Ajello, A., 2010. Financial intermediation, investment dynamics and business cycle fluctuations (MPRA Paper No. 32447). University Library of Munich, Germany.
Bernanke, B.S., Gertler, M., Gilchrist, S., 1999. The financial accelerator in a quantitative business cycle framework (Handbook of Macroeconomics). Elsevier.
Carlstrom, C.T., Fuerst, T.S., 1997. Agency Costs, Net Worth, and Business Fluctuations: A Computable General Equilibrium Analysis. American Economic Review 87, 893910.
Christiano, L., Motto, R., Rostagno, M., 2013. Risk Shocks (NBER Working Paper No. 18682). National Bureau of Economic Research, Inc.
Dib, A., Christensen, I., 2005. Monetary Policy in an Estimated DSGE Model with a Financial Accelerator (Computing in Economics and Finance 2005 No. 314). Society for Computational Economics.
Foerster, A.T., 2011. Financial crises, unconventional monetary policy exit strategies, and agents expectations (Research Working Paper No. RWP 11-04). Federal Reserve Bank of Kansas City.
Gertler, M., Gilchrist, S., 1993. The Role of Credit Market Imperfections in the Monetary Transmission Mechanism: Arguments and Evidence. Scandinavian Journal of Economics 95, 4364.
Gertler, M., Kiyotaki, N., 2010. Financial Intermediation and Credit Policy in Business Cycle Analysis (Handbook of Monetary Economics). Elsevier.
Giannone , D., Reichlin, L., Sala, L., 2005. Monetary Policy in Real Time (NBER Chapters). National Bureau of Economic Research, Inc.
Gilchrist, S., Yankov, V., Zakrajsek, E., 2009. Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets. Journal of Monetary Economics 56, 471493.
Greenwood, J., Hercowitz, Z., Krusell, P., 1997. Long-Run Implications of Investment-Specific Technological Change. American Economic Review 87, 34262.
Greenwood, J., Hercowitz, Z., Huffman, G. 1988. Investment, Capacity Utilization, and the Real Business Cycle, American Economic Review, American Economic Association, American Economic Association, vol. 78(3), pages 402–17, June.
Hirakata, N., Sudo, N., Ueda, K., 2011. Do banking shocks matter for the U.S. economy? Journal of Economic Dynamics and Control 35, 20422063.
Iacoviello, M., 2005. House Prices, Borrowing Constraints, and Monetary Policy in the Business Cycle. American Economic Review 95, 739764.
Justiniano, A., Primiceri, G.E., Tambalotti, A., 2010. Investment shocks and business cycles. Journal of Monetary Economics 57, 132145.
Kiyotaki, N., Moore, J., 2012. Liquidity, Business Cycles, and Monetary Policy (NBER Working Paper No. 17934). National Bureau of Economic Research, Inc.
Kydland, F. E, Prescott, E. C., 1982. Time to Build and Aggregate Fluctuations, Econometrica, Econometric Society, Econometric Society, vol. 50 (6), pages 1345–70, November.
Liu, Z., Waggoner, D.F., Zha, T., 2011. Sources of macroeconomic fluctuations: A regimeswitching DSGE approach. Quantitative Economics 2, 251301.
Mandelman, F.S., 2010. Business cycles and monetary regimes in emerging economies: A role for a monopolistic banking sector. Journal of International Economics 81, 122138.
Primiceri, G., 2005. Why Inflation Rose and Fell: Policymakers Beliefs and US Postwar Stabilization Policy (NBER Working Paper No. 11147). National Bureau of Economic Research, Inc.
Roxburgh, C., Lund, S., Wimmer, T., Amar, E., Atkins, C., Kwek, J., Dobbs, R., Manyika, J. 2011. Debt and deleveraging: The global credit bubble and its economic consequences. McKinsey Report
Sannikov, Y., Brunnermeier, M.K., 2010. A Macroeconomic Model with a Financial Sector (2010 Meeting Paper No. 1114). Society for Economic Dynamics.
Smets, F., Wouters, R., 2007c. Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach. American Economic Review 97, 586606.
A. Appendix - Data
output = LN( GDPC96 / LNSindex ) * 100
investment = LN( ( FPI / GDPDEF ) / LNSindex ) * 100
inflation = LN( GDPDEF / GDPDEF(-l) ) * 100
interest rate = Federal Funds Rate / 4
GDPC96 : Real Gross Domestic Product - Billions of Chained 1996 Dollars, Seasonally Adjusted Annual Rate. Source: US Department of Commerce, Bureau of Economic Analysis
GDPDEF : Gross Domestic Product - Implicit Price Deflator - 1996=100, Seasonally Adjusted. Source: US Department of Commerce, Bureau of Economic Analysis
FPI : Fixed Private Investment - Billions of Dollars, Seasonally Adjusted Annual Rate. Source: US Department of Commerce, Bureau of Economic Analysis
Federal Funds Rate : Averages of Daily Figures - Percent. Source: Board of Governors of the Federal Reserve System.
LFU800000000 : Population level - 16 Years and Older - Not Seasonally Adjusted. Source: US Bureau of Labor Statistics
LNS10000000 : Labor Force Status : Civilian noninstitutional population - Age : 16 years and over- Seasonally Adjusted - Number in thousands. Source: US Bureau of Labor Statistics. (Before 1976 : LFU800000000 : Population level - 16 Years and Older)
Author is grateful of Lucrezia Reichlin for her valuable guidance and thank Domenico Giannone, Andrew Scott, Martin Ellison, Leonardo Melosi, Albert Marcet, Ivanna Vladkova Hollar, Juan Paolo Nicolini, Kenneth R. Beauchemin, Peter Karadi, and Tomasz Wieladek for helpful comments. I acknowledge financial support from London Business School. The views expressed in this paper are mine and do not represent those of the IMF. All errors are my own.
As in Kiyotaki and Moore (1997), therefore the idea is to have asset price variability which contributes to volatility in entrepreneurial net worth.
I use a csminwel optimization algorithm that is known to have good properties for exploring the likelihood surface, instead of fmincon (Matlab built in optimization procedure, which is Newton based). By adopting a Bayesian approach and computing Markov chain Monte Carlo (MCMC) optimization steps, I sampled the posterior distribution for a very large set of points. Sampling posterior topology is an optimization procedure in and of itself. After sampling a large set of points, I didn’t find a point whose log-posterior value was superior to the one found during the first optimization round (csminwel).
By turning off the financial frictions in my model and adding wage rigidities, I would get the SW (2007).
my model’s MDD is -709.80 and the SW model’s MDD is -778.21.
The results are qualitatively robust over different estimation sample sizes: 1962Q1-1985Q4, 1986Q1-2004Q4, and 1962Q1-2010Q4.
Households spend part of their income on installing capital goods for future production. Following the seminal paper by Kydland and Prescott (1982), it is assumed that installing capital goods takes time; the so-called time-to-build period.
For example, suppose Blackberry mobile phone technology becomes obsolete as touch screen smart phone technology is introduced (market capitalization of BB dropped from 80 billion USD to 4 billion USD in October 2013). There could be three channels through which this shock is transmitted. First, capital stock for producing Blackberry devices, de facto, depreciates much more rapidly than originally expected. Second, output of intermediate producers, say those who produce Blackberry parts or software, suffers. Third, the financial balance sheets of banks, which lend to such firms such as Blackberry, suffer as stock valuations fall. One possible microeconomic interpretation is that a large number of goods are produced using good-specific capital. In each period, as a fraction of goods becomes obsolete, the capital used for producing those goods becomes worthless. In aggregate, a capital quality shock reflects the economic obsolescence of capital, which in turn leads to deterioration of the balance sheets of financial intermediaries.
McKinsey “Episodes of Deleveraging.”