
ABSTRACT This paper presents a do-it-yourself algorithm to generate the historical GVKEY-CIK link table. The proposed algorithm features a technique of pre-classifying sample data into different treatment subgroups and utilizing historical firm information available from the source data to increase (reduce) matching efficiency (errors). Simulation results show that our algorithm is superior to applying only conventional name matching operations over the whole sample: 57.5 percent of the overall matching results are error free ex ante, and for the remaining 42.5 percent of data, records without Type I errors (with Type II errors) increase (decrease) by 34.0 percent (59.4 percent) when the optimal threshold is used. JEL Classifications: C89; M40; G10; G18.
Link table, 330, Compustat, EDGAR, 10-K, 004
Link table, 330, Compustat, EDGAR, 10-K, 004
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
