Biresponses Kernel Nonparametric Regression: Inflation and Economic Growth
DOI:
https://doi.org/10.6000/1929-4409.2021.10.54Keywords:
Inflation, economic growth, biresponses, kernelAbstract
The relation between inflation and economic growth is interesting to observe. To maintain the inflation rate, two factors should be taken into account, namely keeping the economic pulse at its optimal rate and keeping people's purchasing power from decreasing. Many factors influence the inflation and economic growth of a nation; one of which is the national bank interest rate. Since the data of inflation are closely related to economic growth, this study aims at modelling the data of inflation rate and economic growth of Central Java Province in Indonesia using bi-response kernel regression. Employing the data from the first trimester of 2007 up to those from the second trimester of 2019 which were processed using kernel Gauss, the best model to minimise the value of GCV was obtained with optimum h for inflation model amounting to 0.12 and 81.75 for economic growth model. The model performance was excellent because the MAPE out sample was less than 10%. The biresponses kernel model is better than the linear biresponses model in terms of GCV, MSE, R2, and MAPE values.
References
Agusmianata N, Militina T, Lestari D (2017) Pengaruh jumlah uang beredar dan tingkat suku bunga serta pengeluaran pemerintah terhadap inflasi di Indonesia. Forum Ekon 19:188–200 DOI: https://doi.org/10.29264/jfor.v19i2.2125
Ampulembang A., Otok BW, Rumiati A. (2016) Modeling of Welfare Indicators in Java Island Using Biresponses MARS. Int J Appl Math Stat 54:66–75
Anggoro Y (2015) Automotive and Logistics Clusters To Drive Economic. The University of North Carolina at Charlotte
Caraka RE, Bakar SA, Anuraga G, et al. (2019a) The step construction of penalized spline in electrical power load data. Telkomnika 17. https://doi.org/10.12928/telkomnika.v17i2.8640 DOI: https://doi.org/10.12928/telkomnika.v17i2.8460
Caraka RE, Bakar SA, Tahmid M, et al. (2019b) Neurocomputing Fundamental Climate Analysis. Telkomnika 17:1818–1827. https://doi.org/10.12928/TELKOMNIKA.v17i4.11788 DOI: https://doi.org/10.12928/telkomnika.v17i4.11788
Caraka RE, Chen RC, Toharudin T, et al. (2019c) Ramadhan Short-Term Electric Load : A Hybrid Model of Cycle Spinning Wavelet and Group Method Data Handling ( CSW-GMDH ). IAENG Int J Comput Sci 46:670–676
Caraka RE, Chen RC, Yasin H, et al. (2019d) Prediction of Status Particulate Matter 2.5 using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO. IEEE Access 7:161654–161665. https://doi.org/10.1109/ACCESS.2019.2950439 DOI: https://doi.org/10.1109/ACCESS.2019.2950439
Chamidah N, Rifada M (2016) Estimation of median growth curves for children up two years old based on biresponse local linear estimator. In: AIP Conference Proceedings DOI: https://doi.org/10.1063/1.4943348
Dartanto T (2010) Income Shocks and Consumption Smoothing Strategies: An Empirical Investigation of Maize Farmer’s Behavior in Kebumen, Central Java, Indonesia. Mod Econ 1:149. https://doi.org/10.4236/me.2010.13017 DOI: https://doi.org/10.4236/me.2010.13017
Dartanto T, Otsubo S (2013) Measurements and Determinants of Multifaceted Poverty:Absolute, Relative, and Subjective Poverty in Indonesia. JICA Res Inst 54:1–52
de Boor C (2002) Chapter 6 - Spline Basics DOI: https://doi.org/10.1016/B978-044451104-1/50007-1
Devi AR, Pratama RFW, Suparti (2019) Comparison of generalized cross validation and unbiased risk method for selecting optimal knot in spline truncated. In: Journal of Physics: Conference Series DOI: https://doi.org/10.1088/1742-6596/1217/1/012094
Eubank RL (2004) A simple smoothing spline, III. Comput Stat 19:227–241. https://doi.org/10.1007/BF02892058 DOI: https://doi.org/10.1007/BF02892058
Fahrika AI (2016) Pengaruh tingkat suku bunga melalui investasi swasta terhadap pertumbuhan. Econ Soc Dev Sudies 3:
Farin G (2002) B-Spline Curves. In: Curves and Surfaces for CAGD. pp 119–146 DOI: https://doi.org/10.1016/B978-155860737-8/50008-9
Fernandes, A. A. R., Budiantara IN, Otok BW, Suhartono (2014) Spline Estimator for Bi-responses Nonparametric Regression Model for Longitudinal Data. Appl Math Sci 8:5653–5665 DOI: https://doi.org/10.12988/ams.2014.47566
Ifeacho C, Ngalawa H (2014) Performance of the South African banking sector since 1994. J Appl Bus Res. https://doi.org/10.19030/jabr.v30i4.8663 DOI: https://doi.org/10.19030/jabr.v30i4.8663
Indriyani S (2016) Analisis pengaruh inflasi dan suku bunga terhadap pertumbuhan ekonomi di indonesia tahun 2005 – 2015. Manaj Bisnis Krisnadwipayana 4: DOI: https://doi.org/10.35137/jmbk.v4i2.37
Lestari B, Chamidah N, Saifudin T (2019) Estimasi Fungsi Regresi Dalam Model Regresi Nonparametrik Birespon Menggunakan Estimator Smoothing Spline dan Estimator Kernel. Mat Stat dan Komputasi 15: DOI: https://doi.org/10.20956/jmsk.v15i2.5710
Marbun W, Suparti, Maruddani DAI (2020) Modeling of composite stock price index (CSPI) using semiparametric regression truncated spline based on GUI R. In: Journal of Physics: Conference Series. p 012096 DOI: https://doi.org/10.1088/1742-6596/1524/1/012096
Martini L, Tjakraatmadja JH, Anggoro Y, et al. (2012) Triple Helix Collaboration to Develop Economic Corridors as Knowledge Hub in Indonesia. Procedia - Soc Behav Sci 52:130–139. https://doi.org/10.1016/j.sbspro.2012.09.449 DOI: https://doi.org/10.1016/j.sbspro.2012.09.449
Nurdiani N, Heryanto N, Darari D (2018) Regresi Nonparametrik Birespon Spline. J EurekaMatika 5:106–121
Pratiwi L (2017) Pemodelan Spline Truncated Dalam Regresi Nonparametrik Birespon. In: E-Proceedings KNSI STIKOM Bali. pp 41–45
Qin X, Qin L, Xu Q (2016) C1 positivity-preserving interpolation schemes with local free parameters. IAENG Int J Comput Sci 43:219–227 DOI: https://doi.org/10.2306/scienceasia1513-1874.2017.43.186
R Core Team (2008) R software. R Found. Stat. Comput. 739:409
Ruppert D, Wand MP, Carroll RJ (2009) Semiparametric regression during 2003–2007*. Electron J Stat 3:1193–1256. https://doi.org/10.1214/09-EJS525 DOI: https://doi.org/10.1214/09-EJS525
Suhartono and Endharta, A. J. (2009) ‘Short term load demand forecasting in Indonesia by using double seasonal recurrent Neural networks’, International Journal of Mathematical Models and Methods in Applied Sciences, 3(3).
Suhermi, N. et al. (2018) ‘Roll motion prediction using a hybrid deep learning and ARIMA model’, in Procedia Computer Science. https://doi.org/10.1016/j.procs.2018.10.526 DOI: https://doi.org/10.1016/j.procs.2018.10.526
Suparti (2013) Analisis Data Inflasi di Indonesia Pasca Kenaikan TDL dan BBM Tahun 2013 Menggunakan Model Regresi Kernel. J Media Stat 6:91–101 DOI: https://doi.org/10.14710/medstat.6.2.91-101
Suparti, Prahutama A, Santoso R (2018a) Modelling inflation in transportation, comunication and financial services using B-Spline time series model. In: Journal of Physics: Conference Series DOI: https://doi.org/10.1088/1742-6596/1025/1/012104
Suparti, Prahutama A, Santoso R (2018b) Mix local polynomial and spline truncated: The development of nonparametric regression model. In: Journal of Physics: Conference Series DOI: https://doi.org/10.1088/1742-6596/1025/1/012102
Suparti, Prahutama A, Santoso R, Devi A. (2018c) Regresi Nonparametrik
Suparti S, Caraka RE, Warsito B, Yasin H (2016) The Shift Invariant Discrete Wavelet Transform (SIDWT) with Inflation Time Series Application. J Math Res 8:14. https://doi.org/10.5539/jmr.v8n4p14 DOI: https://doi.org/10.5539/jmr.v8n4p14
Suparti, Santoso R, Prahutama A, Devi AR (2019) Indonesia’s Inflation Analysis Using Hybrid Fourier - Wavelet Multiscale Autoregressive Method. In: Journal of Physics: Conference Series DOI: https://doi.org/10.1088/1742-6596/1306/1/012041
Suparti, Warsito B, Mukid MA (2014) Analisis Data Inflasi Di Indonesia Menggunakan Model Regresi Polinomial Loka. IndoMS J Stat. DOI: https://doi.org/10.14710/medstat.6.1.1-9
Wahba G (1985) A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline smoothing problem. Ann Stat 13:1378–1402. https://doi.org/10.2307/2241360 DOI: https://doi.org/10.1214/aos/1176349743
Wu H, Zhang JT (2006) Nonparametric regression methods for longitudinal data analysis. Choice Rev Online. https://doi.org/10.5860/choice.44-0978 DOI: https://doi.org/10.5860/CHOICE.44-0978
Wulandari IDAMI, Budiantara IN (2014)Analisis Faktor-Faktor yang Mempengaruhi Persentase Penduduk Miskin dan Pengeluaran Perkapita Makanan di Jawa Timur Menggunakan Regresi Nonparametrik Birespon Spline. J Sains dan Seni ITS 3:30–35
Yandell BS, Eubank RL (1989) Spline Smoothing and Nonparametric Regression. Technometrics 31:379–380. https://doi.org/10.2307/3556148 DOI: https://doi.org/10.1080/00401706.1989.10488561
Zarrouk H, Ben Jedidia K, Moualhi M (2016) Is Islamic bank profitability driven by same forces as conventional banks? Int J Islam Middle East Financ Manag 9:46–66. https://doi.org/10.1108/IMEFM-12-2014-0120 DOI: https://doi.org/10.1108/IMEFM-12-2014-0120
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Policy for Journals/Articles with Open Access
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
Policy for Journals / Manuscript with Paid Access
Authors who publish with this journal agree to the following terms:
- Publisher retain copyright .
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work .
