Mean squared error of the empirical best linear unbiased predictor in an orthogonal finite discrete spectrum linear regression model

The mean squared error (MSE) of the empirical best linear unbiased predictor in an orthogonal finite discrete spectrum linear regression model is derived and a comparison with the MSE of the best linear unbiased predictor in this model is made. It is shown that under weak conditions these two mean square errors are asymptotically the same.     

Development of spatial thinking abilities in engineering 3D modeling course aimed at lower secondary students

International Journal of Technology and Design Education - Spatial visualization ability is an important factor in a child’s cognitive development. Its development is affected by numerous...     

Monetary policy in an oil-dependent economy in the presence of multiple shocks

Review of World Economics - Russian monetary policy has been challenged by large and continuous private capital outflows and a sharp drop in oil prices during 2014. Both contributed to significant...     

SOA Adoption Phases

The paper argues that attitudes to SOA follow a typical hype cycle from Technological Trigger, Peak of Inflated Expectations, and a Trough of Disillusionment to the more recent realization that SOA is a concept that may offer certain benefits but has several limitations. The main research question studies how the attitude to SOA changes in various phases of the hype cycle, how the SOA implementation cycle and an increase in business process maturity (BPMa) are interconnected and which factors influence the transition between the hype cycle phases. The paper shows that an organization’s success with implementing SOA depends on its ability to match the SOA implementation with an increase in BPMa. The dual purpose of implementing SOA is shown in the first framework: to assure the coherence of IT assets and to assure business/IT alignment. In the second framework, the interconnection of SOA and BPMa and its role in transiting through the hype cycle phases is outlined. The findings are analyzed using a longitudinal case study of a large Slovenian company.     

The Role of Information Feedback in the Management Group Decision-Making Process Applying System Dynamics Models

The research project addresses the influence of feedback information on the decision process supported by the application of system dynamics models. A user-friendly application was developed and used in the experiment with decision groups. The participants were 174 undergraduate management science students. They had the task of determining the optimum business strategy by maximizing the multiple criteria function under three experimental conditions: a ₁) an indivIDual decision process without the support of a system dynamics model, a ₂) an indivIDual decision process supported by a system dynamics model, and a ₃) a decision process supported by a system dynamics model and subject interaction via computer mediation. The hypotheses that the indivIDual decision process supported by a system dynamics model yields higher Criteria Function values than one without a system dynamics model, as well as the decision process supported by both a system dynamics model and subject interaction yields higher Criteria Function values than one supported by a system dynamics model alone were confirmed.     

Adoption of the euro: Why is the Czech approach cautious?

When published, the Czech Euro-area Accession Strategy signalled a rather cautious approach to adopting the euro in comparison to the intentions of other EU acceding countries. The euro adoption was scheduled around 2010 and the ERM II was viewed only as a waiting room. The Czech strategy was attuned to specific features of the Czech economy. Although inflation and nominal interest rates converged to the EMU levels before EU entry, large fiscal deficits and the need for significant fiscal reform did not make it possible to meet the Maastrich criteria soon. Moreover, real convergence was viewed as a priority for the forthcoming years and, consequently, the strategy was aimed at maintaining nominal flexibility in order to cushion consequences of price and wage rigidities during the peak period of the catch-up process.     

What Do Central Banks Know about Inflation Factors?

We offer a novel methodology for assessing the quality of central bank monetary policy reports. We evaluate their economic content by comparing verbally reported inflation factors with factors identified from a simple new Keynesian model. Positive correlations indicate that the reported inflation factors were similar to the model-identified ones, marking high-quality inflation reports. Although sample bank reports on average identified inflation factors correctly, the degree of forward-looking reporting varied.