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In our model, we allow the appearance of two types of carryovers (residuals) in the observations collected in the second period.
In our empirical model, we allow the country-pair effects ω ij to be correlated with the explanatory variables.
By contrast, under the random-effects model we allow that the true effect could vary from study to study [29].
In using a fully interacted model, we allow each worker and plant characteristic to affect men's and women's wages differently and thus exploit a more flexible framework than the earlier contributions by Belfield and Heywood ([2006]) and Hirsch et al. ([2012]), which also utilise plant-level measures of product market competition.
Throughout this model, we allow transmission to occur between people and places.
In the model, we allow HAM-A scores to vary with treatment from patient to patient.
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In our specification of the a priori model, we allowed for nonlinear path relations using the composite modeling process described in [18].
To avoid overfitting of the model, we allowed a maximum of one variable per each 10 events.
Hence, in the model, we allowed Set to be different for different cueing conditions.
Because of the complexity of our model, we allowed parsimony to dictate the results presented.
In the generalized linear model, we allowed the model to be related to the response variable via a log link function and model selection was based on Akaike's information criterion (AIC).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com