Most econometric models suffer from the problems of autocorrelation, multicollinearity, and heteroscedasticity. This paper presents a brief on these problems, their causes, how can be detected, tested, and minimized. The OLS method is based on several assumptions, and if these assumptions are fulfilled, we obtain unbiased, consistent, and efficient estimates (less variance compared to other methods). We discuss these problems as follows: First: the problem of multicollinearity Second: The problem of autocorrelation Third: Variation Heteroscedasticity. This article presents inference for many commonly used estimators - Box Plot on Normal Distribution, skewness, kurtosis, and Assumptions for Multiple Regression, that are asymptotically normally distributed. The Section Inference focuses on multicollinearity and hypothesis tests based on correlation matrix estimates measures a goodness of fit that are determine if a data set is well-modeled, heteroskedastic and, if relevant, Autocorrelation test. The Section Model Tests and Diagnostics summarizes tests of model adequacy and model diagnostics. The Section of practical application presents diagnostic tests that are used to judge the quality of the model, whether it is efficiency, convenience, fitness and flawless. The validity its ability to measure sensitivity and specificity. where it is essential indicators of test accuracy and allow to determine the appropriateness of the diagnostic tool.
| Published in | Advances (Volume 3, Issue 3) |
| DOI | 10.11648/j.advances.20220303.12 |
| Page(s) | 49-59 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Multicollinearity, Autocorrelation, Heteroscedasticity
| [1] | Tae, K. H., Roh, Y., Oh, Y. H., Kim, H., & Whang, S. E. (2019, June). Data cleaning for accurate, fair, and robust models: A big data-AI integration approach. In Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning (pp. 1-4). |
| [2] | Gharatkar, S., Ingle, A., Naik, T., & Save, A. (2017, March). Review preprocessing using data cleaning and stemming technique. In 2017 international conference on innovations in information, embedded and communication systems (iciiecs) (pp. 1-4). IEEE. |
| [3] | Mahmoud Abd El Fattah (April, 2022), Faculty of Graduate Studies for Statistical Research and Econometrics of Statistical Studies and Research - Statistics Department, Cairo University. |
| [4] | Hu, Y., & Plonsky, L. (2021). Statistical assumptions in L2 research: A systematic review. Second Language Research, 37 (1), 171-184. |
| [5] | Meuleman, B., Loosveldt, G., & Emonds, V. (2015). Regression analysis: Assumptions and diagnostics. The SAGE handbook of regression analysis and causal inference, 83-110. |
| [6] | Parke, C. S. (2013). Module 7: evaluating model assumptions for multiple regression analysis. Essential first steps to data analysis: Scenario-based examples using SPSS, 147-178. |
| [7] | Garson, G. D. (2012). Testing statistical assumptions. Asheboro, NC: Statistical Associates Publishing. |
| [8] | Osborne, J. W., & Waters, E. (2002). Four assumptions of multiple regression that researchers should always test. Practical assessment, research, and evaluation, 8 (1), 2. |
| [9] | Ezekiel, M. (1925). The assumptions implied in the multiple regression equation. Journal of the American Statistical Association, 20 (151), 405-408. |
| [10] | Roberto Pedace, (2016), Typical Problems Estimating Econometric Models, From The Book: Econometrics For Dummies. |
| [11] | https://docs.google.com/spreadsheets/d/1rFz1kqV56NvQfw0vwHk4ZNfMzJL4IhUW/edit?usp=sharing&ouid=106936511433736093821&rtpof=true&sd=true. |
APA Style
Abeer Mohamed Abd El Razek Youssef. (2022). Diagnostic Tests for Econometric Problems in Multiple Regression Analysis. Advances, 3(3), 49-59. https://doi.org/10.11648/j.advances.20220303.12
ACS Style
Abeer Mohamed Abd El Razek Youssef. Diagnostic Tests for Econometric Problems in Multiple Regression Analysis. Advances. 2022, 3(3), 49-59. doi: 10.11648/j.advances.20220303.12
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Download@article{10.11648/j.advances.20220303.12,
author = {Abeer Mohamed Abd El Razek Youssef},
title = {Diagnostic Tests for Econometric Problems in Multiple Regression Analysis},
journal = {Advances},
volume = {3},
number = {3},
pages = {49-59},
doi = {10.11648/j.advances.20220303.12},
url = {https://doi.org/10.11648/j.advances.20220303.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.advances.20220303.12},
abstract = {Most econometric models suffer from the problems of autocorrelation, multicollinearity, and heteroscedasticity. This paper presents a brief on these problems, their causes, how can be detected, tested, and minimized. The OLS method is based on several assumptions, and if these assumptions are fulfilled, we obtain unbiased, consistent, and efficient estimates (less variance compared to other methods). We discuss these problems as follows: First: the problem of multicollinearity Second: The problem of autocorrelation Third: Variation Heteroscedasticity. This article presents inference for many commonly used estimators - Box Plot on Normal Distribution, skewness, kurtosis, and Assumptions for Multiple Regression, that are asymptotically normally distributed. The Section Inference focuses on multicollinearity and hypothesis tests based on correlation matrix estimates measures a goodness of fit that are determine if a data set is well-modeled, heteroskedastic and, if relevant, Autocorrelation test. The Section Model Tests and Diagnostics summarizes tests of model adequacy and model diagnostics. The Section of practical application presents diagnostic tests that are used to judge the quality of the model, whether it is efficiency, convenience, fitness and flawless. The validity its ability to measure sensitivity and specificity. where it is essential indicators of test accuracy and allow to determine the appropriateness of the diagnostic tool.},
year = {2022}
}
TY - JOUR T1 - Diagnostic Tests for Econometric Problems in Multiple Regression Analysis AU - Abeer Mohamed Abd El Razek Youssef Y1 - 2022/07/29 PY - 2022 N1 - https://doi.org/10.11648/j.advances.20220303.12 DO - 10.11648/j.advances.20220303.12 T2 - Advances JF - Advances JO - Advances SP - 49 EP - 59 PB - Science Publishing Group SN - 2994-7200 UR - https://doi.org/10.11648/j.advances.20220303.12 AB - Most econometric models suffer from the problems of autocorrelation, multicollinearity, and heteroscedasticity. This paper presents a brief on these problems, their causes, how can be detected, tested, and minimized. The OLS method is based on several assumptions, and if these assumptions are fulfilled, we obtain unbiased, consistent, and efficient estimates (less variance compared to other methods). We discuss these problems as follows: First: the problem of multicollinearity Second: The problem of autocorrelation Third: Variation Heteroscedasticity. This article presents inference for many commonly used estimators - Box Plot on Normal Distribution, skewness, kurtosis, and Assumptions for Multiple Regression, that are asymptotically normally distributed. The Section Inference focuses on multicollinearity and hypothesis tests based on correlation matrix estimates measures a goodness of fit that are determine if a data set is well-modeled, heteroskedastic and, if relevant, Autocorrelation test. The Section Model Tests and Diagnostics summarizes tests of model adequacy and model diagnostics. The Section of practical application presents diagnostic tests that are used to judge the quality of the model, whether it is efficiency, convenience, fitness and flawless. The validity its ability to measure sensitivity and specificity. where it is essential indicators of test accuracy and allow to determine the appropriateness of the diagnostic tool. VL - 3 IS - 3 ER -
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