Econometric models for forecasting of macroeconomic indices

The urgency of the research topic was stipulated by the necessity to carry out an effective controlled process by the economic system which can hardly be imagined without indices forecasting characteristic of this system. An econometric model is a safe tool of forecasting which makes it possible to take into consideration the trend of indices development in the past and their cause and effect interrelations. The aim of the article is to build econometric models for macroeconomic indices forecasting, reflecting Russia’s economy stabilization processes. In the process of research econometric modeling methods were used which allow to build, estimate and control the quality of various econometric models. In the given research the following models were built and analyzed: autoregressive integrated moving average model, vector auto-regression model, simultaneous equations system; the comparison of forecast possibilities and forecast accuracy of models built; forecast values of considered macroeconomic indices for the next periods were received. As to the results of study some preference can be given to forecasting on the basis of autoregressive models. The materials of the article can be quite useful for researchers, dealing with problems of modeling and economic processes forecasting, both in their scientific and practical activity. © 2016 Sukhanova, Shirnaeva and Mokronosov

Enhanced Estimation of Autoregressive Wind Power Prediction Model Using Constriction Factor Particle Swarm Optimization

Accurate forecasting is important for cost-effective and efficient monitoring and control of the renewable energy based power generation. Wind based power is one of the most difficult energy to predict accurately, due to the widely varying and unpredictable nature of wind energy. Although Autoregressive (AR) techniques have been widely used to create wind power models, they have shown limited accuracy in forecasting, as well as difficulty in determining the correct parameters for an optimized AR model. In this paper, Constriction Factor Particle Swarm Optimization (CF-PSO) is employed to optimally determine the parameters of an Autoregressive (AR) model for accurate prediction of the wind power output behaviour. Appropriate lag order of the proposed model is selected based on Akaike information criterion. The performance of the proposed PSO based AR model is compared with four well-established approaches; Forward-backward approach, Geometric lattice approach, Least-squares approach and Yule-Walker approach, that are widely used for error minimization of the AR model. To validate the proposed approach, real-life wind power data of \textit{Capital Wind Farm} was obtained from Australian Energy Market Operator. Experimental evaluation based on a number of different datasets demonstrate that the performance of the AR model is significantly improved compared with benchmark methods.Comment: The 9th IEEE Conference on Industrial Electronics and Applications (ICIEA) 201

A Durbin-Watson serial correlation test for ARX processes via excited adaptive tracking

We propose a new statistical test for the residual autocorrelation in ARX adaptive tracking. The introduction of a persistent excitation in the adaptive tracking control allows us to build a bilateral statistical test based on the well-known Durbin-Watson statistic. We establish the almost sure convergence and the asymptotic normality for the Durbin-Watson statistic leading to a powerful serial correlation test. Numerical experiments illustrate the good performances of our statistical test procedure