The Kolmogorov-Gabor Polynomial in Modeling Economic Dynamics

This book revisits the Kolmogorov-Gabor polynomial - a powerful yet often overlooked nonparametric model derived from the famous Kolmogorov-Arnold theorem – with a fresh perspective and new insights. It presents it as a simplified, computationally efficient tool for modeling nonlinear economic dynamics. Thanks to its high accuracy in both approximation and forecasting, the "Elementary Image" (EI) model serves as a practical alternative to standard econometric methods and artificial neural networks, particularly for medium-term economic forecasting. In discussing the theoretical foundations of the EI model, the author highlights its advantages over traditional methods, specifically its adaptability and ease of use. The book provides a step-by-step guide to constructing and optimizing models based on the Kolmogorov-Gabor polynomial, supported by numerous empirical examples that demonstrate their practical effectiveness. Sections covering more advanced topics address adaptive EI models and polynomial networks, ensuring the approach remains relevant amidst the ongoing evolution of mathematical algorithms. The accessible presentation makes the book valuable to both professionals with a basic understanding of statistics and experts tackling complex economic problems. This publication is intended for economists, researchers, and data analysts seeking effective tools for modeling nonlinear processes. It will also appeal to professionals in fields requiring multivariate nonlinear modeling, as it bridges the gap between theoretical principles and practical application, helping to overcome the limitations inherent in econometrics and artificial neural networks when applied to economic forecasting.