Comparison of the estimation methods used in estimating the nonparametric regression model with random errors subject to the ARMA model.
DOI:
https://doi.org/10.31272/jae.i141.1006Keywords:
Lateral least squares method, SCAD method, kernel smoothers, BIC criterionAbstract
This research dealt with comparing the methods used in estimating the nonparametric regression model with random errors subject to the (ARMA) model, as it was estimated by using the profile least squares method with (SCAD) and employing kernel smoothers for the local polynomial (Local Polynomial Smoother). ) When the degree of the polynomial is (P=1), we get the Local Linear Smoother, and (P=2), we get the Local Quadratic Smoother. The Bayesian Information Criterion, which is symbolized by the symbol (BIC), was also used to determine the rank of the error model. Likewise, the Mean Squared Error criterion, symbolized by the symbol (MSE), was used to compare the methods used in estimating the model. Realistic data was used by taking the data of the Asia Cell Communications Company through the trading of the company’s shares in the Iraqi Stock Exchange for the period from (1/3/2023 to 5/15/2023), and the most important thing that was achieved was the superiority of the positional squared regression method using Side least squares with SCAD having the lowest value for (MSE).
Downloads
References
[1] Al-Kilabi, S. M. M. (2018). Using Different Forecasting Methods to Analyze the Numbers of Patients with Malignant Tumors. Master’s Thesis in Statistics, College of Administration and Economics, University of Karbala.
[2] Hamoud, M. Y. (2000). Comparison of Nonparametric Kernel Estimators for Estimating Regression Functions. Master’s Thesis in Statistics, College of Administration and Economics, University of Baghdad.
[3] Jabbarah, A. K. (2019). Comparison of Some Robust Estimation Methods for a Regression Model with an Unstable Explanatory Variable and Autocorrelated Errors. PhD Dissertation in Statistics, College of Administration and Economics, Al-Mustansiriya University.
[4] Majeed, G. H. (2016). Determining the Best Robust Smoothing Method for Estimating a Nonparametric Regression Model with a Practical Application. PhD Dissertation in Statistics, College of Administration and Economics, University of Baghdad.
[5] Rashid, H. A. (2014). Nonparametric Smoothers for the Varying and Partially Varying Coefficients Model. PhD Dissertation in Statistics, College of Administration and Economics, University of Baghdad.
[6] Shihab, T. A. S. (2016). Some Semiparametric Methods in Estimating and Variable Selection for the Single Index Model. PhD Dissertation in Statistics, College of Administration and Economics, University of Baghdad.
[7] Al-Jubouri, A. M. A. (2018). Comparison of Penalized Likelihood Methods for Variable Selection and Parameter Estimation in Poisson Regression Model. Master’s Thesis, College of Computer Science and Mathematics, University of Mosul.
[8] Mutair, H. M., & Al-Sharout, M. H. (2011). Comparison of Some Nonparametric Regression Smoothing Methods Using Simulation. University of Al-Qadisiyah, College of Computer Science and Mathematics, College of Administration and Economics.
[9] Abboudi, E. H., & Kamil, R. T. (2021). Comparison of Some Estimation Methods for a Semiparametric Model for Longitudinal Data. Journal of Administration and Economics, (127), March 2021, pp. 249–261, University of Baghdad.
[10] Oklah, S. J. (2017). Using Box-Jenkins Models to Forecast Traffic Accident Deaths in the Holy Karbala Governorate for the Period 2010–2015. Master’s Thesis in Statistical Sciences, College of Administration and Economics, University of Karbala.
[11] Abed, H. T., & Badr, D. H. (2019). Comparison of Some Estimation Methods for Nonparametric Regression Model Using Simulation. Journal of Administration and Economics, Al-Mustansiriya University, (42).
12 . Yan Li , Some contributions to nonparametric modeling with correlated data .the Pennsylvania state university the graduate school .2008 .
13 . Alexandra Soberon , Juan M. Rodriguez-Poo , Nonparametric and semi-parametric panel data models: recent developments . July 26, 2016 .
14 .Gilles Gasso , Alain Rakotomamonjy and Stephane Canu . solving non- convex lasso-type problems with DC programing . (2008) IEEE xplor , pp. 450-455. DOI: https://doi.org/10.1109/MLSP.2008.4685522
15 . Liangjun Su, Yonghui Zhang , Variable Selection in Nonparametric and Semiparametric Regression Models, September 19, 2012.
16 . Jianqing Fan and Runze Li , Variable Selection via Nonconcav Penalized Likelihood and its Oracle Properties , Version of record first published : 31 Dec 2011.
17 . Jian Huang1 and Huiliang Xie . Asymptotic oracle properties of SCAD-penalized least squares estimators , Festschrift for Piet Groeneboom (2007), pp. 149-166 . DOI: https://doi.org/10.1214/074921707000000337
18 . Juan M .Vilar Fernandez and Mario Francisco Fernandez , Local polynomial Regression smoothers with AR- error Structure , Sociedad de Estadisticale Investigacion Operativa , Test ( 2002 ) Vol . 11 , No . 2 . pp . 439 – 464 DOI: https://doi.org/10.1007/BF02595716
19 . Runze Li, Yan Li, Local Linear Regression for Data with AR Errors , Acta Mathematicae Applicatae Sinica , English Series Vol. 25, No. 3 (2009) 427–444. DOI: https://doi.org/10.1007/s10255-008-8813-3
20 . Guo-liang Fan , Han-ying Liang & Li-xing Zhu , Penalized profile least squares-based statistical inference for varying coeffcient partially linear errors-in-variables models , Received November 4, 2016; accepted May 29, 2017
21 . Wolfgang Hardle and James Stephen Marron , optimal bandwidth selection in nonparametric regression function estimation .the annals of statistics, vol. 13, no. 4 ( dec , 1985 ) , pp. 1465-1481. DOI: https://doi.org/10.1214/aos/1176349748
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Ali Hashem Bani Hussein , Hossam Abdel Razzaq Rasheed
This work is licensed under a Creative Commons Attribution 4.0 International License.
The journal of Administration & Economics is an open- access journal that all contents are free of charge. Articles of this journal are licensed under the terms of the Creative Commons Attribution International Public License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode) that licensees are unrestrictly allowedto search, download, share, distribute, print, or link to the full text of the articles, crawl them for indexing and reproduce any medium of the articles provided that they give the author(s) proper credits (citation). The journal allows the author(s) to retain the copyright of their published article.
Creative Commons-Attribution (BY) 
