UC Irvine

Empirical work on bank loans typically regresses loan spreads (markups of loan interest rates over a benchmark rate) on observed characteristics of banks, firms, and loans. The estimation is problematic when some of these characteristics are only partially observed and the matching of banks and firms is endogenously determined because they prefer partners that have higher quality. We study the U.S. bank loan market with a two-sided matching model to control for the endogenous matching, and obtain Bayesian inference using a Gibbs sampling algorithm with data augmentation. We find evidence of positive assortative matching of sizes, explained by similar relationships between quality and size on both sides of the market. Banks' risk and firms' risk are important factors in their quality. Controlling for the endogenous matching has a strong impact on estimated coefficients in the loan spread equation.