Add to ORKGWhen some variables to be used in a regression analysis contain measurement error, the OLS estimator will be inconsistent. When these variables are left out, OLS will also be inconsistent. In an appropriate metric the first type of inconsistency is smaller than the second type.</p
We aim to raise awareness of the omitted variable bias (i.e., one special form of endogeneity) and h...
This study attempts to investigate the effect of outliers on estimation of parameters in regression ...
Statistical remedies exist for most configurations of missing data, but these remedies require speci...
When some variables to be used in a regression analysis contain measurement error, the OLS estimator...
The Ordinary Least Squares (OLS) method is the most widely used method to estimate the parameters o...
In this paper, I discuss three issues related to bias of OLS estimators in a general multivariate s...
Measurement error biases OLS results. When the measurement error variance in absolute or relative (r...
Omitted variables in regression analysis can lead to the erroneous conclusion that autocorrelation o...
Whenever nonexperimental methods are used to test a hypothesis and 1 or more predictor (inde-pendent...
We analyse models in which additional “controls” or proxies are included in a regression. This might...
Omitted variables in regression analysis can lead to the erroneous conclusion that autocorrelation o...
This paper summarizes and confronts the relationships between six well-known regressions applied in ...
This paper summarizes and confronts the relationships between six well-known regressions applied in ...
In a polynomial regression with measurement errors in the covariate, which is supposed to be normall...
This article considers a linear regression model in which some observations on an explanatory variab...
We aim to raise awareness of the omitted variable bias (i.e., one special form of endogeneity) and h...
This study attempts to investigate the effect of outliers on estimation of parameters in regression ...
Statistical remedies exist for most configurations of missing data, but these remedies require speci...
When some variables to be used in a regression analysis contain measurement error, the OLS estimator...
The Ordinary Least Squares (OLS) method is the most widely used method to estimate the parameters o...
In this paper, I discuss three issues related to bias of OLS estimators in a general multivariate s...
Measurement error biases OLS results. When the measurement error variance in absolute or relative (r...
Omitted variables in regression analysis can lead to the erroneous conclusion that autocorrelation o...
Whenever nonexperimental methods are used to test a hypothesis and 1 or more predictor (inde-pendent...
We analyse models in which additional “controls” or proxies are included in a regression. This might...
Omitted variables in regression analysis can lead to the erroneous conclusion that autocorrelation o...
This paper summarizes and confronts the relationships between six well-known regressions applied in ...
This paper summarizes and confronts the relationships between six well-known regressions applied in ...
In a polynomial regression with measurement errors in the covariate, which is supposed to be normall...
This article considers a linear regression model in which some observations on an explanatory variab...
We aim to raise awareness of the omitted variable bias (i.e., one special form of endogeneity) and h...
This study attempts to investigate the effect of outliers on estimation of parameters in regression ...
Statistical remedies exist for most configurations of missing data, but these remedies require speci...