The detection of chaotic behaviors in commodities, stock markets and weather data is usually complicated by large noise perturbation inherent to the underlying system. It is well known, that predictions, from pure deterministic chaotic systems can be accurate mainly in the short term. Thus, it...

Long memory processes have been extensively studied over the past decades. When dealing with the financial and economic data, seasonality and time-varying long-range dependence can often be observed and thus some kind of non-stationarity can exist inside financial data sets. To take into account...

Forecasting current quarter GDP is a permanent task inside the central banks. Many models are known and proposed to solve this problem. Thanks to new results on the asymptotic normality of the multivariate k-nearest neighbor regression estimate, we propose an interesting and new approach to...

We propose a nouvel methodology for forecasting chaotic systems which uses information on local Lyapunov exponents (LLEs) to improve upon existing predictors by correcting for their inevitable bias. Using simulations of the Rössler, Lorenz and Chua attractors, we find that accuracy gains can be...

The aim of this paper is to introduce a new methodology to forecast the monthly economic indicators used in the Gross Domestic Product (GDP) modelling in order to improve the forecasting accuracy. Our approach is based on multivariate k-nearest neighbors method and radial basis function method...

An empirical forecast accuracy comparison of the non-parametric method, known as multivariate Nearest Neighbor method, with parametric VAR modelling is conducted on the euro area GDP. Using both methods for nowcasting and forecasting the GDP, through the estimation of economic indicators plugged...

Business surveys are an important element in the analysis of the short-term economic situation because of the timeliness and nature of the information they convey. Especially, surveys are often involved in econometric models in order to provide an early assessment of the current state of the...

This papier formalizes the process of forecasting unbalanced monthly data sets in order to obtain robust nowcasts and forecasts of quarterly GDP growth rate through a semi-parametric modelling. This innovative approach lies on the use on non-parametric methods, based on nearest neighbors and on...

We propose a novel methodology for forecasting chaotic systems which is based on the nearest-neighbor predictor and improves upon it by incorporating local Lyapunov exponents to correct for its inevitable bias. Using simulated data, we show that gains in prediction accuracy can be substantial....

We introduce a new type of demand system using a feedforward artificial neural network. The neural network demand system is a flexible system that requires few hypotheses, has no roots in consumer theory but may be used to test it. We use the system to estimate demand elasticities on micro data...