Araştırma Makalesi
BibTex RIS Kaynak Göster

Türkiye’de Sağlık Harcamalarının ARIMA Yöntemi İle Tahmini

Yıl 2023, Cilt: 7 Sayı: Özel Sayı, 552 - 580, 29.10.2023

Muhammed Hasan Yücel Zafer Çalışkan

https://doi.org/10.25295/fsecon.1350399

Öz

Beşerî sermaye bireyin hayatı süresince edindiği bilgi ve becerilerin toplamı olarak tanımlanmaktadır. Bu çalışma beşerî sermaye kavramı ile kişinin edinmiş olduğu bilgi ve becerilerin korunması bağlamında sağlık yatırımlarının toplumun kalkınması ve gelişmesini etkilediği hipotezinden yola çıkmaktadır. Çalışmanın amacı Türkiye’de sağlık hizmetlerine yapılan kamu sağlık harcamalarının projeksiyonunu yapmaktır. Sağlık düzeyi yüksek bir toplumun varlığı ekonomik gelişmeyi, ilerlemeyi ve büyümeyi olumlu yönde etkilemektedir. Refah düzeyinin yüksek olması sağlık hizmetlerine olan talebi artırdığı gibi sağlık sektöründe yapılan harcamaları da artırmaktadır. Bu çalışmada Türkiye’de merkezi bütçeden harcanan kamu sağlık harcamaları ARIMA (10,1,1) modeli ile tahmin edilmektedir. 234 gözlem ile yapılan analizde Hazine Bakanlığı muhasebat bilgi sisteminden alınan iller bazında aylık toplam kamu sağlık harcama verileri kullanılmaktadır. Box-Jenkins metodolojisinin uygulandığı ve 2004:M01-2023:M06 dönemini kapsayan aylık verilerin kullanıldığı çalışmada nominal kamu sağlık harcamalarının gelecek üç yıl içerisinde artacağı ve 2023 yılında 380 milyar TL’ye, 2024 yılında 538 milyar TL’ye ve 2025 yılında 694 milyar TL’ye çıkacağı tahmin edilmektedir. Reel hale getirilmiş kamu sağlık harcamalarının ise 2023 yılında yaklaşık 27 milyar 679 milyon TL, 2024 yılında 30 milyar 756 milyon TL ve 2025 yılında 32 milyar 530 milyon TL düzeyine yükseleceği öngörülmektedir.

Anahtar Kelimeler

Kamu Sağlık Harcamaları ARIMA Zaman Serisi Tahmini Box-Jenkins Metodolojisi

Kaynakça

  • Atalan, A. (2020). Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi, 9(1), 8-16. https://doi.org/10.37989/gumussagbil.538111
  • Billerlioğlu, H. (2019). Türkiye'nin 2023 Nüfus Piramidine Göre Sağlık Harcamaları Projeksiyonu. Cerrahpaşa Lisansüstü Eğitim Enstitüsü, İstanbul.
  • Box, G. E. P. & Jenkins, G. M. (1976). Time Series Analysis Forecasting and Control. Revised Edition, Holden-Day, Oakland, California.
  • Chaabouni, S. & Abednnadher, C. (2013). Modelling and Forecasting of Tunisia’s Health Expenditures using Artificial Neural Network and ARDL Models. International Journal of Medical Science and Public Health, 2(3), 495-503. https://www.bibliomed.org/mnsfulltext/67/67-1355227550.pdf?1692271242
  • Cryer, J. D. & Chan, K. S. (2008). Time Series Analysis with Applications in R. Springer Publication.
  • Di Matteo, L. (2010). The Sustainability of Public Health Expenditures: Evidence from The Canadian Federation. The European Journal of Health Economics, 11(6), 569-584. http://www.jstor.org/stable/40963293
  • Dickey, D. A. & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49, 1057-1072. https://doi.org/10.2307/1912517
  • Dritsakis, N. & Klazoglou, P. (2019). Time Series Analysis using ARIMA Models: An Approach to Forecasting Health Expenditures in USA. Economia Internazionale/ International Economics, 72(1), 77-106. https://www.iei1946.it/article/165/time-series-analysis-using-arima-models-anapproach-to-forecasting-health-expenditures-in-usa
  • Findley, D. F., Monsell, B. C., Bell, W. R., Otto, M. C. & Chen, B.-C. (1998). New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program. Journal of Business & Economic Statistics, 16(2), 127-152. https://doi.org/10.2307/1392565
  • Getzen, T. E. & Pullier, J. P. (1992). International Health Spending Forecasts: Concepts and Evaluation. Social Science and Medicine, 34(9), 1057-1068. https://doi.org/10.1016/0277-9536(92)90136-E
  • Getzen, T. E. (2000). Forecasting Health Expenditures: Short, Medium and Long (Long) Term. Journal of Health Care Finance, 26(3), 56-72. https://ssrn.com/abstract=1950809
  • Güleryüz, D. (2021). Predicting Health Spending in Turkey Using the GPR, SVR, and DT Models. Acta Infologica, 5(1), 155-166. https://doi.org/10.26650/acin.882660
  • Hazine Bakanlığı. https://muhasebat.hmb.gov.tr/iller-itibariyle-merkezi-yonetim-butce-istatistikleri-2004-2019
  • Hylleberg, S. (2006). Seasonal Adjustment. University of Aarhus Department of Economics Working Paper No. 2006-4. http://dx.doi.org/10.2139/ssrn.1147032
  • Kalanlar, B. (2018). Türkiye’nin Yüzüncü Yılında Sağlık Sektörü, Mevcut Durum ve Öngörüler. Hacettepe Sağlık İdaresi Dergisi, 21(3), 495-510. https://dergipark.org.tr/tr/pub/hacettepesid/issue/39661/469798
  • Kayım, H. (1985). İstatistiksel Ön Tahmin Yöntemleri. Ankara.
  • Lee, R. & Miller, T. (2002). An Approach to Forecasting Health Expenditures, with Application to the US Medicare System. Health Services Research, 37(5), 1365-1386. https://doi.org/10.1111/1475-6773.01112
  • Lorenzoni, L., Marino, A., Morgan, D. & James, C. (2019). Health Spending Projections to 2030: New Results Based on a Revised OECD Methodology. OECD Health Working Papers, No. 110, OECD Publishing, Paris. https://doi.org/10.1787/5667f23d-en
  • Ntivuguruzwa, S. (2023). Application of Machine Learning in Long Term Healthcare Cost Prediction. Health Science Journal, 17(4), 1-7. https://www.itmedicalteam.pl/articles/application-of-machine-learning-in-long-term-healthcare-cost-prediction.pdf
  • OECD. (2023). Health Spending (Indicator). Doi: 10.1787/8643de7e-en (Accessed on 23 August 2023).
  • Okatan, E. & Işık, A. H. (2020). Sağlık Harcamalarının Tahmininde Karar Ağacının Kullanımı. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 11(1), 86-94. https://doi.org/10.29048/makufebed.650463
  • Phillips, P. C. B. & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75, 335-346. https://doi.org/10.2307/2336182
  • Sevüktekin, M. (2017). Ekonometrik Zaman Serileri Analizi EViews Uygulamalı. Bursa: Dora Yayıncılık.
  • Sisko, A. M., Keehan, S. P., Poisal, J. A., Cuckler, G. A., Smith, S. D., Madison, A. J., Rennie, K. E. & Hardesty, J. C. (2019). National Health Expenditure Projections, 2018-27: Economic and Demographic Trends Drive Spending and Enrollment Growth. Health Aff (Millwood), 38(3), 491-501. https://doi.org/10.1377/hlthaff.2018.05499
  • Şimşek, M. (2006). Beşerî Sermaye ve Beyin Göçü Kapsamında Türkiye (Birinci Baskı). Bursa: Ekin Kitabevi.
  • T. C. Merkez Bankası. (2023). EVDS-Ortalama Döviz Kurları. https://evds2.tcmb.gov.tr/index.php?/evds/serieMarket
  • TÜİK. (2021). Sağlık Harcamaları İstatistikleri. https://data.tuik.gov.tr/Bulten/Index?p=Saglik-Harcamalari-Istatistikleri-2021-45728
  • TÜİK. (2023). Tüketici Fiyat Endeksi. https://data.tuik.gov.tr/Bulten/Index?p=Tuketici-Fiyat-Endeksi-Temmuz-2023-49649
  • Yaglom, A. M. (1955). Correlation Theory of Processes with Random Stationary Nth Increments. Matematicheskii Sbornik Novaya Seriya, 37(79), 141-196. https://www.mathnet.ru/links/84471424eeb07bc456b9ace471691382/sm5214.pdf
  • Yule, G. U. (1926). Why do we Sometimes get Nonsense-Correlations between Time-Series? A Study in Sampling and the Nature of Time-Series. Journal of the Royal Statistical Society, 89(1), 1-63. https://doi.org/10.2307/2341482
  • Zhao, J. (2015). Forecasting Health Expenditure: Methods and Applications to International Databases. Centre for Health Economics and Policy Analysis (CHEPA), McMaster University. https://forecasters.org/wp-content/uploads/gravity_forms/7-2a51b93047891f1ec3608bdbd77ca58d/2013/07/Zhao_Junying_ISF2013.pdf
  • Zheng, A., Fang, Q., Zhu, Y., Jiang, C., Jin, F. & Wang, X. (2020). An Application of ARIMA Model for Predicting Total Health Expenditure in China from 1978-2022. Journal of Global Health, 10(1), 1-8. https://jogh.org/documents/issue202001/jogh-10-010803.pdf

The Forecasting of Health Expenditure in Türkiye Using ARIMA Method

Yıl 2023, Cilt: 7 Sayı: Özel Sayı, 552 - 580, 29.10.2023

Muhammed Hasan Yücel Zafer Çalışkan

https://doi.org/10.25295/fsecon.1350399

Öz

Human capital is the sum of the knowledge and skills acquired by an individual during his/her life. This study starts from the hypothesis that health investments affect the development and development of society in the context of protecting the knowledge and skills acquired by a person with the concept of human capital. The study aims to project the government health expenditures made on health services in Türkiye. The existence of a society with a high level of health positively affects economic development, progress, and growth. A high level of well-being increases the demand for health services and increases spending in the health sector. In this study, the government health expenditures spent from the central budget in Türkiye are estimated using the ARIMA (10,1,1) model. In the analysis carried out with 234 observations, the total monthly government health expenditures data based on provinces taken from the accounting information system of the Republic of Türkiye Ministry of Treasury and Finance are used. In the study, where the Box-Jenkins methodology is applied and monthly data covering the period 2004:M01-2023:M06 are used, government health expenditures are estimated to increase over the next three years and to increase to 380 billion TL in 2023, 538 billion TL in 2024, and 694 billion TL in 2025. On the other hand, it is estimated that the realized public health expenditures will increase to approximately 27 billion 679 million TL in 2023, 30 billion 756 million TL in 2024, and 32 billion 530 million TL in 2025.

Anahtar Kelimeler

Government Health Expenditure ARIMA Time Series Forecasting Box-Jenkins Methodology

Kaynakça

  • Atalan, A. (2020). Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi, 9(1), 8-16. https://doi.org/10.37989/gumussagbil.538111
  • Billerlioğlu, H. (2019). Türkiye'nin 2023 Nüfus Piramidine Göre Sağlık Harcamaları Projeksiyonu. Cerrahpaşa Lisansüstü Eğitim Enstitüsü, İstanbul.
  • Box, G. E. P. & Jenkins, G. M. (1976). Time Series Analysis Forecasting and Control. Revised Edition, Holden-Day, Oakland, California.
  • Chaabouni, S. & Abednnadher, C. (2013). Modelling and Forecasting of Tunisia’s Health Expenditures using Artificial Neural Network and ARDL Models. International Journal of Medical Science and Public Health, 2(3), 495-503. https://www.bibliomed.org/mnsfulltext/67/67-1355227550.pdf?1692271242
  • Cryer, J. D. & Chan, K. S. (2008). Time Series Analysis with Applications in R. Springer Publication.
  • Di Matteo, L. (2010). The Sustainability of Public Health Expenditures: Evidence from The Canadian Federation. The European Journal of Health Economics, 11(6), 569-584. http://www.jstor.org/stable/40963293
  • Dickey, D. A. & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49, 1057-1072. https://doi.org/10.2307/1912517
  • Dritsakis, N. & Klazoglou, P. (2019). Time Series Analysis using ARIMA Models: An Approach to Forecasting Health Expenditures in USA. Economia Internazionale/ International Economics, 72(1), 77-106. https://www.iei1946.it/article/165/time-series-analysis-using-arima-models-anapproach-to-forecasting-health-expenditures-in-usa
  • Findley, D. F., Monsell, B. C., Bell, W. R., Otto, M. C. & Chen, B.-C. (1998). New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program. Journal of Business & Economic Statistics, 16(2), 127-152. https://doi.org/10.2307/1392565
  • Getzen, T. E. & Pullier, J. P. (1992). International Health Spending Forecasts: Concepts and Evaluation. Social Science and Medicine, 34(9), 1057-1068. https://doi.org/10.1016/0277-9536(92)90136-E
  • Getzen, T. E. (2000). Forecasting Health Expenditures: Short, Medium and Long (Long) Term. Journal of Health Care Finance, 26(3), 56-72. https://ssrn.com/abstract=1950809
  • Güleryüz, D. (2021). Predicting Health Spending in Turkey Using the GPR, SVR, and DT Models. Acta Infologica, 5(1), 155-166. https://doi.org/10.26650/acin.882660
  • Hazine Bakanlığı. https://muhasebat.hmb.gov.tr/iller-itibariyle-merkezi-yonetim-butce-istatistikleri-2004-2019
  • Hylleberg, S. (2006). Seasonal Adjustment. University of Aarhus Department of Economics Working Paper No. 2006-4. http://dx.doi.org/10.2139/ssrn.1147032
  • Kalanlar, B. (2018). Türkiye’nin Yüzüncü Yılında Sağlık Sektörü, Mevcut Durum ve Öngörüler. Hacettepe Sağlık İdaresi Dergisi, 21(3), 495-510. https://dergipark.org.tr/tr/pub/hacettepesid/issue/39661/469798
  • Kayım, H. (1985). İstatistiksel Ön Tahmin Yöntemleri. Ankara.
  • Lee, R. & Miller, T. (2002). An Approach to Forecasting Health Expenditures, with Application to the US Medicare System. Health Services Research, 37(5), 1365-1386. https://doi.org/10.1111/1475-6773.01112
  • Lorenzoni, L., Marino, A., Morgan, D. & James, C. (2019). Health Spending Projections to 2030: New Results Based on a Revised OECD Methodology. OECD Health Working Papers, No. 110, OECD Publishing, Paris. https://doi.org/10.1787/5667f23d-en
  • Ntivuguruzwa, S. (2023). Application of Machine Learning in Long Term Healthcare Cost Prediction. Health Science Journal, 17(4), 1-7. https://www.itmedicalteam.pl/articles/application-of-machine-learning-in-long-term-healthcare-cost-prediction.pdf
  • OECD. (2023). Health Spending (Indicator). Doi: 10.1787/8643de7e-en (Accessed on 23 August 2023).
  • Okatan, E. & Işık, A. H. (2020). Sağlık Harcamalarının Tahmininde Karar Ağacının Kullanımı. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 11(1), 86-94. https://doi.org/10.29048/makufebed.650463
  • Phillips, P. C. B. & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75, 335-346. https://doi.org/10.2307/2336182
  • Sevüktekin, M. (2017). Ekonometrik Zaman Serileri Analizi EViews Uygulamalı. Bursa: Dora Yayıncılık.
  • Sisko, A. M., Keehan, S. P., Poisal, J. A., Cuckler, G. A., Smith, S. D., Madison, A. J., Rennie, K. E. & Hardesty, J. C. (2019). National Health Expenditure Projections, 2018-27: Economic and Demographic Trends Drive Spending and Enrollment Growth. Health Aff (Millwood), 38(3), 491-501. https://doi.org/10.1377/hlthaff.2018.05499
  • Şimşek, M. (2006). Beşerî Sermaye ve Beyin Göçü Kapsamında Türkiye (Birinci Baskı). Bursa: Ekin Kitabevi.
  • T. C. Merkez Bankası. (2023). EVDS-Ortalama Döviz Kurları. https://evds2.tcmb.gov.tr/index.php?/evds/serieMarket
  • TÜİK. (2021). Sağlık Harcamaları İstatistikleri. https://data.tuik.gov.tr/Bulten/Index?p=Saglik-Harcamalari-Istatistikleri-2021-45728
  • TÜİK. (2023). Tüketici Fiyat Endeksi. https://data.tuik.gov.tr/Bulten/Index?p=Tuketici-Fiyat-Endeksi-Temmuz-2023-49649
  • Yaglom, A. M. (1955). Correlation Theory of Processes with Random Stationary Nth Increments. Matematicheskii Sbornik Novaya Seriya, 37(79), 141-196. https://www.mathnet.ru/links/84471424eeb07bc456b9ace471691382/sm5214.pdf
  • Yule, G. U. (1926). Why do we Sometimes get Nonsense-Correlations between Time-Series? A Study in Sampling and the Nature of Time-Series. Journal of the Royal Statistical Society, 89(1), 1-63. https://doi.org/10.2307/2341482
  • Zhao, J. (2015). Forecasting Health Expenditure: Methods and Applications to International Databases. Centre for Health Economics and Policy Analysis (CHEPA), McMaster University. https://forecasters.org/wp-content/uploads/gravity_forms/7-2a51b93047891f1ec3608bdbd77ca58d/2013/07/Zhao_Junying_ISF2013.pdf
  • Zheng, A., Fang, Q., Zhu, Y., Jiang, C., Jin, F. & Wang, X. (2020). An Application of ARIMA Model for Predicting Total Health Expenditure in China from 1978-2022. Journal of Global Health, 10(1), 1-8. https://jogh.org/documents/issue202001/jogh-10-010803.pdf
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Zaman Serileri Analizi, Uygulamalı Ekonomi (Diğer)
Bölüm Makaleler
Yazarlar

Muhammed Hasan Yücel 0000-0002-5301-7522

Zafer Çalışkan 0000-0001-9221-6578

Yayımlanma Tarihi 29 Ekim 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 7 Sayı: Özel Sayı

Kaynak Göster

APA Yücel, M. H., & Çalışkan, Z. (2023). Türkiye’de Sağlık Harcamalarının ARIMA Yöntemi İle Tahmini. Fiscaoeconomia, 7(Özel Sayı), 552-580. https://doi.org/10.25295/fsecon.1350399
 Kapak Resmi İndir

 Fiscaoeconomia is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.