Table 5

Regression results using PCSE model and lag model

VariablesModel 1(a)Model 1(b)Model 2
Coef.Std. err.Coef.Std. err.Coef.Std. err.
ACSIZE−7.22***0.65−1.95***2.72−8.83**2.08
ACIND−3.132.31−5.381.71−7.504.63
ACGD−9.75***1.35−9.34***2.34−9.54**5.22
ACD0.370.45−1.211.498.287.13
ACE−6.05***2.26−9.04***3.26−9.35**2.16
BIG4−7.66**8.17−8.5115.731.735.17
ART8.45***2.159.09***4.313.65***9.05
FSIZE−2.90***0.27−2.87***0.38−7.56**8.28
LEV6.28***1.447.24***2.744.349.25
ROA−9.20***6.78−9.49***19.44−9.27**5.96
Constant−196.48***20.98−165.54***10.50174.5023.70
Year dummyYesYesYes
Observations240240190
R-squared0.300.370.32
Wald χ230.18***16.31***2.78***

Note(s): This table represents the result of the relationship between audit committee characteristics and audit report lag using Equation (1) and(2). Model 1(a) is estimated by using PCSE model with independent autocorrelation structure, whereas the Model 1(b) is estimated by using PCSE model with first-order autocorrelation structure. Model 2 is estimated by using lag model where the proxies of audit committee characteristics are lagged by one year. Standard errors are in parentheses. ***, ** and * indicate level of significance at 1, 5 and 10%, respectively. For definition of variables, refer to Table 2 

Source(s): Authors’ own creation

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