vast majority of heat pipes manufactured today are conventional wicked circular cross-section units, there is an increasing trend towards miniaturization and the use of micro-groove type structures as wicks.
However, it may not perform as well in the presence of persistent explanatory variables, serially correlated errors, and relatively short time periods in comparison with the number of cross-section units (Reed and Webb 2010).
However, due to a larger database in our time-series cross-section data (35 point in time and 10 cross-section units),e we are now able to estimate effects of the different independent variables in different time periods.
The asymptotic validity of the test procedure is predicated on the number of time series observations, T, being large relative to the number of cross-section units, N, in that: either (i) N is fixed as T→∞; or, (ii) N2/T→0 as both T and N diverge, jointly, to infinity.
Reed and Webb (2010: 11) find that the PCSE estimator does not perform as well when the explanatory variables are highly persistent, the error terms are serially correlated and the number of time periods is relatively short in comparison with the number of cross-section units.
As in Bai and Ng (2002), we normalize ( pi_{it} ) for each cross-section unit to have a unit variance.
While this comes at the cost of disregarding cross-province differences by imposing the same underlying structure for each cross-section unit, Gavin and Theodorou emphasize that the panel approach allows one to uncover common dynamic relationships [63].
In order to carry out these tests first for each variable, we estimated an auroregressive (AR) process, and then an Augmented Dickey Fuller (ADF) test regression was fitted for each cross-section unit.
All scattering curves have been calibrated into absolute units (i.e., macroscopic scattering cross sections in units of cross-section per unit volume cm/cm = cm 1).
So we refer to such an absorption cross section per unit length as simply an absorption cross section in this study.
