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We benefited from comments and suggestions of Fabio Bagliano, Eduardo Borensztein, Luis Cubeddu, Ilan Goldfajn, Vincent Hogan, Sunil Sharma, and seminar participants at the IMF. We are particularly indebted to Robert Marquez, Michael Mussa, and Miguel Savastano. Address for correspondence: Giovanni Dell’Ariccia (or Pietro Garibaldi), International Monetary Fund, 700 19th Street, N.W., Washington D.C.
In the rest of the paper we use the term financial intermediaries and banks interchangeably.
In reality, a significant component of aggregate bank lending is represented by line of credits, which banks can close without time delays. However, recalling the portion of funds actually withdrawn by clients requires a time consuming procedures.
Within the related labor market literature, Garibaldi (1998) proposes a model in which firing is stochastic and time consuming.
These results are partly related to the literature that analyzes empirically the relationship between bank lending rates and money market rates. See Hannan and Berger (1991), Neumark and Sharpe (1992) and Scholnick (1996).
This assumption implies that the amount of capital invested in existing loans does not affect the number of applications screened. Relaxing this assumption would make the analytic of the model much more cumbersome, but it would not alter its conclusions.
W can also say that the banking system issues new loans with an average waiting time
This assumption rule out banks’ effort as a determinant of the probability of recovery.
We will show that in equilibrium some project are never financed, and have zero value.
The appendix shows that the alternative banks’ policy of entering the recovery state when the loan is still viable is never optimal when time is continuous, and the idiosyncratic shock λi is independent of the probability of recovering the capital.
The proof in the Appendix is in the spirit of Sharma (1987), who shows the existence and uniqueness of a fixed point in traditional dynamic matching models.
We do not report the expressions for the match’s surplus in tight and easy states, but the detailed expressions are available from the authors upon request.
and similarly for the other value functions.
For the U.S. we use the Federal Fund Rate.
To obtain meaningful results we have to take into account all those factors that affect both the money market interest rate and bank lending, and that, if excluded, would bias our estimates. An alternative procedure could have been to include the variables that influence the money-market rate directly in the final regression.
For Mexico both criteria gave the same results, so that we have only one set of shocks.
Our theoretical framework assumes that the banking system is endowed with a fixed amount of capital.
we test the following null hypothesis of instability: ζ0 = γ0 = ν1 = μ1 = 0. The F-statistic for this test is non-standard, and its critical values are reported in Pesaran et al. (1996). In this case the critical bound values at the 99 percent level are 5.315 and 6.414. The computed F—statistic for Mexico and for the US are, respectively, 7.7 and 6.5 when the shocks are obtained with the Akaike criterion, and 6.6 when the US shocks are obtained through the Schwartz criterion. Thus, we comfortably reject the null of instability at the 99 percent level.
This idea relies on the particular role of deposits as a source of funds for the banks. See Kashyap and Stein (1993).
Garibaldi (1997) analyses the asymmetric effects of monetary policy on job creation and destruction in a dynamic matching model.
Our theory is consistent with asymmetric adjustment of lending volumes at the individual and aggregate level, while it predicts a symmetric adjustment of lending rates at the individual loan level. Microdata on banks’ lending would allow us to test this empirical implication.