The credit supply channel of monetary policy: evidence from a FAVAR model with sign restrictions

Abstract

We test whether the credit channel of the monetary policy was present in the United States’ economy from January 2001 to April 2016. To this end, we use a factor-augmented vector autoregression, and we impose sensible theoretical sign restrictions in our structural identification scheme. We use the expected substitution effect between bank commercial loans and commercial papers to identify the credit supply channel. We found that the credit channel appears to have operated in the US economy during the sample period. However, when we split the sample, we found that the credit channel did not operate after the subprime crisis (close to the Zero Lower Bound of the interest rate). This result is robust to changing the sign restriction horizons. It supports current views in the literature regarding the ineffectiveness of the credit channel as a means to foster real economic activity during crises episodes.

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Notes

  1. 1.

    See Boivin et al. (2010) for a recent survey of this literature.

  2. 2.

    Such standard conditions can be found for instance in Lütkepohl (2006).

  3. 3.

    We also attempted to identify the response of the bank loans to an interest rate shock, using a traditional Cholesky factorization, and also imposing a decreasing in the issuing of commercial papers 1 month after the shock, and setting free bank loans. In both cases, we were not able to identify plausible scenarios, in the sense that the estimated IRFs of traditional variables such as economic activity or prices, presented counterintuitive dynamics after the shocks. These results are available upon request.

  4. 4.

    We assume that the full set of variables in the FAVAR \(X_{t}\) is related to the factors as follows: \(X_{t} = \varLambda^{f} \varvec{F}_{\varvec{t}} + \varLambda^{y} \varvec{Y}_{\varvec{t}} + e_{t}\), where \(\varLambda^{f}\) is an N × K matrix of unobservable factor loadings that contains the loadings \(\lambda_{i}^{K}\) and \(\varLambda^{y}\) is an N × M observable matrix of factor loadings.

  5. 5.

    This is the minimal order for which the residuals are uncorrelated in time.

  6. 6.

    We also estimate our model using subsets of variables seeking to identify each factor directly, instead of recovering them from the whole data set. All the qualitative results reported in what follows remain and they are available upon request.

  7. 7.

    The shocks are symmetric, so it makes no difference in the argument to instead claim a reduction in the market interest rate.

  8. 8.

    We have rotated the sing of the IRF associated to the price factor to present the results in a more intuitive fashion. Note that factor is identified up to a column sign rotation and in our case, it presents a negative correlation with the price series.

  9. 9.

    Our results are robust to reducing the number of factors to two factors (this increasing the degrees of freedom), in the pre-ZLB and post-ZLB sample periods.

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Correspondence to Jorge M. Uribe.

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Appendix

Appendix

See Figs. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 and 14, Tables 1, 2, and 3.

Table 3 Series and transformations to achieve stationarity

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Holguín, J.S., Uribe, J.M. The credit supply channel of monetary policy: evidence from a FAVAR model with sign restrictions. Empir Econ 59, 2443–2472 (2020). https://doi.org/10.1007/s00181-019-01759-5

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Keywords

  • Credit channel
  • FAVAR
  • Sign restrictions
  • Monetary policy

JEL Classification

  • E51
  • E52
  • C32