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Estimation of the Loan Spread Equation with Endogenous Bank-Firm Matching

In: Structural Econometric Models

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  • Jiawei Chen

Abstract

This article estimates the loan spread equation taking into account the endogenous matching between banks and firms in the loan market. To overcome the endogeneity problem, I supplement the loan spread equation with a two-sided matching model and estimate them jointly. Bayesian inference is feasible using a Gibbs sampling algorithm that performs Markov chain Monte Carlo (MCMC) simulations. I find that medium-sized banks and firms tend to be the most attractive partners, and that liquidity is also a consideration in choosing partners. Furthermore, banks with higher monitoring ability charge higher spreads, and firms that are more leveraged or less liquid are charged higher spreads.

Suggested Citation

  • Jiawei Chen, 2013. "Estimation of the Loan Spread Equation with Endogenous Bank-Firm Matching," Advances in Econometrics, in: Structural Econometric Models, volume 31, pages 251-289, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-9053(2013)0000032009
    DOI: 10.1108/S0731-9053(2013)0000032009
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    Cited by:

    1. Florence Honoré & Martin Ganco, 2023. "Entrepreneurial teams' acquisition of talent: Evidence from technology manufacturing industries using a two‐sided approach," Strategic Management Journal, Wiley Blackwell, vol. 44(1), pages 141-170, January.

    More about this item

    Keywords

    Loan spread equation; two-sided matching; Bayesian inference; Gibbs sampling; G21; C78; C11;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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