Abstract
Abstract Paper aims This paper presents a comparative evaluation of different forecasting methods using two artificial neural networks (Multilayer Perceptron network and Radial Basis Functions Neural Network) and the Gaussian process regression. Originality Due to the current world scenario, solving economic problems has become extremely important. Artificial neural networks are one of the most promising tools to forecast economic trends and are being widely studied in economic analyses. Therefore, due to the concerns about the performance of different forecasting methods to solve economic problems, this study contributes with an example of the forecasting performance of artificial neural network models compared with Gaussian process regression using Nelson-Plosser and U.S. macroeconomic real-life data sets. Research method Two real-life data sets were used to evaluate the forecasting methods proposed in this paper. These data sets were normalised to values between zero and one. After that, the data training was performed and, once it was built, a model was used to generate forecasts. Thus, observations were made to verify how accurately the fitted model forecast the values. Main findings The results obtained from the study show that, for all forecasting horizons, multi-layer perceptron networks and Gaussian process regression models had the most satisfactory results. On the other hand, the radial basis functions neural network model was unsuitable for econometric data. Implications for theory and practice This study contributes to a discussion about artificial neural networks and Gaussian process regression models for econometric forecasting. Although artificial neural networks are mainly used in economic analyses, the results showed that not all models, such as radial basis functions neural networks, present good results. In addition, the regression of the Gaussian process showed promising results to forecast econometric data.
Highlights
Over the last few years, the world has been going through several cascading crises, and economic problems have become increasingly concerning in many countries
Unlike most other studies in the field, this uses Gaussian Process Regression (GPR), which is capable of yielding reliable out-of-sample predictions in the presence of highly non-linear unknown relationships between dependent and explanatory variables
The performance of the optimisation techniques when training the Wavelet Neural Networks (WNN) was compared to the well-established Backpropagation algorithm and Extreme Learning Machine (ELM), assuming accuracy measures
Summary
Over the last few years, the world has been going through several cascading crises, and economic problems have become increasingly concerning in many countries. Time series are observations on economic variables, which can be drawn from various fields of economics and business Such variables include: dividend price ratio, dividend yield, industrial production growth, treasury. This paper presents a comparative study of the performance of the econometric model and ANN. What sets this study apart from many others published in the area is that it uses the Nelson-Plosser (Nelson & Plosser, 1982) and U.S macroeconomic (Smets & Wouters, 2005) real-life data sets to analyse and evaluate both methods Both data sets comprise fourteen U.S data observed yearly, which generate fourteen economic time series widely used by researchers in this field. Unlike most other studies in the field, this uses Gaussian Process Regression (GPR), which is capable of yielding reliable out-of-sample predictions in the presence of highly non-linear unknown relationships between dependent and explanatory variables.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
