Pemodelan Regresi Semiparametrik B Spline (Studi Kasus: Pengaruh Harga Emas dan Minyak Mentah Dunia Terhadap Indeks Harga Saham Gabungan)

M. Pratama Aryansah, Suparti Suparti

Abstract

The increases of the world gold and crude oil prices have a big role as a main factor that effect composite stock price index, the effect can make investors to buy stock from Bursa Efek Indonesia. Regression semiparametric used in this research for a purpose to get combined parametric and nonparametric with B Spline approach. B Spline is a development of spline to overcome weaknesses in making singular matrix at a high order spline with many knot points and close together. Variable parametric component is composite stock price index with crude oil price, and variable nonparametric component is composite stock price index with gold price that got obtained from January 2015 until December 2022. The result from this research is best regression semiparametric B-Spline modelling can be obtained using some combination of order and knot points. The optimal point is obtained on 2nd order using 4 knot point (1.135;1.319,15;1.320,75;1.323,25) with a minimum GCV value is 100.227,8. The best measure of goodness with a coefficient of determination value (R-Square) obtained a value 78,8%, because the value is more than 67% make it as a strong model. MAPE value is 3,37% that has a value less than 10 %, make this model have a perfect forecasting ability.

Keywords: Gold; Crude Oil; Composite Stock Price Index; Semiparametric B Spline; GCV

Full Text:

PDF

References

A. Basit, “Pengaruh Harga Emas dan Minyak Dunia terhadap Indeks Harga Saham Gabungan (IHSG) Periode 2016-2019,” J. Manajemen Bisnis Islam., vol. 1, no. 2, pp. 95-110, 2020.
O. Blanchard, Macroeconomics 7th edn. Pearson Prentice Hall, 2017.
D. Ruppert, M. P. Wand, and R. J. Carrol, Semiparametric Regression. Cambridge University Press, 2003.
S. Wulandary and D. I. Purnama, “Perbandingan Regresi Nonparametrik Kernel NWE dan B Splines Pada Pemodelan Rata-Rata Lama Sekolah dan Pengeluaran Perkapita Di Indonesia,” J. Journal of Probability and Statistics., vol. 1, no. 2, pp. 89-97, 2020.
H. Mariani, Emas: Kandungan dan Penggunaan. Elex Media Komputindo, 2010.
D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis 5th edn. John Wiley & Sons, 2013.
Suparti, R. Santoso, A. Prahutama, and A. R. Devi, Regresi Nonparametrik. Wade Group, 2018.
R. L. Eubank, Nonparametric Regression and Spline Smoothing 2nd edn. Marcel Dekker, 1999.
D. Eberly. “B Spline Interpolation on Lattices.” https://www.geometrictools.com(accessed Feb. 12, 2023).
A. P. Sugiantari and I. N. Budiantara, “Analisis Faktor – Faktor yang Mempengaruhi Angka Harapan Hidup di Jawa Timur Menggunakan Regresi Semiparametrik Spline,” J. Sains dan Seni POMITS, vol. 2, no. 1, pp. 2337-3520, 2013.
G. Wahba, Spline Models for Observational Data. SIAM, 1990.
D. Chicco, M. J. Warrens, and G. Jurman, The Coefficient of Determination R-squared is More Informative than SMAPE, MAE, MAPE, MSE, and RMSE in Regression Analysis Evaluation. PeerJ Compt. Sci., 2021.
P. C. Chang, Y. W. Wang, and C. H. Liu, “The Development of a Weighted Evolving Fuzzy Neural Network for PCB Sales Forecasting Expert Systems with Applications,” Expert Systems with Applications, vol. 32, pp. 86-96, 2007.

Refbacks

  • There are currently no refbacks.