Monetary Politics in a Monetary Union: A Note on Common Agency with Rational Expectations

Is the politicisation of monetary policy in a currency union desirable? This paper shows that in a setting where political influence by national governments is modeled as a common agency game with rational expectations, the answer to this question crucially depends on whether the common central bank can commit to follow its policy.Common Agency, Political Pressures, European Monetary Union

Neural Network Ensembles for Time Series Prediction

Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predictive engine shifts from the historical auto-regression to modelling complex non-linear relationships between multidimensional features and the time series outputs. In order to exploit these time-disparate relationships for the improved time series forecasting, the system requires a flexible methodology of combining multiple prediction models applied to multiple versions of the temporal data under significant noise component and variable temporal depth of predictions. In reply to this challenge a composite time series prediction model is proposed which combines the strength of multiple neural network (NN) regressors applied to the temporally varied feature subsets and the postprocessing smoothing of outputs developed to further reduce noise. The key strength of the model is its excellent adaptability and generalisation ability achieved through a highly diversified set of complementary NN models. The model has been evaluated within NISIS Competition 2006 and NN3 Competition 2007 concerning prediction of univariate and multivariate time-series. It showed the best predictive performance among 12 competitive models in the NISIS 2006 and is under evaluation within NN3 2007 Competition

Nature-Inspired Learning Models

Intelligent learning mechanisms found in natural world are still unsurpassed in their learning performance and eficiency of dealing with uncertain information coming in a variety of forms, yet remain under continuous challenge from human driven artificial intelligence methods. This work intends to demonstrate how the phenomena observed in physical world can be directly used to guide artificial learning models. An inspiration for the new learning methods has been found in the mechanics of physical fields found in both micro and macro scale. Exploiting the analogies between data and particles subjected to gravity, electrostatic and gas particle fields, new algorithms have been developed and applied to classification and clustering while the properties of the field further reused in regression and visualisation of classification and classifier fusion. The paper covers extensive pictorial examples and visual interpretations of the presented techniques along with some testing over the well-known real and artificial datasets, compared when possible to the traditional methods

An Overview of Classifier Fusion Methods

A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance. As there is little theory of information fusion itself, currently we are faced with different methods designed for different problems and producing different results. This paper gives an overview of classifier fusion methods and attempts to identify new trends that may dominate this area of research in future. A taxonomy of fusion methods trying to bring some order into the existing “pudding of diversities” is also provided

Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems

Combining classifiers by majority voting (MV) has recently emerged as an effective way of improving performance of individual classifiers. However, the usefulness of applying MV is not always observed and is subject to distribution of classification outputs in a multiple classifier system (MCS). Evaluation of MV errors (MVE) for all combinations of classifiers in MCS is a complex process of exponential complexity. Reduction of this complexity can be achieved provided the explicit relationship between MVE and any other less complex function operating on classifier outputs is found. Diversity measures operating on binary classification outputs (correct/incorrect) are studied in this paper as potential candidates for such functions. Their correlation with MVE, interpreted as the quality of a measure, is thoroughly investigated using artificial and real-world datasets. Moreover, we propose new diversity measure efficiently exploiting information coming from the whole MCS, rather than its part, for which it is applied

An Overview of Classifier Fusion Methods

A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance. As there is little theory of information fusion itself, currently we are faced with different methods designed for different problems and producing different results. This paper gives an overview of classifier fusion methods and attempts to identify new trends that may dominate this area of research in future. A taxonomy of fusion methods trying to bring some order into the existing “pudding of diversities” is also provided

Which entrepreneurs expect to expand their businesses? Evidence from survey data in Lithuania

This paper presents an empirical study based on a survey of 399 small and medium size companies in Lithuania. Applying bivariate and ordered probit estimators, we investigate why some business owners intend to expand their firms, while others do not. Our main findings provide evidence that the characteristics of the owners matter. Those with higher education and ‘learning by doing’ attributes either through previous job experience or additional entrepreneurial experience are more likely to expand their businesses. In addition, the model implications include that the intentions to expand are correlated with exporting and with size of the enterprise: medium and small size companies are more likely to grow than micro enterprises and self-employed entrepreneurs. We also analyse the link between the main perceptions of constraints to business activities and growth expectations and find that the factors, which are perceived as main business barriers, are not necessary those, which are associated with low growth expectations. In particular, perceptions of both corruption and of inadequate tax systems are main barriers to growth.http://deepblue.lib.umich.edu/bitstream/2027.42/40109/3/wp723.pd