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Mean 1 obtained from these regressions is 07156

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Unformatted text preview: tion of the Cross-Sectional Distribution of the Firm-Specific Parameters 0.2300 0.2934 ˆ Larcker [1987]. We report the results in table 3. The mean β 1 obtained from ˆ these regressions is 0.7156 (aggregate z -statistic = 139.82) and the mean β 2 is −0.2403 (aggregate z -statistic = −14.48), supporting the sticky costs hypothesis. The z -statistics require the assumption of cross-sectional independence ¯ (i.e., r = 0). To check the validity of the cross-sectional independence assumption, we randomly selected 100 firms with complete time series of data from our sample firms and estimated the pairwise correlation between the regression residuals. The mean correlation is only 0.0289, indicating that cross-sectional dependence is not a serious concern in our data. When there is cross-sectional variation in the coefficients, an alternative and more direct approach (to aggregation of individual time-series regressions) is to estimate a single random coefficients model (Green [1997, p. 669]). We estimated a random coefficients model under the assumption that the coefficients β1 and β2 are normally distributed across firms. We report the results of estimating this model for the 1,817 firms in table 3, as ˆ well. The mean β 1 obtained from these estimations is 0.6717 (t -statistic = ˆ 76.36) and the mean β 2 is −0.1405 (t -statistic = −9.46), supporting the sticky costs hypothesis. 58 3.5 M. C. ANDERSON, R . D. BANKER , AND S. N. JANAKIRAMAN OTHER ROBUSTNESS TESTS To provide assurance that the results were not systematically affected by inflation, we converted all SG&A and revenue amounts to equivalent 1984 dollars and reestimated model (I) for the pooled sample with the ˆ inflation-adjusted amounts. The results, β 1 = 0.5466 (t -statistic = 160.92) ˆ2 = −0.1721 (t -statistic = −24.18), are similar to those reported for and β model (I). One of the components of SG&A expense is foreign currency translation adjustments (annual Compustat #150). Because these adjustments introduce noise into the measure of SG&A, we removed them from the SG&A data and estimated model (I) again. Results of this estimation, ˆ ˆ β 1 = 0.5983 (t -statistic = 85.81) and β 2 = −0.2077 (t -statistic = −13.84), are also similar to those reported for the initial estimation. It may be argued that there are two-way relations between SG&A costs and sales revenue. Expenditures on SG&A costs, such as marketing costs, affect sales volume whereas sales volume affects SG&A costs. To address this potential simultaneity, we estimated a simultaneous equations model that included changes in SG&A expenditures and sales revenue as endogenous variables. In the first equation of this model, the change in SG&A costs is expressed as a function of the change in sales revenue. In the second equation, the change in sales revenue is expressed as a function of the change in SG&A costs. Lagged variables are included in both...
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