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      Functional approach to analysis of daily tax revenues

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      Lietuvos matematikos rinkinys

      Vilnius University Press

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          Abstract

          We present a functional data analysis approach to modeling and analyzing daily tax revenues. The main features of daily tax revenue we need to extract are some patterns within calendar months which can be used for prediction. As standard seasonal time series techniques cannot be used due to varying number of banking days per calendar month and presence of seasonality between and within months we interpret monthly tax revenues as curves obtained from daily data. Standard smoothing techniques and registration taking into account time variability are used for data preparation.

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          Author and article information

          Journal
          Lietuvos matematikos rinkinys
          LMR
          Vilnius University Press
          2335-898X
          0132-2818
          November 15 2019
          November 12 2019
          : 60
          : 7-14
          Article
          10.15388/LMR.A.2019.14948
          © 2019

          All content is freely available without charge to users or their institutions. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission of the publisher or the author. Articles published in the journal are distributed under a http://creativecommons.org/licenses/by/4.0/.

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