134 research outputs found
Identifying all distinct sample p-p plots, with an application to the exact finite sample distribution of the L1-FCvM test statistic
(Non-)Insurance Markets, Loss Size Manipulation and Competition:Experimental Evidence*
The common view that insurer buyer power may effectively counteract provider market power critically rests on the idea that consumers and insurers have a joint interest in pushing for price and cost reductions. We develop theory and provide experimental evidence that the interests of insurers and consumers may be misaligned when insurers have the power to influence the service supplier's cost. Insurers with such buyer power may benefit from increasing initial loss sizes to create demand for insurance. Insurer competition eliminates their profits but markets do not return to the initial non-insurance state. This constitutes a welfare loss
A K-sample Homogeneity Test based on the Quantification of the p-p Plot
We propose a quantification of the p-p plot that assigns equal weight to all distances between the respective distributions: the surface between the p-p plot and the diagonal. This surface is labelled the Harmonic Weighted Mass (HWM) index. We introduce the diagonal-deviation (d-d) plot that allows the index to be computed exactly under all circumstances. For two balanced samples absent ties the finite sample distribution of the HWM index is derived. Simulations show that in most cases unbalanced samples and ties have little effect on this distribution. The d-d plot allows for a straightforward extension to the K-sample HWM index. As we have not been able to derive the distribution of the index for K>2, we simulate significance tables for K=3,...,15. An example involving economic growth rates of the G7 countries illustrates that the HWM test can have better power than alternative Empirical Distribution Function tests
Empirical relevance of the Hillman condition for revealed comparative advantage: 10 stylized facts
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