Credit demand, credit supply, and economic activity

Nathan S. Balke; Zheng Zeng

doi:10.1515/bejm-2012-0116

Article

Credit demand, credit supply, and economic activity

Nathan S. Balke and Zheng Zeng

Published/Copyright: October 5, 2013

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information Explore this Subject

From the journal The B.E. Journal of Macroeconomics Volume 13 Issue 1

Abstract

In this paper, we attempt to identify the separate contributions of credit demand, supply of financial intermediation, and supply of funds to fluctuations in indicators of credit conditions and to fluctuations in economic activity. We estimate a common factor model in which the six factors correspond to supply of funds, financial intermediation, credit demand, aggregate uncertainty, real economic activity, and inflation. We use a simple model of financial intermediation to motivate restrictions on the factor loadings designed to identify supply of funds, uncertainty, credit demand, and financial intermediation factors. We find that the supply of funds and financial intermediation factors explain most of the variation in interest rates spreads, while the financial intermediation and credit demand factors typically contribute to most of the fluctuations in credit quantity variables. For credit indicators, the 2008–2009 financial crisis appears to be largely due to a decline in the financial intermediation. However, this decline in financial intermediation seems to have originated from output and uncertainty shocks, rather than shocks to financial intermediation itself.

Keywords: aggregate uncertainty; credit demand; credit supply; financial conditions indices; financial intermediation; JEL Classification: E44; E51; C32

Corresponding author: Zheng Zeng, Department of Economics, Bowling Green State University, Bowling Green, OH 43403, USA, Tel.: +(419) 372-8397, e-mail: zzeng@bgsu.edu

¹
Including two lags of the idiosyncratic factors and two lags on the common factors provides a relatively parsimonious yet flexible model to capture the dynamics between the observables and the unobserved factors. We experimented with other lag lengths and the results were fairly similar.
²
We estimated the state equation with VARs of three and six lags respectively, and the results of these two specifications are similar. In this paper we only report the results of the model with three VAR lags as this VAR yielded slightly more precise estimates of the factors.
³
We obtain very similar results when we impose the same sign restrictions on government securities’ factor loadings as on the commercial paper and Libor rates.
⁴
The commercial paper/T-Bill spread has often been used as an indicator of credit conditions [see for example, Friedman and Kuttner (1992)]. Similarly, the financial conditions indices of Hakkio and Keeton (2009) and Hatzius et al. (2010) include yields on government securities among their indicators.
⁵
The results are insensitive to the choice of alternative short-rates with which to calculate the spreads.
⁶
Total net borrowings of household sectors and nonfinancial business are obtained from the Federal Reserve Statistical Release, Z.1, Flow of Funds Accounts, Table F.1 “Total Net Borrowing and Lending in Credit Markets” and are the sum of lines 3–6.
⁷
Lown and Morgan (2004) and Asea and Blomberg (1996) found that the lending standard survey is informative in explaining variation in business loans and real economic activity.
⁸
The series is calculated following Gilchrist, Sim and Zakrajsek (2009a) and Bloom, Floetotto and Jaimovich (2009).
⁹
We do not include Federal Reserve Balance sheet variables in our set of indicators precisely because these variables move so dramatically over this period.
¹⁰
To save space we present only a selected number of indicators. The historical decompositions for all of the other indicators are available upon request.
¹¹
We only report the C&I loans, net credit tightening standard survey, loan availability survey, commercial bank total loans, and total credit liabilities. The decompositions of other series are consistent with the results of the selected indicators, and are available upon request.
¹²
Perhaps, this is not too surprising given the correlations between innovations in the factors were relatively small, with the exception of credit demand and supply of financial intermediation. Changing the order of credit demand and financial intermediation affects the impulse response analysis somewhat but had little effect on the historical decompositions.
¹³
The responses of the rest of the indicators are all consistent with the ones of the representative indicators. They are not reported here but are available upon request.
¹⁴
Note that we do not impose any sign restriction on the factor loadings of short-term interest rates or interest rate spreads with respect to the uncertainty factor.
¹⁵
On the advice of an anonymous referee, we checked to see whether the results were sensitive to the inclusion of the 2008–2009 financial crisis in the sample. We estimated the model up the 2006 and then used the Kalman filter to decompose the observed time series into contribution of the underlying factors and structural shocks for the entire sample. Most of the results using the short sample to estimate the model are similar to those of the model estimated to the full sample. Where the results differ are in decomposition of short-term interest rates. In the short sample model, the credit demand factor plays a larger role in the decomposition of short-term interest rates than in the full sample model.
¹⁶
Balke (2000) examines a threshold VAR where the threshold variable depends on credit conditions. In this case, credit conditions can have an effect independent of credit shocks by triggering a change in regime.
¹⁷
In our model, the elements of matrices A, H, F, Q and R.

Many thanks to Nick Bloom and William Dunkelberg for supplying their data. We also thank Tim Fuerst, Kundan Kishore, and Peter VanderHart as well as seminar participants at the June 2010 Western Economic Association Meetings and the February 2011 Eastern Economic Association Meetings for helpful comments and suggestions. The views expressed in this paper are solely those of the authors and not those of the Federal Reserve Bank of Dallas or the Federal Reserve System.

Appendix A

A. Bayesian estimation

Kim and Nelson (1998) and Johannes and Polson (2003) provide detailed introductions to Bayesian Markov Chain Monte Carlo methods. The objective of the estimation is to find the posterior distribution of both the unknown parameters, Θ,¹⁷ and the unobservable state vector, S, given the observable indicators, Y. By sequentially sampling S⁽ⁱ⁾ from posterior distribution P(S|Θ, Y) and Θ⁽ⁱ⁾ from P(Θ|S, Y), the resulting sample distribution of (S⁽ⁱ⁾, Θ⁽ⁱ⁾) converges to P(S, Θ|Y).

Define and The steps taken in the Gibbs Sampler are as follows:

Taking the parameters of the state space model as given, draw a realization of the state vector conditional on the model’s parameters and the observed data. Because the state space model presented by equation (1) and (2) is linear and Gaussian, the distribution of S_T given and that of S_t given S_t+1 and for t=T–1, T–2, …, 1 are also Gaussian:
S_T∼N(S_T∣T, P_T∣T),
where
We can take Kalman filter approach to obtain S_T∣T and P_T∣T by filtering forward:
S_t∣t=S_t∣t–1+P_t∣t–1H′(HP_t∣t–1H′+R)^–1η_t∣t–1,
P_t∣t=P_t∣t–1–P_t∣t–1H′(HP_t∣t–1H′+R)^–1HP_t∣t–1;
where η_t∣t–1=(Y_t–AY_t–1–HS_t∣t–1) in our model is the new information that Y_t can bring to forecasting S_t, and is weighted by the variance of the conditional forecast error (HP_t∣t–1H′+R).
We draw from the conditional distribution, and obtain and by sampling backwards:
where is the new information updated by the state equation and equals to (S_t+1–FS_t∣t) in our model, and is weighted by the variance (FP_t∣tF′+Q).
Taking state vector and variance-covariance matrix Q as given, draw parameters of the state equation, i.e., the elements in F matrix. We can rewrite the state equation in matrix form as below:
y=ρX+ν, ν∼N(0, Q);
where X=(S₀S₁ … S_T–1)′ and y=(S₁S₂ … S_T)′.
We employ a multivariate normal prior distribution for ρ given by
where are known and I[s(ρ)] is an indicator function used to denote that roots of F(L) lie outside the unit circle. The posterior distribution for ρ is given by
where
We draw ρ from the posterior and discard the draws if the roots lie outside the stationary region.
Taking the state vector and F matrix as given, draw elements in the variance-covariance matrix Q. We employ an Inverse-Wishart prior distribution W^–1(Ψ_o, κ_o), where Ψ_o and κ_o are known. The posterior distribution is given by W^–1(Ψ₁, κ₁) where
Taking the state vector and the R matrix as given, draw parameters in the measurement equation, i.e., the elements in A and H matrices. The posterior distributions have similar forms as those in step 2 with the linear regressions given by the measurement equations. Draws of parameters which resulted in roots lying outside the stationary region or violated sign restrictions were discarded and another new draw was made.
In order to help setting the scale of the factors, we set tight priors (around one) on the coefficients of the Fed Funds rate, Commercial and Industrial loan growth, and S&P 500 composite volatility to set the scales of our credit, and uncertainty factors, respectively. Also, we normalize the factor loadings of real GDP and GDP deflator to fix the scale of our output and inflation factors.
Taking the state vector and the parameter vector A(L) and H(L) as given, draw variances in R matrix from the Inverse-Wishart posterior distribution similar as those in step 3 with linear regressions given by the measurement equations.
Repeat steps 1–5. This is done for a burn-in buffer of 100,000 draws after which every tenth draw is used to form the posterior distribution consisting of total 10,000 draws.

References

Adrian, Tobias, and Hyun-Song Shin. 2010. “Financial Intermediaries and Monetary Economics.” Federal Reserve Bank of New York Staff Report 398.10.2139/ssrn.1491603Search in Google Scholar

Adrian, Tobias, Arturo Estrella, and Hyun Song Shin. 2010.“Monetary Cycles, Financial Cycles, and the Business Cycle.” Federal Reserve Bank of New York Staff Report 421.Search in Google Scholar

Alexopoulos, Michelle, and Jon Cohen. 2009. “Uncertain Times, Uncertain Measures.” Working Paper, University of Toronto.Search in Google Scholar

Asea, Patric, and Brock Blomberg. 1996. “Lending Cycles.” Journal of Econometrics 83 (1–2): 89–128.10.1016/S0304-4076(97)00066-3Search in Google Scholar

Balke, Nathan S. 2000. “Credit and Economic Activity: Credit Regimes and Nonlinear Propagation of Shocks.” Review of Economics and Statistics 82 (2): 344–349.10.1162/rest.2000.82.2.344Search in Google Scholar

Bernanke, Ben, and Mark Gertler. 1989. “Agency Costs, Net Worth, and Business Fluctuations.” The American Economic Review 79 (1): 14–31.Search in Google Scholar

Bernanke, Ben, and Cara Lown. 1991. “The Credit Crunch.” Brookings Papers on Economic Activity 2: 205–247.10.2307/2534592Search in Google Scholar

Bernanke, Ben, Mark Gertler, and Simon Gilchrist. 1996. “The Financial Accelerator and the Flight to Quality.” The Review of Economics and Statistics 78 (1): 1–15.10.2307/2109844Search in Google Scholar

Bernanke, Ben, Mark Gertler, and Simon Gilchrist. 1999. “The Financial Accelerator in a Quantitative Business Cycle Framework.” Handbook of Macroeconomics, 1341–1393. 1st ed., Vol. 1, Chapter 21.Search in Google Scholar

Bloom, Nicholas. 2009. “The Impact of Uncertainty Shocks.” Econometrica 77 (3): 623–685.10.3982/ECTA6248Search in Google Scholar

Bloom, Nicholas, Max Floetotto, and Nir Jaimovich. 2009. “Really Uncertain Business Cycles.” Working Paper.Search in Google Scholar

Butzen, Paul, Catherine Fuss, and Philip Vermeulen. 2002. “The Impact of Uncertainty on Investment Plans.” Working Paper Research 24, National Bank of Belgium.10.2139/ssrn.1692687Search in Google Scholar

Carlstrom, Charles, Timothy Fuerst, and Matthias Paustian. 2010. “Optimal Monetary Policy in a Model with Agency Cost.” Journal of Money, Credit and Banking 42: 37–70.10.1111/j.1538-4616.2010.00329.xSearch in Google Scholar

Chari, V. V., Lawrence Christiano, and Patrick Kehoe. 2008. “Facts and Myths about the Financial Crisis of 2008.” Working Paper 666, Federal Reserve Bank of Minneapolis.10.21034/wp.666Search in Google Scholar

Christiano, Lawrence, Roberto Motto, and Massimo Rostagno. 2010. “Financial Factors in Economic Fluctuations.” Working Paper Series 1192, European Central Bank.10.2139/ssrn.1600166Search in Google Scholar

Clair, Robert, and Paula Tucker. 1993. “Six Causes of the Credit Crunch.” Economic Review, Federal Reserve Bank of Dallas.Search in Google Scholar

Cúrdia, Vasco, and Michael Woodford. 2009. “Credit Spreads and Monetary Policy.” Staff Reports No. 385, Federal Reserve Bank of New York.10.3386/w15289Search in Google Scholar

Cúrdia, Vasco, and Michael Woodford. 2010. “The Central-Bank Balance Sheet As an Instrument of Monetary Policy.” Staff Reports 463, Federal Reserve Bank of New York.10.3386/w16208Search in Google Scholar

Dunkelberg, William, and Jonathan Scott. 2009. “The Response of Small Business Owners to Changes in Monetary Policy.” Business Economics 44: 23–37.10.1057/be.2008.6Search in Google Scholar

Friedman, Benjamin, and Kenneth Kuttner. 1992. “Money, Income, Prices, and Interest Rates.” American Economic Review 82 (3): 472–492.Search in Google Scholar

Gertler, Mark, and Nobuhiro Kiyotaki. 2010. “Financial Intermediation and Credit Policy in Business Cycle Analysis.” Handbook of Monetary Economics, 547–599. Chapter 11.Search in Google Scholar

Gilchrist, Simon, and Egon Zakrajsek. 2010. “Credit Risk and the Macroeconomy.”Search in Google Scholar

Gilchrist, Simon, Jae W. Sim, and Egon Zakrajsek. 2009a. “Uncertainty, Credit Spreads, and Investment Dynamics.” Working Paper.Search in Google Scholar

Gilchrist, Simon, Vladimir Yankov, and Egon Zakrajsek. 2009b. “Credit Market Shocks and Economic Fluctuations: Evidence from Corporate Bond and Stock Markets.” NBER Working Papers 14863.10.3386/w14863Search in Google Scholar

Hakkio, Craig, and William Keeton. 2009. “Financial Stress: What Is It, How Can It Be Measured, and Why Does It Matter?.” Economic Review, Federal Reserve Bank of Kansas City, issue QII: 5–50.Search in Google Scholar

Hall, Robert. 2010. “Why Does the Economy Fall to Pieces after a Financial Crisis?” Journal of Economic Perspectives 24 (4): 3–20.10.1257/jep.24.4.3Search in Google Scholar

Hatzius, Jan, Peter Hooper, Frederic Mishkin, Kermit Schoenholtz, and Mark Watson. 2010. “Financial Conditions Indexes: A Fresh Look after the Financial Crisis.” 2010 U.S. Monetary Policy Forum Report, February 22.Search in Google Scholar

Johannes, Michael, and Nicholas Polson. 2003. “MCMC Methods for Continuous-Time Financial Econometrics.” SSRN Working Paper 480461.10.2139/ssrn.480461Search in Google Scholar

Kim, Chang-Jin, and Charles R. Nelson. 1998. State-Space Models with Regime-Switching: Classical and Gibbs-Sampling Approaches with Applications. 1st ed, Vol. 1. Cambridge, MA, London, England: MIT Press Books, number 0262112388, March.Search in Google Scholar

Kiyotaki, Nobuhiro, and John Moore. 1997. “Credit Cycle.” Journal of Political Economy 105 (2): 211–248.10.1086/262072Search in Google Scholar

Lown, Cara, and Donal Morgan. 2004. “The Credit Cycle and the Business Cycle: New Findings Using the Loan Officer Opinion Survey.” SIFR Research Report Series 27, Institute for Financial Research.Search in Google Scholar

Stock, James, and Mark Watson. 1989. “New Indexes of Coincident and Leading Economic Indicators.” NBER Chapters, In NBER Macroeconomics Annual 1989, Vol. 4, 351–409. Cambridge, MA, London, England: National Bureau of Economic Research, Inc., The MIT Press.10.1086/654119Search in Google Scholar

Stock, James, and Mark Watson. 2002. “Macroeconomic Forecasting Using Diffusion Indexes.” Journal of Business and Economic Statistics 20 (2): 147–162.10.1198/073500102317351921Search in Google Scholar

Stock, James, and Mark Watson. 2003. “Forecasting Output and Inflation: The Role of Asset Prices.” Journal of Economic Literature 41 (3): 788–829.10.1257/jel.41.3.788Search in Google Scholar

Stock, James, and Mark Watson. 2005. “Implications of Dynamic Factor Models for VAR Analysis.” NBER Working Paper No. 11467.10.3386/w11467Search in Google Scholar

Woodford, Michael. 2010. “Financial Intermediation and Macroeconomic Analysis.” Journal of Economic Perspectives 24 (4): 21–44.10.1257/jep.24.4.21Search in Google Scholar

Published Online: 2013-10-05

Published in Print: 2013-01-01

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.1515/bejm-2012-0116

Keywords for this article

aggregate uncertainty; credit demand; credit supply; financial conditions indices; financial intermediation; JEL Classification: E44; E51; C32