Bachmann, Rudiger, and Christian Bayer, 2013, “Wait-and-See business cycles?” Journal of Monetary Economics, Vol. 60, No. 6, pp. 704–719.
Bachmann, Ruediger, and Giuseppe Moscarini, 2011, “Business Cycles and Endogenous Uncertainty,” 2011 Meeting Papers 36, Society for Economic Dynamics.
Balke, Nathan S., Enrique Martinez-Garcia, and Zheng Zeng, 2017, “Understanding the Aggregate Effects of Credit Frictions and Uncertainty,” Globalization and Monetary Policy Institute Working Paper 317, Federal Reserve Bank of Dallas.
Basu, Susanto, and Brent Bundick, 2017, “Uncertainty Shocks in a Model of Effective Demand,” Econometrica, Vol. 85, No. 3, pp. 937–958.
Berger, David, Ian Dew-Becker, and Stefano Giglio, 2016, “Contractionary Volatility or Volatile Contractions?” Unpublished manuscript.
Bernanke, Ben S., 1983, “Irreversibility, Uncertainty, and Cyclical Investment,” The Quarterly Journal of Economics, Vol. 98, No. 1, pp. 85–106.
Bernanke, Ben S., Mark Gertler, and Simon Gilchrist, 1999, “The financial accelerator in a quantitative business cycle framework,” in J. B. Taylor and M. Woodford (eds.), Handbook of Macroeconomics, Vol. 1, chap. 21, pp. 1341–1393 (Elsevier).
Bloom, Nicholas, Max Floetotto, Nir Jaimovich, Itay Saporta-Eksten, and Stephen J. Terry, 2012, “Really Uncertain Business Cycles,” NBER Working Papers 18245, National Bureau of Economic Research, Inc.
Born, Benjamin, and Johannes Pfeifer, 2014, “Policy risk and the business cycle,” Journal of Monetary Economics, Vol. 68, No. C, pp. 68–85.
Caballero, Ricardo J, 1991, “On the Sign of the Investment-Uncertainty Relationship,” American Economic Review, Vol. 81, No. 1, pp. 279–88.
Caldara, Dario, Jesus Fernandez-Villaverde, Juan Rubio-Ramirez, and Wen Yao, 2012, “Computing DSGE Models with Recursive Preferences and Stochastic Volatility,” Review of Economic Dynamics, Vol. 15, No. 2, pp. 188–206.
Carlstrom, Charles T, and Timothy S Fuerst, 1997, “Agency Costs, Net Worth, and Business Fluctuations: A Computable General Equilibrium Analysis,” American Economic Review, Vol. 87, No. 5, pp. 893–910.
Carlstrom, Charles T., Timothy S. Fuerst, and Matthias Paustian, 2010, “Optimal Monetary Policy in a Model with Agency Costs,” Journal of Money, Credit and Banking, Vol. 42, No. s1, pp. 37–70.
Carroll, Christopher D., 2001, “A Theory of the Consumption Function, with and without Liquidity Constraints,” Journal of Economic Perspectives, Vol. 15, No. 3, pp. 23–45.
Carroll, Christopher D., 2009, “Precautionary saving and the marginal propensity to consume out of permanent income,” Journal of Monetary Economics, Vol. 56, No. 6, pp. 780–790.
Carroll, Christopher D., and Miles S. Kimball, 2007, “Precautionary Saving and Precautionary Wealth,” Palgrave Dictionary of Economics and Finance, 2nd Ed, , No. 530.
Carroll, Christopher D., and Andrew A. Samwick, 1995, “How Important is Precautionary Saving?” The Review of Economics and Statistics, Vol. 80(3), pp. 410–419.
Cesa-Bianchi, Ambrogio, and Emilio Fernandez-Corugedo, 2014, “Uncertainty in a model with credit frictions,” Bank of England working papers 496, Bank of England.
Cesa-Bianchi, Ambrogio, Alessandro Rebucci, and M. Hashem Pesaran, 2015, “Uncertainty and Economic Activity: A Global Perspective,” Unpublished manuscript.
Chetty, Raj, 2006, “A Bound on Risk Aversion Using Labor Supply Elasticities,” NBER Working Papers 12067, National Bureau of Economic Research, Inc.
Christiano, Lawrence, Roberto Motto, and Massimo Rostagno, 2010, “Financial factors in economic fluctuations,” Working Paper Series 1192, European Central Bank.
Christiano, Lawrence, Roberto Motto, and Massimo Rostagno, 2014, “Risk Shocks,” American Economic Review, Vol. 104(1), pp. 27–65.
Christiano, Lawrence J., Roberto Motto, and Massimo Rostagno, 2003, “The Great Depression and the Friedman-Schwartz hypothesis,” Proceedings, Vol. Federal Reserve Bank of Cleveland, pp. 1119–1215.
Cooper, Russell W., and John C. Haltiwanger, 2006, “On the Nature of Capital Adjustment Costs,” Review of Economic Studies, Vol. 73, No. 3, pp. 611–633.
D’Erasmo, Pablo, and Hernan J Moscoso-Boedo, 2011, “Intangibles and Endogenous Firm Volatility over the Business Cycle,” Virginia Economics Online Papers 400, University of Virginia, Department of Economics.
Dorofeenko, Victor, Gabriel S. Lee, and Kevin D. Salyer, 2008, “Time-Varying Uncertainty And The Credit Channel,” Bulletin of Economic Research, Vol. 60, No. 4, pp. 375–403.
Faia, Ester, and Tommaso Monacelli, 2007, “Optimal interest rate rules, asset prices, and credit frictions,” Journal of Economic Dynamics and Control, Vol. 31, No. 10, pp. 3228–3254.
Fernald, John, 2012, “A quarterly, utilization-adjusted series on total factor productivity,” Working Paper Series 2012-19, Federal Reserve Bank of San Francisco.
Fernald, John, and Kyle Matoba, 2009, “Growth accounting, potential output, and the current recession,” FRBSF Economic Letter, , No. Aug 17.
Fernandez-Villaverde, Jesus, Pablo Guerron-Quintana, Keith Kuester, and Juan Rubio-Ramirez, 2015, “Fiscal Volatility Shocks and Economic Activity,” American Economic Review, Vol. 105, No. 11, pp. 3352–84.
Fernandez-Villaverde, Jesus, Pablo Guerron-Quintana, and Juan F. Rubio-Ramirez, 2010, “Fortune or Virtue: Time-Variant Volatilities Versus Parameter Drifting in U.S. Data,” NBER Working Papers 15928, National Bureau of Economic Research, Inc.
Fernandez-Villaverde, Jesus, Pablo Guerron-Quintana, Juan F. Rubio-Ramirez, and Martin Uribe, 2011, “Risk Matters: The Real Effects of Volatility Shocks,” American Economic Review, Vol. 101, No. 6, pp. 2530–61.
Fernandez-Villaverde, Jesus, and Juan F. Rubio-Ramirez, 2010, “Macroeconomics and Volatility: Data, Models, and Estimation,” NBER Working Papers 16618, National Bureau of Economic Research, Inc.
Fisher, Jonas D M, 1999, “Credit Market Imperfections and the Heterogeneous Response of Firms to Monetary Shocks,” Journal of Money, Credit and Banking, Vol. 31, No. 2, pp. 187–211.
Gale, Douglas, and Martin Hellwig, 1985, “Incentive-Compatible Debt Contracts: The One-Period Problem,” Review of Economic Studies, Vol. 52, No. 4, pp. 647–63.
Gali, Jordi, and Mark Gertler, 1999, “Inflation dynamics: A structural econometric analysis,” Journal of Monetary Economics, Vol. 44, No. 2, pp. 195–222.
Gilchrist, Simon, Jae W. Sim, and Egon Zakrajsek, 2014, “Uncertainty, Financial Frictions, and Investment Dynamics,” NBER Working Papers 20038, National Bureau of Economic Research, Inc.
Gilchrist, Simon, and Egon Zakrajsek, 2012, “Credit Spreads and Business Cycle Fluctuations,” American Economic Review, Vol. 102, No. 4, pp. 1692–1720.
Greenwood, Jeremy, Zvi Hercowitz, and Gregory W Huffman, 1988, “Investment, Capacity Utilization, and the Real Business Cycle,” American Economic Review, Vol. 78, No. 3, pp. 402–17.
Ilut, Cosmin L., and Martin Schneider, 2014, “Ambiguous Business Cycles,” American Economic Review, Vol. 104, No. 8, pp. 2368–99.
Justiniano, Alejandro, and Giorgio E. Primiceri, 2008, “The Time-Varying Volatility of Macroeconomic Fluctuations,” American Economic Review, Vol. 98, No. 3, pp. 604–41.
Kimball, Miles S., John G. Fernald, and Susanto Basu, 2006, “Are Technology Improvements Contractionary?” American Economic Review, Vol. 96, No. 5, pp. 1418–1448.
King, Robert G., Charles I. Plosser, and Sergio T. Rebelo, 1988, “Production, growth and business cycles : I. The basic neoclassical model,” Journal of Monetary Economics, Vol. 21, No. 2–3, pp. 195–232.
Leduc, Sylvain, and Zheng Liu, 2015, “Uncertainty shocks are aggregate demand shocks,” Working Paper Series 2012–10, Federal Reserve Bank of San Francisco.
Leland, H, 1968, “Saving and Uncertainty: The Precautionary Demand for Saving,” The Quarterly Journal of Economics, Vol. 82, pp. 465–473.
Monacelli, Tommaso, and Roberto Perotti, 2008, “Fiscal Policy, Wealth Effects, and Markups,” NBER Working Papers 14584, National Bureau of Economic Research, Inc.
Townsend, Robert, 1979, “Optimal contracts and competitive markets with costly state verification,” Staff Report 45, Federal Reserve Bank of Minneapolis.
Appendix A. Appendix
We would like to thank Martin Andreasen, Susanto Basu, Nick Bloom, Jesus Fernandez-Villaverde, Paolo Gelain, Wouter den Haan, Richard Harrison, Frederic Malherbe, Roland Meeks, Matthias Paustian, Johannes Pfeifer, Jumana Saleheen, Martin Seneca, Konstantinos Theoridis, and seminar participants at various conferences. Any views expressed are solely those of the authors and so cannot be taken to represent those of the IMF or the Bank of England or to state any policies. This paper is a revised version of that one previously circulated with the title “Uncertainty in a Model with Credit Frictions”. Online appendix available at https://sites.google.com/site/ambropo/CF_Uncertainty_OnlineAppendix.pdf.
For example, Leland (1968), Kimball (1990) and Carroll and Kimball (2007) show the theoretical conditions needed for (future) uncertainty to affect consumption, later quantified empirically by Carroll and Samwick (1995) and others (see eg Carroll and Kimball). Hartman (1976), Abel (1983), Bernanke (1983), Caballero (1991), and Dixit and Pindyck (1994) show the theoretical conditions needed for uncertainty to affect investment. Recently Bloom (2009) has shown that uncertainty can have sizeable effects on firms’ demand for factor inputs.
An increasing body of research has studied the role of micro and macro uncertainty using dynamic stochastic general equilibrium (DSGE) models. For uncertainty about aggregate shocks see Justiniano and Primiceri (2008), Fernandez-Villaverde and Rubio-Ramirez (2010), Fernandez-Villaverde and others (2011), Basu and Bundick (2017), Born and Pfeifer (2014), Fernandez-Villaverde and others (2015), Gourio (2012), and Leduc and Liu (2015). For uncertainty about idiosyncratic shocks see Dorofeenko, Lee, and Salyer (2008), Gilchrist, Sim, and Zakrajsek (2014), Arellano, Bai, and Kehoe (2012), Christiano, Motto, and Rostagno (2014). Finally, Bloom (2009), Bloom and others (2012), Bachmann and Bayer (2013), and Balke, Martinez-Garcia, and Zeng (2017) consider both notions of uncertainty.
Other papers considered both micro and macro uncertainty shocks, but in different environments relative to ours (see Bloom (2009), Bloom and others (2012), and Bachmann and Bayer (2013)). While writing this paper we became aware of a paper by Balke, Martinez-Garcia, and Zeng (2017) who also consider both micro and macro uncertainty in a set up similar to ours. The key difference lies in the estimation of the time series properties of micro uncertainty, as we explain in more detail below.
For example Bachmann and Bayer (2013), Born and Pfeifer (2014), Gilchrist, Sim, and Zakrajsek (2014), and Chugh (2016) find little evidence of uncertainty shocks being a major driver of business cycle fluctuations. In contrast, Christiano, Motto, and Rostagno (2014) find that a large share of output fluctuations can be explained by (micro) uncertainty shocks. Bloom and others (2012) show that the conditional impact of (large) uncertainty shocks can be economically significant. Fernandez-Villaverde and others (2011) and Basu and Bundick (2017) also show that, when the monetary policy is constrained by the zero lower bound, the conditional impact of uncertainty shocks can be sizable.
In a similar spirit, Balke, Martinez-Garcia, and Zeng (2017) estimate their model with the simulated method of moments.
Our results are closer to the few “micro” estimates available in the literature. Chugh (2016) uses the disaggregated plant-level data constructed by Cooper and Haltiwanger (2006) from the Longitudinal Research database; Gilchrist, Sim, and Zakrajsek (2014) use disaggregated data from Compustat on firms’ net sales; Bachmann and Bayer (2013) use firm-level German data from USTAN.
This assumption is in contrast with ‘micro’ measures of productivity, which are typically quite persistent in the data. However, assuming persistence in the idiosyncratic productivity shock would require having to track the distribution of entrepreneurs through time and thereby complicate the solution of the optimal debt contract.
Note that other papers in the earlier literature have considered a similar definition of time-varying uncertainty (or “risk”) as the one used here. See, for example Christiano, Motto, and Rostagno (2003), Dorofeenko, Lee, and Salyer (2008), Christiano, Motto, and Rostagno (2010), and Christiano, Motto, and Rostagno (2014).
In the next Sections we will also consider alternative values for some key parameters to shed light on the propagation mechanisms.
These parameter values imply the following great ratios in steady state: consumption over total output is 76 percent; investment over total output is 18 percent, and entrepreneurial consumption over total output is 6 per-cent.
The data is available at the following website: https://people.stanford.edu/nbloom/. It includes data on over 50,000 establishments from 1972 to 2009. Bloom and others (2012) focus on a sub-set of 15,673 establishments with 25+ years of data.
Differently from Chugh (2016) we use a different data set that covers a longer and more recent sample period and a larger number of cross-sectional units. Chugh (2016) uses annual data of plant-level profitability constructed by Cooper and Haltiwanger (2006) from the Longitudinal Research Database (LRD). The data set covers approximately 7,000 large U.S. manufacturing plants over the period 1974–1988.
Note that, due to bankruptcy, some of the variations in idiosyncratic TFP would be censored in the data (and more so at times of high uncertainty). As a consequence, our estimates of the standard deviation of micro uncertainty shocks might be biased downward.
The data can be downloaded at: http://www.frbsf.org/economic-research/total-factor-productivity-tfp/. See Fernald (2012), Fernald and Matoba (2009), and Kimball, Fernald, and Basu (2006). The sample period was chosen to yield estimates that can be compared against the micro uncertainty estimates. The results are very similar if we use more recent data.
Results are little changed when considering a different size of the rolling window.
Micro uncertainty shocks (as the ones considered in this paper) and the Knightian uncertainty shocks (as in Ilut and Schneider, 2014) represent an exception and their impact can be studied with linear methods.
As Fernandez-Villaverde and others (2011) show, 3rd-order approximation to the policy functions is sufficient to capture the dynamics of the model, with little gain to using an approximation higher than the 3rd-order.
We refer the reader to the Online Appendix for details on how the IRFs are constructed.
In our baseline calibration —and differently from Basu and Bundick (2017)— the macro uncertainty shock does not generate an impact increase in “precautionary labor supply”, since consumption does not enter the labor supply schedule. We explore the role of different preferences below.
This is different from similar studies in the literature (e.g. Basu and Bundick, 2017; Fernandez-Villaverde and others, 2011), who find larger impacts of similar types of macro uncertainty shocks. The main difference lies in the type of uncertainty shock (i.e., whether to goverment spending or demand) and the size of the uncertainty shock. Our results are comparable with those reported Born and Pfeifer (2014).
A more detailed description of these mechanisms is explained in the Online Appendix with a simple comparative steady state exercise.
With non-GHH (separable) preferences and flexible prices the decline in consumption brought about the macro uncertainty shock and the precautionary saving motive, acts to shift labor supply (it expands for a given level of the real wage). This worsens the co-movement problem as hours (and hence output) expand in response to the uncertainty shock. See below for more on separable preferences.
In a previous version of this paper (Cesa-Bianchi and Fernandez-Corugedo, 2014), we show that the effect of uncertainty shocks on output almost doubles when using a version of the model calibrated so as to obtain an average probability of changing prices of 5 quarters, instead of 4 quarters as in the baseline.
Note here that, in the limiting case where the monitoring cost is zero (e.g., assuming that both the entrepreneur and the bank could costlessly observe idiosyncratic shocks), the impact of a micro uncertainty shock would also tend to zero.
The full set of impulse responses are not reported here for brevity, but they are available in the Online Appendix.
We use US data over the sample period 1972:Q1–2012:Q4. Real GDP, private final consumption expenditure, and gross fixed capital formation are from OECD Main Economic Indicators; Hours are from the BLS; the BAA to AAA spread is from Moody.
Results are similar when using the Excess Bond Premium of Gilchrist and Zakrajsek (2012) as an alternative proxy for the external finance premium.
To obtain the moments implied by the model, we simulate the model economy for 2000 periods. We then use the last 164 periods (i.e., the same number of observations that we have in the data, from 1972:Q1 to 2012:Q4) to compute the statistics of the simulated variables (in log-deviation from an HP trend with smoothing parameter 1600).
Given that our model does not feature anticipated shocks, we compare our results with the percentage of the variance of GDP accounted for by the unanticipated component of the risk shock in Christiano, Motto, and Rostagno (2014) (see their Table 5).
We also checked the role played by investment adjustment costs in driving our results by running a simulation where we set the adjustment cost parameter to zero. Apart from increasing the volatility of investment in the unconditional simulations, the unconditional business cycle statistics are very similar to the baseline.