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Since the fixed effects themselves are generally not of interest, to ease estimation they are removed using a within transformation.
We transform the data as in Cameron and Trivedi (2009) by calculating the variables' mean over time for every firm and subsequently performing a within transformation.
The fact that age and a time trend evolve in a similar way could also raise concerns in the context of the estimation of a fixed effects model using a within transformation.
The within transformation removes time invariant fixed effects by subtracting from each variable its time mean.
We will estimate the within transformation of Eq. 3 by OLS.
Stock-specific and time-specific effects are controlled using the within transformation.
Similar(43)
To account for the fixed effect, a within-transformation is applied, as in the "Local average models" section.
To proceed, as in the local average and local aggregate model, Zη and Lν are replaced by a network-level fixed effect, (boldsymbol {L}tilde {boldsymbol {nu }}), which is then removed using the within-transformation, J.
Return is regressed on these dummy variables by means of the following model: begin{aligned} R_{i,t} = sum _{a = 0}^{4} iota _a^{f} A_{a,t} + epsilon _{i,t}^{f}, end{aligned} (16 where we apply the within-transformation defined in Eq. (6).
Because these unobservable individual effects were cancelled out by within-transformation in FE-regression (for more technical details: [ 7] and Additional file 1), time-constant unobserved heterogeneity is no longer a problem.
Within the transformation process, model-based analyses have become indispensable for advice addressing a diverse set of questions.
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