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Climate indices are considered to describe the non-stationarity in the variable of interest and to estimate the quantiles in this case.
To determine the curvature parameter for b 1 and b 2, we analyze the effect of an incremental increase in the variable of study.
Our DD and pseudo-panels design requires large N samples to avoid random fluctuations in the variable of interest from one year to the next.
The ME for a continuous covariate is computed as the change in the probability from the mean value after an increase of one standard deviation in the variable of interest, with all other variables held at their observed values.
Bi-temporal change detection compares two periods in time by removing all variances caused by non-relevant factors to measure change only caused by differences in the variable of interest (Green et al. 1994).
In economic terms, this condition implies that, in the absence of the treatment, the evolution in the variable of interest (in our case, leverage) would have been identical for both the treatment and control groups.
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In this study the variable of concern were placed in a stratified model adaptation.
These last scenarios characterize the possible trends in the variables of change previously identified.
Control laws which are bounded in the variables of position and velocity are constructed.
Furthermore, using RP data makes it difficult to observe the effect of large variations in the variables of interest.
The fact that the proposed measures do not a capture a unidimensional variable may be explained by the multidimensionality in the variables of interest.
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