The bank lending channel of monetary policy: identification and estimation using Portuguese micro bank data

of deposits and capital. The possibility of other forms of external financing alternative to
deposits and capital (money market funds, certificates of deposits, etc.) is taken into
account by introducing into the credit equation an interest rate representing the cost of
such funds. Our basic equation now reads as:

ln(C / P)^s_tt = α0_i + α₁ln(D / P)_it + α2 ln(K / P)_it + α3l_t + α4i_t + α5s_t + α₆π_t

where s_t stands for the cost of external financing alternative to deposits or capital. We
argue that if α₁ > 0 this is evidence of the existence of a lending channel, provide α₃ is
finite (not very large). We note that equation (7.1) is also in line with the loan-supply
function derived in Kashyap and Stein (1995). In the theoretical model suggested by these
authors the supply of loans depends on the loan security spread, on the volume of deposits
(assumed to be out of the bank control), on the cost of raising non-deposits external
finance, as well as on the uncertainty surrounding the future expected deposits.

The variable s_t is supposed to proxy the cost of external funds available to the banks
(funds alternative to deposits or capital). After 1995, s_t is also expected to measure the
costs of funds obtained abroad in other EU countries. Assuming that the uncovered
interest rate parity (UIP) holds we use the short-term interest rate on Portuguese interbank
money market (Lisbor) as a proxy for the total cost of external funds at the Portuguese
banks disposal during the sample period²³.

As size, liquidity and capitalisation ratios may be important sources of heterogeneity
in banks loan-supply functions, the estimated equations also include several interaction
effects that account for these heterogeneity sources.

Under the assumption of cointegration equation (7.1) - which explicitly allows for
levels specific effects captured by the coefficients α0_i - may be estimated using POLS,
PCOLS, PDOLS or PFMOLS. We have seen above that the POLS estimator is consistent,
but not superconsistent, if the regressors are correlated with the residuals, and that they
may exhibit substantial biases in finite samples. Simulation results also show that the
PCOLS estimator does not significantly improve over simple POLS (see, for instance,
Baltagi and Kao (2000))²⁴. In contrast PFMOLS is superconsistent even when the

²³ After 1995 s_t may be seen as being equal to the sum of the short-term interest rate abroad, s_t^* , plus
the exchange risk premium, φ_t, so that according to the UIP we have s_t = s** + φ_t . It can be seen from the
data that exchange risk premium, measured by φ_t = s_t - s_t* is decreasing over time, converging to zero by
97/98.

²⁴ This is likely to be case for Portuguese data given our small sample.

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