=.715, adjusted R-square
=.518, and F = 62.3 is significant at p=0.000 with df =1.
The model summary showed R value, R-square
value, F value and P-value.
The same correlation value R becomes 0.562 (p<0.05) and R-square
value increases to 0.316 when the AC is added to control variables as an IV to predict AC.
Table 2 presents the results of alphas, betas and adjusted R-square
by computing CAPM regression based on 10-day MAPs.
is 59.7%, R-square
is 35.6%, adjusted R-square
is 34.6% and estimation of the standard error is 1.044.
Finally, to understand the economic causes of the time variation of market integration, I examine the relation between the integration R-square
and business-cycle related macroeconomic variables, including the unemployment rate, real estate loan growth and the default spread (Fama and French, 1989; Bernanke, Gertler, and Gilchrist 1996).
increase due to interaction = 0.0030; p = 0.3162).
newspapers, state television (PTV), private news channels, radio Pakistan, weekly/monthly news magazines, online/internet news), bring change in R-square
In a given model value of R-square
is quite high that cause the estimated reuts doubtful but such higher value is due to inclusion of large number of lags and having maximum significant value of lags.
where a2 = 4.378, b2 = -0.32, c2 = 95.62, d2 = -0.0007474, and "x" is the MgO concentration ([micro] x [ml.sup.-1]) (SSE: 0.8483, R-square
: 0.9969, adjusted R-square
: 0.9923, and RMSE: 0.6513).
Regardless, when standard errors are clustered, the "text-book" adjusted R-square
statistic is biased upward since the data contain far fewer independent degrees of freedom than the number of observations.