## 4 Results
Recognizing this limitation, our study proposes a methodological shift toward the Generalized Method of Moments (GMM) estimator. This specifically involves leveraging the [PERSON] and [PERSON] (2011), as enhanced by [PERSON] and [PERSON] (2013), using the xtabond2 command in Stata (2013). This advanced econometric approach allows us to address several critical issues, most notably the endogeneity problem. Endogeneity often arises in empirical research due to omitted variable bias, measurement error, or simultaneity. In the context of our study, one concern revolves around simultaneity and the potential bidirectional causality between operating expenses and sales revenue. For example, a company might increase its operating expenses (such as marketing or expanding production capacity) with the aim of boosting \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{6}{c}{**Panel A. Descriptive Statistics (Whole Sample)**} \\ \hline
**Variable** & **Mean** & **Median** & **Standard** & **First** & **Third** \\  & & & **Deviation** & **Quartile** & **Quartile** \\ \hline \(\Delta lnOC_{it}\) & 0.033 & 0.019 & 0.126 & \(-\)0.064 & 0.129 \\ \(\Delta lnSALE_{it}\) & 0.032 & 0.016 & 0.143 & \(-\)0.076 & 0.141 \\ \(DEBT_{it}\) & 0.350 & 0.337 & 0.180 & 0.209 & 0.461 \\ \(SD_{it}\) & 0.538 & 1.00 & 0.498 & 0.00 & 1.00 \\ \hline \multicolumn{6}{c}{**Panel B. Descriptive Statistics (by Country)**} \\ \hline
**Country** & **Observations** & \(\Delta lnOC_{it}\) & \(\Delta lnSALE_{it}\) & \(DEBT_{it}\) & \(SD_{it}\) \\ \hline Denmark & 24 & 0.008 & 0.004 & 0.386 & 0.444 \\ Finland & 44 & 0.004 & 0.036 & 0.375 & 0.485 \\ Iceland & 5 & 0.09 & 0.104 & 0.337 & 0.500 \\ Norway & 90 & 0.09 & 0.046 & 0.313 & 0.684 \\ Sweden & 65 & 0.001 & 0.005 & 0.344 & 0.519 \\ \hline Nordic countries & 228 & 0.026 & 0.029 & 0.348 & 0.571 \\ \hline Spain & 581 & 0.034 & 0.032 & 0.351 & 0.525 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics. sales revenue. Conversely, an increase in sales revenue could provide the company with the necessary resources to finance an increase in operating expenses. Moreover, there may be unobserved factors (omitted variables) that affect both operating expenses and sales revenue, leading to a correlation between the independent variables and the error term in a regression model. For instance, product quality, management efficiency, or market conditions might influence both operating expenses and sales revenue, but if these factors are not included in the model, they can cause endogeneity issues. Such endogeneity can lead to biased and inconsistent parameter estimates if traditional estimation methods are employed. The GMM estimator is particularly suited for our analysis for several reasons. First, it enables us to exploit the panel structure of our data, using internal instruments generated from the lagged values of our variables of interest. This is critical for mitigating the impact of endogeneity by ensuring that our instruments are not correlated with the error terms. Second, the Blundell and Bond extension of the original Arellano and Bond estimator improves efficiency by incorporating additional moment conditions, which is particularly beneficial in the presence of weak instruments [82,83]. By adopting the GMM approach, we aim to achieve more reliable and robust estimates, providing insights into cost behavior that are not confounded by endogeneity. The analysis of the entire sample (Table 2, Model 1) reveals a significant positive relationship between sales revenue changes (\(\Delta lnSALE_{it}\)) and operating cost changes (\(\Delta lnOC_{it}\)), with a coefficient of 0.899 (\(p<0.01\)). This indicates that operating costs increase with sales revenue. The interaction term, \(DEC_{it}\times\Delta lnSALE_{it}\), is negative (\(-0.153\), \(p<0.05\)), implying that the reduction in operating costs during sales declines is not as pronounced, further evidencing the sticky nature of costs.