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The Application of the Law of Large Number in Econometric
Subject area: Science,Engineering and Technology · Area of research: Science
Abstract
This paper explores the fundamental role of the Law of Large Numbers (LLN) in econometrics. The LLN, a cornerstone of probability and statistics, underpins many econometric techniques by ensuring that sample means converge to their expected values as the sample size increases, thus providing a foundation for reliable statistical inference. This article elucidates the importance of the LLN in guaranteeing stable long-term results for averages of random economic events, highlighting its crucial implications for estimation and hypothesis testing in econometric models (multiple regression model). Furthermore, the paper discusses the application of the LLN in where it justifies practices such as portfolio diversification, risk estimation, and asset pricing by enhancing the reliability of empirical estimates with larger datasets. Ultimately, this analysis underscores the LLN's significance in reinforcing the credibility of statistical inference in econometrics and financial econometrics, ensuring that empirical results become more accurate and robust with increasing data.
How to cite this paper
@article{1708489,
author = {Peter Nyam, A. Bishir, Kadiri Simon Angulu, Abba Muhammad Yalwa, Obalisa Yemisi Funmilola},
title = {The Application of the Law of Large Number in Econometric},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {11},
pages = {1018-1023},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708489.pdf},
abstract = {This paper explores the fundamental role of the Law of Large Numbers (LLN) in econometrics. The LLN, a cornerstone of probability and statistics, underpins many econometric techniques by ensuring that sample means converge to their expected values as the sample size increases, thus providing a foundation for reliable statistical inference. This article elucidates the importance of the LLN in guaranteeing stable long-term results for averages of random economic events, highlighting its crucial implications for estimation and hypothesis testing in econometric models (multiple regression model). Furthermore, the paper discusses the application of the LLN in where it justifies practices such as portfolio diversification, risk estimation, and asset pricing by enhancing the reliability of empirical estimates with larger datasets. Ultimately, this analysis underscores the LLN's significance in reinforcing the credibility of statistical inference in econometrics and financial econometrics, ensuring that empirical results become more accurate and robust with increasing data.},
month = {May},
}