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The proposed sales accelerator model can be considered as a dynamic panel data equation where the lags of the dependent variable along and the other variables are taken as an independent variable to verify the cash flow sensitivity of investment.
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As usual in dynamic panel data, this equation cannot be estimated by calling on a within estimator since the demeaned lagged migration rate m it − 1 − m ¯ i is not independent of the demeaned error term ε it − 1 − ε ¯ i, where m ¯ i and ε ¯ i are the time average of respectively the migration rate and the error term.
We have applied several GMM specifications for dynamic panel data models.
The dynamic panel data approach, generalized method of moments (GMM), is used for estimation purpose.
In the empirical section we performed a Dynamic Panel Data model.
In this paper, we discus and address this problem using spatial dynamic panel data methods.
Linear regression based on dynamic panel data, examined the impact of working capital on competitive performance.
Table 2 reports the results of the specifications presented in (6) as the dynamic panel data model.
Results from dynamic panel data analyses reveal stronger temporal persistence in science- than in practice-based innovative search activities.
It is therefore standard in the literature to use dynamic panel data methods such as those proposed by Arellano and Bond (1991) to overcome lag dependency.
The study is conducted using the generalized method of moments (GMM) estimator on dynamic panel data for the period of (2009 2015) on 768 listed manufacturing firms.
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