This paper compares ordinary least squares (OLS), weighted least squares (WLS), and adaptive least squares (ALS) by means of a Monte Carlo study and an application to two empirical data sets. Overall, ALS emerges as the winner: It achieves most or even all of the efficiency gains of WLS over OLS when WLS outperforms OLS, but it only has very limited downside risk compared to OLS when OLS outperforms WLS
When ratings are collected using an incompletely crossed design, differences in rater stringency may...
Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable ...
In the present thesis we deal with the linear regression models based on least squares. These method...
When testing for the equality of regression slopes based on ordinary least squares (OLS) estimation,...
These days, it is common practice to base inference about the coefficients in a hetoskedastic linear...
Conditional heteroskedasticity of the error terms is a common occurrence in financial factor models,...
In pursuit of efficiency, we propose a new way to construct least squares estimators, as the minimiz...
This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear p...
A Monte Carlo simulation is used to compare estimation and inference procedures in least absolute va...
We consider inference for linear regression models estimated by weighted-average least squares (WALS...
This paper shows how asymptotically valid inference in regression models based on the weighted least...
Equal weights are an alternative weighting procedure to the optimal weights offered by ordinary leas...
Model averaging has become a popular method of estimation, following increasing evidence that model ...
Stable autoregressive models of known finite order are considered with martingale differences errors s...
textFull weighted least squares (full WLS) and robust weighted least squares (robust WLS) are curre...
When ratings are collected using an incompletely crossed design, differences in rater stringency may...
Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable ...
In the present thesis we deal with the linear regression models based on least squares. These method...
When testing for the equality of regression slopes based on ordinary least squares (OLS) estimation,...
These days, it is common practice to base inference about the coefficients in a hetoskedastic linear...
Conditional heteroskedasticity of the error terms is a common occurrence in financial factor models,...
In pursuit of efficiency, we propose a new way to construct least squares estimators, as the minimiz...
This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear p...
A Monte Carlo simulation is used to compare estimation and inference procedures in least absolute va...
We consider inference for linear regression models estimated by weighted-average least squares (WALS...
This paper shows how asymptotically valid inference in regression models based on the weighted least...
Equal weights are an alternative weighting procedure to the optimal weights offered by ordinary leas...
Model averaging has become a popular method of estimation, following increasing evidence that model ...
Stable autoregressive models of known finite order are considered with martingale differences errors s...
textFull weighted least squares (full WLS) and robust weighted least squares (robust WLS) are curre...
When ratings are collected using an incompletely crossed design, differences in rater stringency may...
Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable ...
In the present thesis we deal with the linear regression models based on least squares. These method...