0555825 20230316104816.520220321235959.9 10 s. E cav_un_epca*0551773978-80-87990-25-4450-459SlanýMelandrium2021Löster T.PavelkaT. Nonlinear regression machine learning kernel smoothing regularization regularization networks cav_un_auth*0345793 Kalina Jan UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0416837 Vidnerová P. CZ http://library.utia.cas.cz/separaty/2021/SI/kalina-0555825.pdf GA21-05325S GA ČR cav_un_auth*0409039 This paper is devoted to two important kernel-based tools of nonlinear regression: the Nadaraya-Watson estimator, which can be characterized as a successful statistical method in various econometric applications, and regularization networks, which represent machine learning tools very rarely used in econometric modeling. This paper recalls both approaches and describes their common features as well as differences. For the Nadaraya-Watsonestimator, we explain its connection to the conditional expectation of the response variable. Our main contribution is numerical analysis of suitable data with an economic motivation and a comparison of the two nonlinear regression tools. Our computations reveal some tools for the Nadaraya-Watson in R software to be unreliable, others not prepared for a routine usage. On the other hand, the regression modeling by means of regularization networks is much simpler and also turns out to be more reliable in our examples. These also bring unique evidence revealing the need for a careful choice of the parameters of regularization networks cav_un_auth*0422181 International Days of Statistics and Economics /15./ 20210909 20210911 Prague CZ BA 10000 10100 10102 2023 PR RVO:67985556 http://hdl.handle.net/11104/0330279 S 2021 SCOPUS PUBMED WOS cav_un_epca*0551773 The 15th International Days of Statistics and Economics Conference Proceedings Melandrium 2021 Slaný 450 459 978-80-87990-25-4 Löster T. 340 Pavelka T. 340