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

Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model

Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI) is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX) in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics

Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches

ARTIFICIAL NEURAL NETWORK APPLICATION IN GROSS DOMESTIC PRODUCT FORECASTING AN INDONESIA CASE

Gross Domestic Product (GDP) is a benchmark for economic production conditions of a country. Estimates of economic growth in the coming year in a country has important roles, among others as a benchmark in determining business plans for business entities, and the basis for devising government fiscal policy. Artificial Neural Network (ANN) has been increasingly recognized as a good forecasting tool in various fields. Its nature that can mimic the workings of the human brain makes it flexible for non-linear and nonparametric data. GDP growth forecasting techniques using ANN has been widely used in various countries, such as the United States, Canada, Germany, Austria, Iran, China, Japan and others. In Indonesia, forecasting of GDP is only done by government institutions, namely National Planning Board, using macroeconomic model. In this study, ANN is used as a tool for forecasting GDP growth in Indonesia, using some variables, such as GDP growth in the two previous periods, population growth rate, inflation, exchange rate and political stability and security conditions in Indonesia. Results from this study indicate that ANN forecasts GDP relatively better than the one issued by the government. Further study would be to use ANN to predict other economic indicators. Keywords: GDP growth, ANN, Forecastin

A Hybrid Intelligent Early Warning System for Predicting Economic Crises: The Case of China

This paper combines artificial neural networks (ANN), fuzzy optimization and time-series econometric models in one unified framework to form a hybrid intelligent early warning system (EWS) for predicting economic crises. Using quarterly data on 12 macroeconomic and financial variables for the Chinese economy during 1999 and 2008, the paper finds that the hybrid model possesses strong predictive power and the likelihood of economic crises in China during 2009 and 2010 remains high.Computational intelligence; artificial neural networks; fuzzy optimization; early warning system; economic crises

Using Recurrent Neural Networks To Forecasting of Forex

This paper reports empirical evidence that a neural networks model is applicable to the statistically reliable prediction of foreign exchange rates. Time series data and technical indicators such as moving average, are fed to neural nets to capture the underlying "rules" of the movement in currency exchange rates. The trained recurrent neural networks forecast the exchange rates between American Dollar and four other major currencies, Japanese Yen, Swiss Frank, British Pound and EURO. Various statistical estimates of forecast quality have been carried out. Obtained results show, that neural networks are able to give forecast with coefficient of multiple determination not worse then 0.65. Linear and nonlinear statistical data preprocessing, such as Kolmogorov-Smirnov test and Hurst exponents for each currency were calculated and analyzed.Comment: 23 pages, 13 figure

A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.

Does money matter in inflation forecasting?.

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation