The thesis analyzes the accuracy of the multi-step inflation forecast using se-lected methods of machine learning through inflationary factors in the Visegrad group countries. The methods that were applied in the work analysis are the re-gression of tree methods and the algorithm method to the k-nearest neighbors. Based on the regression tree method, we are able to identify factors that are most prominent in price level development. The output of the analysis consists of 8 models, the suitability and accuracy of which are discussed. The results of the em-pirical analysis are compared with the assumptions that were presented before the analysis has begun. This suggests that methods are not suitable for multi-step inflation prediction.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:429374 |
| Date | January 2018 |
| Creators | Číriová, Nora |
| Source Sets | Czech ETDs |
| Language | Slovak |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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