Is there a Superior Distance Function for Matching in Small Samples?
Eva Dettmann, Claudia Becker, Christian Schmeißer
Abstract
The study contributes to the development of ’standards’ for the application of matching algorithms in empirical evaluation studies. The focus is on the first step of the matching procedure, the choice of an appropriate distance function. Supplementary o most former studies, the simulation is strongly based on empirical evaluation ituations. This reality orientation induces the focus on small samples. Furthermore, ariables with different scale levels must be considered explicitly in the matching rocess. The choice of the analysed distance functions is determined by the results of former theoretical studies and recommendations in the empirical literature. Thus, in the simulation, two balancing scores (the propensity score and the index score) and the Mahalanobis distance are considered. Additionally, aggregated statistical distance functions not yet used for empirical evaluation are included. The matching outcomes are compared using non-parametrical scale-specific tests for identical distributions of the characteristics in the treatment and the control groups. The simulation results show that, in small samples, aggregated statistical distance functions are the better
choice for summarising similarities in differently scaled variables compared to the
commonly used measures.
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Equity and Bond Market Signals as Leading Indicators of Bank Fragility
Reint E. Gropp, Jukka M. Vesala, Giuseppe Vulpes
Journal of Money, Credit and Banking,
No. 2,
2006
Abstract
We analyse the ability of the distance to default and subordinated bond spreads to signal bank fragility in a sample of EU banks. We find leading properties for both indicators. The distance to default exhibits lead times of 6-18 months. Spreads have signal value close to problems only. We also find that implicit safety nets weaken the predictive power of spreads. Further, the results suggest complementarity between both indicators. We also examine the interaction of the indicators with other information and find that their additional information content may be small but not insignificant. The results suggest that market indicators reduce type II errors relative to predictions based on accounting information only.
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