Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets

This paper develops a flexible multi-dimensional assessment method for the comparison of different statistical-econometric techniques based on learning mechanisms with a view to analysing and forecasting regional labour markets. The aim of this paper is twofold. A first major objective is to explore the use of a standard choice tool, namely Multicriteria Analysis (MCA), in order to cope with the intrinsic methodological uncertainty on the choice of a suitable statistical-econometric learning technique for regional labour market analysis. MCA is applied here to support choices on the performance of various models -based on classes of Neural Network (NN) techniques-that serve to generate employment forecasts in West Germany at a regional/district level. A second objective of the paper is to analyse the methodological potential of a blend of approaches (NN-MCA) in order to extend the analysis framework to other economic research domains, where formal models are not available, but where a variety of statistical data is present. The paper offers a basis for a more balanced judgement of the performance of rival statistical tests

Neural Networks for Cross-Sectional Employment Forecasts: A Comparison of Model Specifications for Germany

In this paper, we present a review of various computational experiments – and consequent results – concerning Neural Network (NN) models developed for regional employment forecasting. NNs are widely used in several fields because of their flexible specification structure. Their utilization in studying/predicting economic variables, such as employment or migration, is justified by the ability of NNs of learning from data, in other words, of finding functional relationships – by means of data – among the economic variables under analysis. A series of NN experiments is presented in the paper. Using two data sets on German NUTS 3 districts (326 and 113 labour market districts in the former West and East Germany, respectively), the results emerging from the implementation of various NN models – in order to forecast variations in full-time employment – are provided and discussed In our approach, single forecasts are computed by the models for each district. Different specifications of the NN models are first tested in terms of: (a) explanatory variables; and (b) NN structures. The average statistical results of simulated out-of-sample forecasts on different periods are summarized and commented on. In addition to variable and structure specification, the choice of NN learning parameters and internal functions is also critical to the success of NNs. Comprehensive testing of these parameters is, however, limited in the literature. A sensitivity analysis is therefore carried out and discussed, in order to evaluate different combinations of NN parameters. The paper concludes with methodological and empirical remarks, as well as with suggestions for future research.neural networks, sensitivity analysis, employment forecasts, Germany

Forecasting Regional Employment in Germany by Means of Neural Networks and Genetic Algorithms

The aim of this paper is to develop and apply Neural Network (NN) models in order to forecast regional employment patterns in Germany. NNs are statistical tools based on learning algorithms with a distribution over a large amount of quantitative data. NNs are increasingly deployed in the social sciences as a useful technique for interpolating data when a clear specification of the functional relationship between dependent and independent variables is not available. In addition to traditional NN models, a further set of NN models will be developed in this paper, incorporating Genetic Algorithm (GA) techniques in order to detect the networks’ structure. GAs are computer-aided optimization tools that imitate natural biological evolution in order to find the solution that best fits the given case. Our experiments employ a data set consisting of a panel of 439 districts distributed over the former West and East Germany,. The West and East data sets have different time horizons, as employment information by district is available from 1987 and 1993 for West and East Germany, respectively. Separate West and East models are tested, before carrying out a unified experiment on the full data set for Germany. The above models are then evaluated by means of several statistical indicators, in order to test their ability to provide out- of-sample forecasts. A comparison between traditional and GAenhanced models is ultimately proposed. The results show that the West and East NN models perform with different degrees of precision, because of the different data sets’ time horizons.forecasting; neural networks; regional labour markets

A Rank-order Analysis of Learning Models for Regional Labor Market Forecasting

Using a panel of 439 German regions we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for regional employment growth. Because of relevant differences in data availability between the former East and West Germany, NN models are computed separately for the two parts of the country. The comparisons of the models and their ex-post forecasts have been carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance. Furthermore, the Friedman statistic is indifferent to the scale on which the data are measured. The evaluation of the ex-post forecasts suggests that NN models are generally able to correctly identify the fastest-growing and the slowest-growing regions, and hence predict rather well the correct ranking of regions in terms of their employment growth. The comparison among NN models – on the basis of several criteria – suggests that the choice of the variables used in the model may influence the model’s performance and the reliability of its forecasts.forecasts, regional employment, learning algorithms, rank order test

Mode space approach for tight-binding transport simulations in graphene nanoribbon field-effect transistors including phonon scattering

In this paper, we present a mode space method for atomistic non-equilibrium Green's function simulations of armchair graphene nanoribbon FETs that includes electron-phonon scattering. With reference to both conventional and tunnel FET structures, we show that, in the ideal case of a smooth electrostatic potential, the modes can be decoupled in different groups without any loss of accuracy. Thus, inter-subband scattering due to electron-phonon interactions is properly accounted for, while the overall simulation time considerably improves with respect to real-space, with a speed-up factor of 40 for a 1.5-nm-wide device. Such factor increases with the square of the device width. We also discuss the accuracy of two commonly used approximations of the scattering self-energies: the neglect of the off-diagonal entries in the mode-space expressions and the neglect of the Hermitian part of the retarded self-energy. While the latter is an acceptable approximation in most bias conditions, the former is somewhat inaccurate when the device is in the off-state and optical phonon scattering is essential in determining the current via band-to-band tunneling. Finally, we show that, in the presence of a disordered potential, a coupled mode space approach is necessary, but the results are still accurate compared to the real-space solution.Comment: 10 pages, 12 figures. Copyright (2013) American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physic

Boosting the voltage gain of graphene FETs through a differential amplifier scheme with positive feedback

We study a possible circuit solution to overcome the problem of low voltage gain of short-channel graphene FETs. The circuit consists of a fully differential amplifier with a load made of a cross-coupled transistor pair. Starting from the device characteristics obtained from self-consistent ballistic quantum transport simulations, we explore the circuit parameter space and evaluate the amplifier performance in terms of dc voltage gain and voltage gain bandwidth. We show that the dc gain can be effectively improved by the negative differential resistance provided by the cross-coupled pair. Contact resistance is the main obstacle to achieving gain bandwidth products in the terahertz range. Limitations of the proposed amplifier are identified with its poor linearity and relatively large Miller capacitance.Comment: 19 pages, 10 figure

Neural networks for regional employment forecasts: are the parameters relevant?

In this paper, we present a review of various computational experiments concerning neural network (NN) models developed for regional employment forecasting. NNs are nowadays widely used in several fields because of their flexible specification structure. A series of NN experiments is presented in the paper, using two data sets on German NUTS-3 districts. Individual forecasts are computed by our models for each district in order to answer the following question: How relevant are NN parameters in comparison to NN structure? Comprehensive testing of these parameters is limited in the literature. Building on different specifications of NN models—in terms of explanatory variables and NN structures—we propose a systematic choice of NN learning parameters and internal functions by means of a sensitivity analysis. Our results show that different combinations of NN parameters provide significantly varying statistical performance and forecasting power. Finally, we note that the sets of parameters chosen for a given model specification cannot be light-heartedly applied to different or more complex model