Loan Guarantees in a Crisis: An Antidote to a Credit Crunch
In: Federal Reserve Bank of Kansas City Working Paper No. 21-03
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In: Federal Reserve Bank of Kansas City Working Paper No. 21-03
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In: Federal Reserve Bank of Kansas City Working Paper No. 20-19
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In: Federal Reserve Bank of Kansas City Working Paper No. 17-06
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Working paper
In: FEDS Working Paper No. 2014-49
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Working paper
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In: FEDS Working Paper No. 2018-085
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In: FEDS Working Paper No. 2018-85
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In: The journal of financial research: the journal of the Southern Finance Association and the Southwestern Finance Association, Band 44, Heft 4, S. 695-723
ISSN: 1475-6803
AbstractWe introduce a new software package for determining linkages between datasets without common identifiers. We apply this to three datasets commonly used in academic research on syndicated lending: Refinitiv LPC DealScan, S&P Global Market Intelligence Compustat, and National Information Center Structure Data. We benchmark the results of our match using results from the literature and previously matched files that are publicly available. We find that company level matching is enhanced by careful cleaning of the data and considering hierarchical relationships. The R package for one of the company‐level matches can be found on GitHub and CRAN, which can be considered a general toolkit to match different firm‐level datasets with one another.