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For Classical Regression Model the OLS or Ordinary Least Squares - estimators (or the betas) are BLUE (Best, Linear, Unbiased, Estimator) when :

  1. The regression is linear in the coefficients, it is correctly specified and has an additive error term.
  2. Mean of the error term is zero. (Include a constant term in the regression (B0 which will force the mean to be zero)
  3. The independent variables are not correlated with the error term. (If they are correlated then the betas will be biased.)
  4. Observations of the error term (the residuals) are not correlated with each other.
  5. The error term has a constant variance (Homoskedasticity)
  6. No independent variable is a perfect linear function of any of the other independent variable. (If this is true - multicollinearity will occur)
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Why are unbiased estimators preferred over biased estimators?

Unbiased estimators are preferred over biased estimators because they, on average, accurately reflect the true value of the parameter being estimated, leading to more reliable conclusions. While biased estimators can be closer to the true value in some specific cases, their systematic error can mislead interpretations and decisions. Unbiased estimators ensure that the estimates converge to the true parameter value as sample size increases, enhancing their overall credibility in statistical analysis.


What is the advantage of using OLS method?

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Maximum likelihood estimators of the logistic distribution?

The likelihood has to be maximized numerically, as the order statistic is minimal sufficient


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Maximum likelihood estimators of the Cauchy distribution cannot be written in closed form since they are given as the roots of higher-degree polynomials. Please see the link for details.

Related Questions

In the presence of heteroscedasticity OLS estimators are biased as well as inefficient?

They are still unbiased however they are inefficient since the variances are no longer constant. They are no longer the "best" estimators as they do not have minimum variance


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What is the advantage of using OLS method?

OLS leads to a closed-form solution. The problems in other metrics such as L1 do not. Furthermore, the statistical theory for OLS is much richer than for other metrics in spite of the fact that OLS leads to difficulties in dealing with 'outliers'.


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When would you use 2sls instead of ils?

Two-stage least squares (2SLS) is used instead of ordinary least squares (OLS) when there is concern about endogeneity in the regression model, such as when an independent variable is correlated with the error term. This typically arises in the presence of omitted variable bias, measurement error, or simultaneous causality. 2SLS helps to provide consistent estimators by using instrumental variables that are correlated with the endogenous explanatory variables but uncorrelated with the error term. In contrast, OLS is appropriate when all variables are exogenous and there are no such concerns.


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