MODELING CENTRAL JAVA INFLATION AND GRDP RATE USING SPLINE TRUNCATED BIRESPON REGRESSION AND BIRESPON LINEAR MODEL

*Suparti Suparti  -  Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro, Indonesia
Alan Prahutama  -  Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro, Indonesia
Agus Rusgiyono  -  Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro, Indonesia
Sudargo Sudargo  -  PGRI Semarang University, Indonesia
Received: 14 Oct 2019; Published: 30 Dec 2019.
Open Access Copyright (c) 2019 MEDIA STATISTIKA
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Citation Format:
Article Info
Section: Articles
Language: EN
Full Text:
Statistics: 159 166
Abstract
Inflation and Gross Regional Domestic Income (GRDP) are two macroeconomic variables of a region that are correlated with each other. GRDP prices constant (real) can be used as an indicator of economic growth in a region from year to year. Inflation is calculated from the CPI rate and economic growth is calculated from the GRDP rate. Inflation and economic growth in an area are influenced by several factors including bank interest rates. Analysis of data consisting of 2 correlated responses can be performed with birespon regression analysis. In this research, modeling of inflation data and the rate of GRDP through birespon data modeling uses spline truncated nonparametric method and birespon linear parametric method. The purpose of this study is to model inflation data and the Central Java GRDP rate using spline truncated birespon regression. The results are compared with the birespon linear regression model. By using quarterly data from the first quarter of 2007 - the second quarter of 2019, the spline truncated model is better than the linear model, because the spline truncated model has a smaller MSE and R2 is greater than the linear model. Both models have the same performance which is quite good.
Keywords
Inflation; GRDP; Spline Biresponse

Article Metrics:

  1. Agusmianata, N., Militina,T. dan Lestari, D. (2017). Pengaruh jumlah uang beredar dan tingkat suku bunga serta pengeluaran pemerintah terhadap inflasi di Indonesia , Forum Ekonomi Volume 19 (2), 2017, p 188-200
  2. Ampulembang, A.P, Otok, B.W and Rumiati, A.T.( 2016). Modeling of Welfare Indicators in Java Island Using Biresponses MARS. International Journal of Applied Mathematics and Statistics 54 (2): 66–75
  3. Chamidah, N dan Rifada, M. (2016).Estimation of median growth curves for children up two years old based on biresponse local linear estimator. AIP Conference Proceedings 1718
  4. Fahrika, A.I. (2016), Pengaruh tingkat suku bunga melalui investasi swasta terhadap pertumbuhan, Economics, Social and Development Sudies, vol 3 no 2 (2016))
  5. Fernandes, A. A. R., Budiantara, I. N., Otok, B. W., dan Suhartono. 2014. Spline Estimator for Bi-responses Nonparametric Regression Model for Longitudinal Data. Applied Mathematical Sciences Vol. 8 No. 114 : Hal. 5653-5665.
  6. Indriyani, S (2016) Analisis pengaruh inflasi dan suku bunga terhadap pertumbuhan ekonomi di Indonesia tahun 2005 – 2015. Jurnal Manajemen Bisnis Krisnadwipayana, Vol 4, No 2 (2016)
  7. Lestari, B., Chamidah, N. dan Saifudin, T. (2019). Estimasi Fungsi Regresi Dalam Model Regresi Nonparametrik Birespon Menggunakan Estimator Smoothing Spline dan Estimator Kernel, Jurnal Matematika, Statistika dan Komputasi Vol 15 No.2.
  8. Nurdiani, N, N Heryanto, and D Darari. (2018). Regresi Nonparametrik Birespon Spline.Jurnal EurekaMatika 5 (106–121)
  9. Panjaitan, M. N. Y., dan Wardoyo. 2016. Faktor-Faktor yang Mempengaruhi Inflasi di Indonesia. Jurnal Ekonomi BisnisVol.21, No. 3 : Hal. 182-193
  10. Pratiwi, LPS.(2017). Pemodelan Spline Truncated Dalam Regresi Nonparametrik Birespon.E-Proceedings KNSI STIKOM Bali, 441–45
  11. Suparti (2013). Analisis Data Inflasi di Indonesia Menggunakan Model Regresi Spline. Jurnal Media Statistika, Vol. 6, No. 1, hal 1 – 9.
  12. Suparti , Prahutama, A. , Santoso, R. (2018a). Mix local polynomial and spline truncated: the development of nonparametric regression model. Journal of Physics Conference series, 1025 (1), hal 1-7.
  13. Suparti, Prahutama, A., Santoso, R. and Devi, A.R. (2018b). Regresi Nonparametrik. Ponorogo ,Wade Group.
  14. Suparti, Prahutama, A., Rahmawati, R., dan Utami, T.W., (2016). Modelling Local Polynomial for Longitudinal Data A Case Study; Inflation sectors in Indonesia, ARPN Journal of Engineering and Aplied Science (JEAS), Vol.11 No.23, December 2016,
  15. Suparti, Warsito, B. dan Mukid, M.A. (2014a). Analisis Data Inflasi Di Indonesia Menggunakan Model Regresi Polinomial Lokal. IndoMS Journal on Statistics.
  16. Suparti, Warsito, B. dan Mukid, M.A. (2015). Analisis Data Inflasi Di Indonesia Menggunakan Model Arima Box-Jenkins, Kernel dan Spline . Prosiding Seminar Nasional Matematika Saintekinfo, MIPA UNS, hal. 150-156.
  17. Wu, H. dan Zhang, J.T. (2006). Nonparametric Regression Methods for Longitudinal Data Analysis. A John-Wiley and Sons Inc. Publication, New Jersey.
  18. Wulandari, I.D.A.M.I, and Budiantara. I.N. (2014). Analisis Faktor-Faktor yang Mempengaruhi Persentase Penduduk Miskin dan Pengeluaran Perkapita Makanan di Jawa Timur Menggunakan Regresi Nonparametrik Birespon Spline. Jurnal Sains dan Seni ITS 3 (1): 30–35.