Maximum Likelihood Model
DOI:
https://doi.org/10.18041/1657-2815/libreempresa.2020v17n2.8027Keywords:
Optimization, Maximization, Asymptotic efficiency, Unbiased, Large samples, Constraints, Wald test, Lagrange multiplierAbstract
The objective of this article is to make an introduction to the Maximum Likelihood (MV) model, widely used for decades in statistics, biometrics, engineering and econometrics. Despite its usefulness, basic econometrics courses continue to emphasize Ordinary Least Squares (OLS) due to its ease of mathematics and conceptual understanding and leave the MV for exercises with commercial software that does include it by default, due to the superiority of the results compared to those of the OLS. MV is widely used for non-linear regressions and large samples, for example, models of dichotomous dependent variables such as Logit and Probit; conditional heteroscedasticity such as GARCH and EGARCH, censored and truncated models, etc. It is expected that as artificial intelligence develops in data science and machine learning, OLS will be discarded.
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