Future Prediction for Tax Complaints to Turkish Ombudsman by Models from Polynomial Regression and Parametric Distribution
The aim of this study is to forecast the amount of tax complaints filed with the Turkish Ombudsman in the future and whether or not policymakers require a specific tax Ombudsman. The polynomial regression for discrete data set is proposed to fit the number of events of tax complaints in the period f...
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| Format: | Article |
| Language: | English |
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Akif AKGUL
2024-03-01
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| Series: | Chaos Theory and Applications |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/3670830 |
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| author | Mehmet Niyazi Çankaya Murat Aydın |
| author_facet | Mehmet Niyazi Çankaya Murat Aydın |
| author_sort | Mehmet Niyazi Çankaya |
| collection | DOAJ |
| description | The aim of this study is to forecast the amount of tax complaints filed with the Turkish Ombudsman in the future and whether or not policymakers require a specific tax Ombudsman. The polynomial regression for discrete data set is proposed to fit the number of events of tax complaints in the period from years $2013$ to $2021$. The artificial data set is generated by models which are polynomial regression and parametric distribution. The location, scale and shape parameters are determined according to the smallest value between the observed and predicted dependent variable. After determining the smallest value for the tried values of shape parameter and the parameters of polynomial regression, the best value determined by grid search for shape parameter is around $1.07$. Thus, the heavy-tailed from of exponential power distribution is gained. The artificial data sets are generated and sorted from the smallest to biggest ones. The maximum values are around $700$ and $800$ which can be regarded as future prediction because the distance among observations is taken into account by models from polynomial regression and parametric distribution. Since the polynomial regression and the parametric models are used simultaneously for modelling, the distance among observations can also be modelled by parametric model as an alternative approach provided. |
| format | Article |
| id | doaj-art-63225935f842427ba676f26aa29baf48 |
| institution | Kabale University |
| issn | 2687-4539 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Akif AKGUL |
| record_format | Article |
| series | Chaos Theory and Applications |
| spelling | doaj-art-63225935f842427ba676f26aa29baf482025-01-23T18:20:09ZengAkif AKGULChaos Theory and Applications2687-45392024-03-0161637210.51537/chaos.14224001971Future Prediction for Tax Complaints to Turkish Ombudsman by Models from Polynomial Regression and Parametric DistributionMehmet Niyazi Çankaya0https://orcid.org/0000-0002-2933-857XMurat Aydın1https://orcid.org/0000-0002-7211-5208UŞAK ÜNİVERSİTESİ, UYGULAMALI BİLİMLER YÜKSEKOKULUUŞAK ÜNİVERSİTESİ, UYGULAMALI BİLİMLER YÜKSEKOKULUThe aim of this study is to forecast the amount of tax complaints filed with the Turkish Ombudsman in the future and whether or not policymakers require a specific tax Ombudsman. The polynomial regression for discrete data set is proposed to fit the number of events of tax complaints in the period from years $2013$ to $2021$. The artificial data set is generated by models which are polynomial regression and parametric distribution. The location, scale and shape parameters are determined according to the smallest value between the observed and predicted dependent variable. After determining the smallest value for the tried values of shape parameter and the parameters of polynomial regression, the best value determined by grid search for shape parameter is around $1.07$. Thus, the heavy-tailed from of exponential power distribution is gained. The artificial data sets are generated and sorted from the smallest to biggest ones. The maximum values are around $700$ and $800$ which can be regarded as future prediction because the distance among observations is taken into account by models from polynomial regression and parametric distribution. Since the polynomial regression and the parametric models are used simultaneously for modelling, the distance among observations can also be modelled by parametric model as an alternative approach provided.https://dergipark.org.tr/en/download/article-file/3670830estimationinferencepublic economicsparametric modelssimulation |
| spellingShingle | Mehmet Niyazi Çankaya Murat Aydın Future Prediction for Tax Complaints to Turkish Ombudsman by Models from Polynomial Regression and Parametric Distribution Chaos Theory and Applications estimation inference public economics parametric models simulation |
| title | Future Prediction for Tax Complaints to Turkish Ombudsman by Models from Polynomial Regression and Parametric Distribution |
| title_full | Future Prediction for Tax Complaints to Turkish Ombudsman by Models from Polynomial Regression and Parametric Distribution |
| title_fullStr | Future Prediction for Tax Complaints to Turkish Ombudsman by Models from Polynomial Regression and Parametric Distribution |
| title_full_unstemmed | Future Prediction for Tax Complaints to Turkish Ombudsman by Models from Polynomial Regression and Parametric Distribution |
| title_short | Future Prediction for Tax Complaints to Turkish Ombudsman by Models from Polynomial Regression and Parametric Distribution |
| title_sort | future prediction for tax complaints to turkish ombudsman by models from polynomial regression and parametric distribution |
| topic | estimation inference public economics parametric models simulation |
| url | https://dergipark.org.tr/en/download/article-file/3670830 |
| work_keys_str_mv | AT mehmetniyazicankaya futurepredictionfortaxcomplaintstoturkishombudsmanbymodelsfrompolynomialregressionandparametricdistribution AT murataydın futurepredictionfortaxcomplaintstoturkishombudsmanbymodelsfrompolynomialregressionandparametricdistribution |