Article Sidebar

Submitted
May 30, 2022
Accepted
Jun 22, 2022
Published
Jun 30, 2022
Download
PDF
Statistic
Read Counter : 545 Download : 665

Main Article Content

Abstract

Time series data is data that is prone to autocorrelation. Autocorrelation is a violation of assumptions in Ordinary Least Square regression. The presence of autocorrelation can make parameter estimates, not BLUE (Best, Linear, Unbiased Estimator). Several methods to overcome autocorrelation include Cochrane-Orcutt and Hildreth-Lu methods. Therefore, this study aimed to compare the Cochrane-Orcutt and Hildreth-Lu methods to deal with autocorrelation in the time series regression of the Gorontalo Human Development Index case in 2010 2021. We used HDI data for Gorontalo Province from 2010-to 2021, taken from the BPS-Statistics Indonesia Gorontalo Province. The method we used was Cochrane-Orcutt and Hildreth-Lu in the case of regression using Ordinary Least Squares (OLS) parameter estimation. The results obtained are that the Cochrane-Orcutt and Hildreth-Lu could overcome autocorrelation. The results of the Durbin Watson test after using both methods show no autocorrelation. However, the Hildreth-Lu method resulted in a lower Root Mean Square Error (RMSE) of 0.147 compared to the RMSE of the OLS model of 0.165 and the RMSE of the Cochrane-Orcutt model of 0.196. Therefore, the Hildreth-Lu method was the best method to overcame autocorrelation in this case.

Keywords

autocorrelation Cochrane-Orcutt Hildreth-Lu regression time series

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite
Tri Subhi, K., & Al Azkiya, A. (2022). Comparison of Cochrane-Orcutt and Hildreth-Lu Methods to Overcome Autocorrelation in Time Series Regression (Case Study of Gorontalo Province HDI 2010-2021). Parameter: Journal of Statistics, 2(2), 30-36. https://doi.org/10.22487/27765660.2022.v2.i2.15913

References

  1. Aprianto, A. (2020). Citation: Metode Cochrane-Orcutt Untuk Mengatasi Autokorelasi Pada estimasi Parameter Ordinary Least Squares. Buletin Ilmiah Mat, Stat, dan Terapannya (Bimaster) 9(1), 95-102.
  2. Asnidar. (2018). Citation: Pengaruh indeks pembangunan manusia (IPM) dan inflasi terhadap pertumbuhan ekonomi di Kabupaten Aceh Timur. Jurnal Samudera Ekonomika, 2(1): 1-12.
  3. Badan Pusat Statistik. (2022, Feb 22). Indeks Pembangunan Manusia menurut Provinsi 2019-2021. Retrieved from https://www.bps.go.id/indicator/26/494/1/-metode-baru-indeks-pembangunan-manusia-menurut-provinsi.html.
  4. Feldstein, M. (1996). Citation: Social security and saving: new time-series evidence. National Tax Journal, 9(2).
  5. Hildreth, C., Lu, J.Y. (1960). Demand Relations with Autocorrelated Disturbances. Michigan (US): Michigan State University, Agricultural Experiment Station, Department of Agricultural economics.
  6. Mendenhall, W., Beaver, R.J., Beaver, B.M. (2012). Introduction to Probability & Statistics: 14th Edition. Boston (US): Brooks/Cole, Cengage Learning.
  7. Montgomery, D.C., Jennings, C.L., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting: 2nd Edition. Hoboken (US): John Wiley and Sons.
  8. United Nations Development Programme. (2022). New threats to human security in the Anthropocene Demanding greater solidarity. New York(US): UNDP.
  9. United Nations Development Programme. (2018). Human Development Indices and Indicators: 2018 Statistical Update. New York (US): UNDP.
  10. Wiranda, L., & Sadikin, M. (2019). Citation: Penerapan Long Short-Term Memory Pada Data Time Series Untuk Memprediksi Penjualan Produk PT. Metiska Farma. JANAPATI (Jurnal Nasional Pendidikan Teknik Informatika), 10, 1–13.