Table 6 Regression results using...

Table 6

Regression results using Bayesian model

Panel A: Bayesian regression results
Variables	Posterior mean	95% C.I	MCSE
ACSIZE	−7.43	[−14.05, −0.96]	0.0031
ACIND	−1.53	[−18.39, 14.09]	0.0085
ACGD	−8.32	[−22.58, 6.57]	0.0044
ACD	0.11	[−3.87, 4.28]	0.0165
ACE	−7.60	[−20.23, 6.32]	0.0062
BIG4	−18.87	[−28.17, −7.36]	0.0082
ART	10.89	[3.01, 18.82]	0.0068
FSIZE	−2.99	[−4.18, −1.81]	0.0110
LEV	16.79	[8.14, 25.72]	0.0068
ROA	71.90	[41.54, 106.76]	0.0196
CONSTANT	187.18	[164.96, 210.02]	1.0064

Panel A: Bayesian regression results
Variables	Posterior mean	95% C.I	MCSE
ACSIZE	−7.43	[−14.05, −0.96]	0.0031
ACIND	−1.53	[−18.39, 14.09]	0.0085
ACGD	−8.32	[−22.58, 6.57]	0.0044
ACD	0.11	[−3.87, 4.28]	0.0165
ACE	−7.60	[−20.23, 6.32]	0.0062
BIG4	−18.87	[−28.17, −7.36]	0.0082
ART	10.89	[3.01, 18.82]	0.0068
FSIZE	−2.99	[−4.18, −1.81]	0.0110
LEV	16.79	[8.14, 25.72]	0.0068
ROA	71.90	[41.54, 106.76]	0.0196
CONSTANT	187.18	[164.96, 210.02]	1.0064

Panel B: MCMC diagnostic results
Independent variables	Rc statistics
ACSIZE	1.000
ACIND	1.021
ACGD	1.071
ACD	1.032
ACE	1.038
CONSTANT	1.053
VAR	1.000

Panel B: MCMC diagnostic results
ACSIZE	1.000
ACIND	1.021
ACGD	1.071
ACD	1.032
ACE	1.038
CONSTANT	1.053
VAR	1.000

Note(s): This table represents the result of the relationship between audit committee characteristics and audit report lag using Bayesian regression with Markov Chain Monte Carlo (MCMC) simulation. Total number of draws was 12,500 of which 2,500 were discarded. For definition of variables, refer to Table 2. Panel B shows the MCMC convergence results using Gelman–Rubin diagnostic test

Source(s): Authors’ own creation

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