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Original title:
Multifractal approaches in econometrics and fractal-inspired robust regression
Authors: Kalina, Jan
Document type: Papers
Conference/Event: MME 2021: International Conference on Mathematical Methods in Economics /39./, Prague (CZ), 20210908
Year: 2021
Language: eng
Abstract: While the mainstream economic theory is based on the concept of general economic equilibrium, the economies throughout the world have recently been facing serious transformations and challenges. Thus, instead of a convergence to equilibrium, the economies can be regarded as unstable, turbulent or chaotic with properties characteristic for fractal or multifractal processes. This paper starts with a discussion of recent data analysis tools inspired by fractal or multifractal concepts. We pay special attention to available data analysis tools based on reciprocal weights assigned to individual observations - these are inspired by an assumed fractal structure of multivariate data. As an extension, we consider here a novel version of the least weighted squares estimator of parameters for the linear regression model, which exploits reciprocal weights. Finally, we perform a statistical analysis of 31 datasets with economic motivation and compare the performance of the least weighted squares estimator with various weights. It turns out that the reciprocal weights, inspired by the fractal theory, are not superior to other choices of weights. In fact, the best prediction results are obtained with trimmed linear weights.
Keywords: chaos in economics; fractal market hypothesis; prediction; reciprocal weights; robust regression
Host item entry: MME 2021, 39th International Conference on Mathematical Methods in Economics. Conference Proceedings, ISBN 978-80-213-3126-6
Note: Související webová stránka: https://mme2021.v2.czu.cz/dl/99363?lang=en
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available in the digital repository of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0322694
Permalink: http://www.nusl.cz/ntk/nusl-508170
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Authors: Kalina, Jan
Document type: Papers
Conference/Event: MME 2021: International Conference on Mathematical Methods in Economics /39./, Prague (CZ), 20210908
Year: 2021
Language: eng
Abstract: While the mainstream economic theory is based on the concept of general economic equilibrium, the economies throughout the world have recently been facing serious transformations and challenges. Thus, instead of a convergence to equilibrium, the economies can be regarded as unstable, turbulent or chaotic with properties characteristic for fractal or multifractal processes. This paper starts with a discussion of recent data analysis tools inspired by fractal or multifractal concepts. We pay special attention to available data analysis tools based on reciprocal weights assigned to individual observations - these are inspired by an assumed fractal structure of multivariate data. As an extension, we consider here a novel version of the least weighted squares estimator of parameters for the linear regression model, which exploits reciprocal weights. Finally, we perform a statistical analysis of 31 datasets with economic motivation and compare the performance of the least weighted squares estimator with various weights. It turns out that the reciprocal weights, inspired by the fractal theory, are not superior to other choices of weights. In fact, the best prediction results are obtained with trimmed linear weights.
Keywords: chaos in economics; fractal market hypothesis; prediction; reciprocal weights; robust regression
Host item entry: MME 2021, 39th International Conference on Mathematical Methods in Economics. Conference Proceedings, ISBN 978-80-213-3126-6
Note: Související webová stránka: https://mme2021.v2.czu.cz/dl/99363?lang=en
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available in the digital repository of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0322694
Permalink: http://www.nusl.cz/ntk/nusl-508170
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Record created 2022-09-28, last modified 2023-12-17