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References in periodicals archive ?
When using the ordinary least square method and the linear regression method, the variance of the estimated coefficient is larger (Wang, 2014).
First, we employed linear regression by ordinary least squares estimation on the log-transformed weight and length data.
Linear Ordinary Least Squares with an Inequality Constraint.
Keywords: Linear models Ordinary least square Method Recursive Test
As shown in equation 1, the variance of an ordinary least square coefficient derived from a SRS may be expressed as:
Dynamic Ordinary Least Squares (DOLS) is attributed to Saikkonen (1992) and Stock & Watson (1993).
A difference-in-differences model estimated via ordinary least squares regressions was employed to produce estimates for the effects of disease management programs on the outcome variables.
In the present study, we aim to fill the gap in the literature and extend the earlier studies in a number of ways: to the best of our knowledge, this study is the first of its kind using Canadian data; this study uses the Ordered Probit method as well as Ordinary Least Squares and Propensity Score Matching methods; and unlike previous studies, which used data from either lower-level or upper-level economics courses, this study uses data from both levels of economics courses.
Therefore, in this case, the supply and demand models were estimated independently, using the ordinary least squares method and pooled cross-sectional (50 states and the District of Columbia) and quarterly time series data for the period 1980-2003.
The authors use this example to explain nonstandard error issues and to compare discontinuity design with traditional ordinary least squares.
Collinearity does not violate any of the assumptions of ordinary least squares regression and thus gives unbiased predictions from the linear combination of predictors.
Some simplicity is achieved by the authors' advocacy of only two estimation methods, namely ordinary least squares and instrumental variables estimation (IVE) and its special case, two-stage least squares estimation, and also by the insistence on linear models, regardless of whether the relationship is linear or not.