are designed by a manufacturer and offered to a supplier. The first type of contract is a flat penalty contract, under which the supplier pays a fixed penalty to the manufacturer in each period in which the contract service level is not achieved. The second type of contract is a unit penalty contract, under which a penalty is due for each unit delivered fewer than specified by the parameters of the contract. We show how the supplier responds to the contracts and how the contract parameters can be chosen, such that the supply chain is coordinated. We also derive structural results about optimal values of the contract parameters, provide numerical results, and connect our service level measures to traditional service level measures. The results of our analyses can be used by decision makers to design optimal service level contracts and to provide them with a solid foundation for contract negotiations.     

Unternehmensbewertung bei wertabhängiger und autonomer Finanzierungspolitik unter Berücksichtigung einer Kursgewinnbesteuerung

The German tax reform in 2009 entails the general taxation of realized capital gains. The objective of this paper is to investigate the effect of a differentiated taxation of dividend payments and capital gains on discounted cash flow valuations. We develop explicit und practical useful valuation formulas for the free cash flow approach in case of financing based on market values and the adjusted present value approach in case of autonomous financing. Furthermore it is shown how the required risk-adjusted cost of equity can be derived from a modified Tax-CAPM. Finally we discuss how the particularities resulting from the new German tax law can be taken into account in discounted cash flow valuations.     

Concurrent processing of heteroskedastic vector-valued mixture density models

We introduce a combined two-stage least-squares (2SLS)–expectation maximization (EM) algorithm for estimating vector-valued autoregressive conditional heteroskedasticity models with standardized errors generated by Gaussian mixtures. The procedure incorporates the identification of the parametric settings as well as the estimation of the model parameters. Our approach does not require a priori knowledge of the Gaussian densities. The parametric settings of the 2SLS_EM algorithm are determined by the genetic hybrid algorithm (GHA). We test the GHA-driven 2SLS_EM algorithm on some simulated cases and on international asset pricing data. The statistical properties of the estimated models and the derived mixture densities indicate good performance of the algorithm. We conduct tests on a massively parallel processor supercomputer to cope with situations involving numerous mixtures. We show that the algorithm is scalable.