Angeloni, Ignazio, Anil Kashyap, Benoît Mojon, and Daniele Terlizzese, 2002, “Monetary Transmission in the Euro Area: Where do We Stand?” ECB Working Paper No. 114 (Frankfurt: European Central Bank).
Altunbas, Yener, Leonardo Gambacorta, and David Marqués, 2007, “Securitisation and the Bank Lending Channel,” ECB Working Paper No. 838 (Frankfurt: European Central Bank).
Ang, Andrew, and Monika Piazzesi, 2003, “A No-Arbitrage Vector Autoregression of Term Structure Dynamics with Macroeconomic and Latent Variables,” Journal of Monetary Economics, Vol. 50, No. 4, pp. 745–87.
Bean, Charles, Jens Larsen, and Kalin Nikolov, 2002, “Financial Frictions and the Monetary Transmission Mechanism: Theory, Evidence and Policy Implications,” ECB Working Paper No. 113 (Frankfurt: European Central Bank).
Berger, Helge, Thomas Harjes, and Emil Stavrev, 2008, “The ECB’s Monetary Analysis Revisited,” IMF Working Paper No. 08/171 (Washington: International Monetary Fund).
Bernanke, Ben, Vincent Reinhart, and Brian Sack, 2004, “Monetary Policy Alternatives at the Zero Bound: An Empirical Assessment,” Board of Governors of the Federal Reserve, Finance and Economics Discussion Series 2004–48 (September).
Bulíř, Aleš, Martin Čihák, and Kateřina Šmídková, 2008, “Writing Clearly: ECB’s Monetary Policy Communication,” IMF Working Paper No. 08/252 (Washington: International Monetary Fund).
Čihák, Martin, and Thomas Harjes, 2008, “Liquidity Management in the Euro Area,” IMF Country Report No. 08/263 (Washington: International Monetary Fund).
Dai, Q., and K. Singleton, 2000, “Specification Analysis of Affine Term Structure Models,” The Journal of Finance, Vol. 55, pp. 1943–78.
Eggertsson, Gauti, and Michael Woodford, 2004, “Policy Options in a Liquidity Trap,” The American Economic Review, Vol. 94, No. 2, pp. 76–9.
European Central Bank, 2009, “Recent Developments in the Retail Bank Interest Rate Pass-Through in the Euro Area,” ECB Monthly Bulletin (August), pp. 93–105.
Friedman, Benjamin M., and Kenneth N. Kuttner, 1992, “Money, Income, Prices, and Interest Rates,” American Economic Review, Vol. 82, pp. 472–92.
Heinemann, Friedrich, and Katrin Ullrich, 2005, “Does it Pay to Watch Central Bankers’ Lips? The Information Content of ECB Wording,” ZEW - Centre for European Economic Research Discussion Paper No. 05-070.
Hördahl, Peter, Oreste Tristani, and David Vestin, 2006, “A Joint Econometric Model of Macroeconomic and Term Structure Dynamics,” Journal of Econometrics, Vol. 131, pp. 405–44.
International Monetary Fund, 2008, “Stress in Bank Funding Markets and Implications for Monetary Policy,” Global Financial Stability Report, October (Washington).
Krugman, Paul, 1998, “It’s Baaack: Japan’s Slump and the Return of the Liquidity Trap,” Brookings Papers on Economic Activity, Vol. 29, pp. 137–206.
Mishkin, Frederic, S., 1995, “Symposium on the Monetary Transmission Mechanism,” Journal of Economic Perspectives, Vol. 9, No. 4, pp. 3–10.
Rudebush, Glen, and Tao Wu, 2003, “A Macro-Finance Model of the Term Structure, Monetary Policy, and the Economy,” FRBSF Working Paper No. 17 (San Francisco: Federal Reserve Bank of San Francisco).
Rudebusch, Glenn, Eric Swanson, and Tao Wu, 2006, “The Bond Yield ‘Conundrum’ from a Macro-Finance Perspective,” FRBD Working Paper No. 2006–16 (Dallas: Federal Reserve Bank of Dallas).
Sims, Christopher A., and Tao Zha, 1998, “Bayesian Methods for Dynamic Multivariate Models,” International Economic Review, Vol. 39, No. 4, pp. 949–68.
Sørensen, Christoffer Kok, and Thomas Werner, 2007, “Bank Interest Rate Pass-Through in the Euro Area: A Cross Country Comparison,” ECB Working Paper No. 580 (Frankfurt: European Central Bank).
We thank Luc Everaert for guidance. We are also very grateful to Brian Sack and Eric Swanson for providing us with their Matlab codes for estimating the macro-finance model of term structure. We used a modified version of the codes that fine-tune the nonlinear optimization method of the Bernanke, Reinhart, and Sack (2004). Finally, we thank for useful comments to F. Hammermann, S. Sauer, C. Kamps, R. Adalid, P. Moutot, other ECB staff, and participants in seminars at the IMF and the ECB. Any remaining errors are ours.
See, for example, the speech by José Manuel González-Páramo on “Financial market failures and public policies: A central banker’s perspective on the global financial crisis”, January 2009.
As argued in Berger, Harjes, and Stavrev (2008), the ECB’s two pillar approach (which includes a monetary pillar which gives high prominence to monetary aggregates in determining the appropriate policy stance) may have made communication more challenging. Bulíř, Čihák, and Šmídková (2008) arrive at a similar conclusion.
For a description of the ECB’s enhanced credit support, see in particular the speech by Jean-Claude Trichet, President of the ECB, at the University of Munich on July 13, 2009, and “Governing Council decisions on nonstandard measures,” ECB Monthly Bulletin, June 2009, pp. 9–10.
Meanwhile, the (already large) number of counterparties participating in ECB’s refinancing operations increased from 1,700 before the crisis to 2,200, as refinancing through money markets became more difficult. The number of active counterparties before the crisis was about 450 (compared to 20 in the United States), and increased to 750 during the crisis; also, the number of counterparties that participated in the one-year longer-term refinancing operation was more than 1,100.
The size of the ECB’s balance sheet has multiplied by a factor of 1.5 between mid-2007 and early 2009. The corresponding ratios for the Bank of England’s and for the U.S. Federal Reserve have been 3.0 and 2.5.
The models with the levels of the variables are also estimated using Bayesian VAR (BVAR), following Sims and Zha (1998). The BVAR results are qualitatively similar to the OLS estimates, and available upon request.
For the empirical analysis, which is done with monthly data, πt = 1200(p_sat - p_sat-1), where p_sat is the logarithm of the seasonally adjusted harmonized index of consumer prices (HICP), and π12t = 100(pt - pt-12), where pt is a logarithm of the HICP index. Economic activity is approximated by a weighted average of industrial production (30 percent share) and retail sales indexes. As initial values for the output gap yt for the Bayesian estimation, the log-difference of the actual index from its Hodrick-Prescott filtered value is used.
Given that the model is estimated with monthly data, the leads and lags in equations (2) and (3) are chosen so as to obtain dynamic responses that are in line with the dynamic response from models estimated with quarterly data and also reflect plausible lengths of price setting contracts as well as the lags with which capacity utilization affects inflation in reality.
Instead of loan rates, quantity of loans could be used in the aggregate demand equation. These two approaches should be equivalent in principle. However, in times of severe banking sector stress, rationing of borrowers may occur, which is not fully reflected in loan interest rates. Testing whether using loans instead of loan rates in the aggregate demand equation changes the results, is left for future research.
These results are consistent with the finding by IMF (2008) that the 3-month Euribor rates have more stable and reliable relation with the policy rate than other lender rates, and by Sørensen and Werner (2007) that bank rates on corporate loans appear to adjust most efficiently, followed by mortgage loan rates. Also, our findings are consistent with the conclusion by the ECB (2009) “even during the current financial crisis, the bank interest rate pass-through has worked relatively well in terms of responding to developments in the EURIBOR and longer-term market rates, although less well in terms of responding to developments in the EONIA.”
Altunbas, Gambacorta, and Marqués (2007) suggest that the pass-through to market rates has become less efficient already before the crisis, due to increased securitization.
The pre-crisis sample is prior to August 2007 and the crisis sample is from September 2007 to February 2009.The choice of August 2007 as a split point of the sample is supported by Chow break-point test.
The market-based measures of inflation expectations need to be interpreted with caution, given the problems in the markets during the crisis. Nonetheless, the measures still contained useful information, as illustrated by their high correlation (a correlation coefficient of 75 percent) with the model-derived inflation expectations.
In the years before the crisis, the ECB developed a system of code words that effectively pre-announced the next adjustments in policy rates. However, these pre-announcements have occurred only several weeks before the policy decisions, and were arguably only a weak form of commitment (e.g., Heineman and Ullrich, 2005).
On April 30, 2009, the Fed’s Federal Open Market Committee stated that “The Committee will maintain the target range for the federal funds rate at 0 to ¼ percent and anticipates that economic conditions are likely to warrant exceptionally low levels of the federal funds rate for an extended period.”
Instead of loan rates, the quantity of loans could be used in the aggregate demand equation (A2). In principle, having loan rates or the quantity of loans in the aggregate demand equation should be equivalent. However, in times of severe stress in the banking system rationing of borrowers may occur, which is not fully reflected in loan interest rates. Testing whether using loans instead of loan rates in the aggregate demand equation changes the results, is left for future research.