Hostname: page-component-5c6d5d7d68-ckgrl Total loading time: 0 Render date: 2024-08-30T00:42:28.205Z Has data issue: false hasContentIssue false
  • English
  • Français

IMPERFECT TRANSMISSION OF TECHNOLOGY SHOCKS AND THE BUSINESS CYCLE CONSEQUENCES

Published online by Cambridge University Press:  14 December 2012

Hamilton B. Fout  and
Neville R. Francis
Hamilton B. Fout*
Affiliation:
Federal National Mortgage Association and Kansas State University
Neville R. Francis
Affiliation:
University of North Carolina
*
Address correspondence to: Hamilton B. Fout, Federal National Mortgage Association (Fannie Mae), 3900 Wisconsin Avenue, Washington, DC 20016, USA; e-mail: hamfout@gmail.com.
Get access Rights & Permissions [Opens in a new window]

Abstract

We investigate the business cycle effects of imperfect transmission of technology shocks within a basic real business cycle (RBC) model along two dimensions. First, we assume that agents cannot distinguish a temporary increase in productivity growth from a sustained increase in the underlying growth rate of productivity and instead must conduct signal extraction exercises and update beliefs about the source of aggregated shocks. Second, we propose a technology adjustment cost resulting in the slow diffusion of technological innovations into the production process. Both of these impediments to the transmission of technology result in a large initial wealth effect, increasing investment and hours less, relative to the usual RBC model without these frictions. Furthermore, each of these features is capable of producing a decline in hours on impact of the technology shock matching the negative response in hours found in the data by such works as Gali [American Economic Review 89(1), 249–271 (1999)].

Keywords

Technology ShocksLearningKalman FilterImpulse Responses
Type
Articles
Information
Macroeconomic Dynamics , Volume 18 , Issue 2 , March 2014 , pp. 418 - 437
Copyright
Copyright © Cambridge University Press 2012 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Altig, David, Christiano, Lawrence J., Eichenbaum, Martin, and Linde, Jesper (2011) Firm-specific capital, nominal rigidities and the business cycle. Review of Economic Dynamics 14 (2), 225–247.Google Scholar
Basu, Susanto, Fernald, John G., and Kimball, Miles S. (2006) Are technology improvements contractionary? American Economic Review 96 (5), 1418–1448.Google Scholar
Bils, Mark and Klenow, Peter J. (2004) Some evidence on the importance of sticky prices. Journal of Political Economy 112 (5), 947–985.Google Scholar
Bullard, James and Duffy, John (2004) Learning and Structural Change in Macroeconomic Data. Working paper, Federal Reserve Bank of St. Louis, 1–45.Google Scholar
Christiano, Lawrence J., Martin Eichenbaum, and Robert Vigfusson (2003) What Happens after a Technology Shock? NBER working paper 9819, 1–54.Google Scholar
Dynan, Karen (2000) Habit formation in consumer preferences: Evidence from panel data. American Economic Review 90 (3), 391–406.CrossRefGoogle Scholar
Edge, Rochelle M., Laubach, Thomas, and Williams, John C. (2007) Learning and shifts in long-run productivity growth. Journal of Monetary Economics 54 (8), 2421–2438.Google Scholar
Francis, Neville R., Owyang, Michael T., and Roush, Jennifer E. (2008) A Flexible Infinite-Horizon Alternative to Long-Run Restrictions with an Application to Technology Shocks. Working paper, Federal Reserve Bank of St. Louis, 1–39.Google Scholar
Francis, Neville R. and Ramey, Valerie A. (2005) Is the technology-driven real business cycle hypothesis dead? Journal of Monetary Economics 52 (8), 1379–1399.Google Scholar
Gali, Jordi (1999) Technology, employment, and the business cycle: Do technology shocks explain aggregate fluctuations? American Economic Review 89 (1), 249–271.Google Scholar
Gali, Jordi and Rabanal, Pau (2005) Technology shocks and aggregate fluctuations: How well does the real business cycle model fit postwar U.S. data? NBER Macroeconomics Annual 19, 225–288.Google Scholar
Gil-Alana, Luis Alberiko and Moreno, Antonio (2009) Technology shocks and hours worked: A fractional integration perspective. Macroeconomic Dynamics 13, 580–604.Google Scholar
Groth, Charlotta and Khan, Hashmat (2010) Investment adjustment costs: An empirical assessment. Journal of Money, Credit and Banking 42 (8), 1469–1494.Google Scholar
Harvey, A.C. (1985) Trends and cycles in macroeconomic time series. Journal of Business and Economic Statistics 3 (3), 216–227.Google Scholar
Huang, Kevin X. D., Liu, Zheng, and Zha, Tao (2009) Learning, adaptive expectations and technology shocks. Economic Journal 119 (536), 377–405.CrossRefGoogle Scholar
Kydland, Finn and Prescott, Edward (1982) Time to build and aggregate fluctuations. Econometrica 50 (6), 1345–1370.Google Scholar
Linde, Jesper (2009) The effects of permanent technology shocks on hours: Can the RBC-model fit the VAR evidence? Journal of Economic Dynamics and Control 33 (3), 597–613.CrossRefGoogle Scholar
Moscoso Boedo, Hernan J. (2010) Optimal technology and development. Journal of Macroeconomics 32 (2), 617–634.Google Scholar
Nakamura, Emi and Steinsson, Jon (2008) Five facts about prices: A reevaluation of menu cost models. Quarterly Journal of Economics 123 (4), 1415–1464.Google Scholar
Oliner, Stephen D. and Sichel, Daniel E. (1994) Computers and output growth revisited: How big is the puzzle? Brookings Papers on Economic Activity 2, 273–334.Google Scholar
Oliner, Stephen D. and Sichel, Daniel E.. (2000) The resurgence of growth in the late 1990s: Is information technology the story? Journal of Economic Perspectives 14 (4), 3–22.Google Scholar
Pesavento, Elena and Rossi, Barbara (2005) Do technology shocks drive hours up or down? A little evidence from an agnostic procedure. Macroeconomic Dynamics 9, 478–488.Google Scholar
Quandt, R.E. (1960) Tests of the hypothesis that a linear regression system obeys two separate regimes. Journal of the American Statistical Association 55, 324–330.CrossRefGoogle Scholar
Rogers, Evertt M. (1995) Diffusion of Innovations, 4th ed., New York: Free Press.Google Scholar
Rotemberg, Julio J. (2003) Stochastic technical progress, smooth trends, and nearly distinct business cycles. American Economic Review 93 (5), 1543–1559.Google Scholar
Stock, James H. and Watson, Mark W. (1998) Median unbiased estimation of coefficient variance in a time-varying parameter model. Journal of the American Statistical Association 93 (2), 349–358.CrossRefGoogle Scholar
Van Nieuwerburgh, Stijn and Veldkamp, Laura (2006) Learning asymmetries in real business cycles. Journal of Monetary Economics 53 (4), 753–772.Google Scholar