Global Journal of Management and Business Research, D: Accounting and Auditing, Volume 21 Issue 2

For each of the seven production departments, we estimated a separate regression, for the two models specified above. To ensure that inferences from the estimated models are reasonable, we examined the assumptions underlying OLS regression and checked for potential data problems. First order serial correlation was 0.46 for the Brush & Steel Wool department and ranged between 0.16 and 0.27 for the other six departments for both models. Durbin-Watson statistics indicated that first order serial correlations were significant in all cases. All of the estimation results reported in this paper are after correcting for serial correlation using the Park-Mitchell (1980) variant of the Prais-Winsten (1954) method. We checked the OLS residuals for consistency with the assumption that they are distributed normally. No deviations from normality were indicated at conventional levels of significance using the Kolmogorov test statistic. After the logarithmic transformation of dependent and independent variables, Glesjer's (1969) test did not reject homoskedasticity, but White's (1980) general test for misspecification indicated the presence of heteroskedasticity for all seven departments for model 2, and for three cases for model 1. Therefore, in Table 4 we report results based on White's heteroskedasticity consistent standard errors, but in Table 3, we report standard t- and F-statistics. None of the test results based on White's corrected statistics are different from the corresponding results based on Standard t- and F-statistics for model 1, but for model 2 White's corrected statistic does not reject the null hypothesis that γ 3=0 in Machine Shop and Component Assembly. We also checked for contemporaneous correlations between the residuals for both models for all seven departments. The rejection of the linear model with DLCOST as the only cost driver can also be interpreted as further evidence against proportionality. Test of proportionality with the loglinear version of model 1 corresponds to the test of the null hypothesis: β 1 = 1 because proportionality, (i.e. ILCOST = w*DLCOST) implies, ln (ILCOST) = ln w + 1* ln (DLCOST). The results (not shown) indicate that proportionality is rejected for five departments. Tests of proportionality with the loglinear version of model 2 correspond to the test of constant returns to scale hypothesis: γ 1 + γ 2 + γ 3 = 1. This null hypothesis is rejected for five departments (results not shown). We also estimated both models 1 and 2 separately for each of the five years covered by our data set for each of the seven departments. Proportionality (results not shown) is rejected in 28 out of 35 regressions. Estimation results (not shown) based on weekly data rejected proportionality in all seven departments. Estimation of model 1 based on monthly data (Table 5) indicated that proportionality is rejected for four of the seven departments. In the multiple drivers model 2 based on monthly data (Table 6), γ 0 is significant in only one department. Finally, even in ARMA models (Table 7), the proportionality hypothesis is rejected for five out of the seven departments. However, the magnitudes of β 1 range between 1.00 and 1.05 in four of the seven departments. This suggests a need to evaluate the economic significance of the deviation from proportionality. In summary, all different specifications of our models reject proportionality of costs. different departments, found no significant contemporaneous correlations and, therefore, concluded that there was no need for estimating our models as a system of seemingly unrelated regressions. 7 Global Journal of Management and Business Research Volume XXI Issue II Version I Year 2021 ( ) D © 2021 Global Journals Cost Hierarchy: Evidence and Implications