Fuentes-Albero, Cristina (2007): Technology Shocks, Statistical Models, and The Great Moderation.
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In this paper we compare the cyclical features implied by an RBC model with two technology shocks under several statistical specifications for the stochastic processes governing technological change. We conclude that while a trend-stationary model accounts better for the observed volatilities, a difference-stationary model does a relatively better job of accounting for the correlation of the variables of interest with output. We also explore some counterfactuals to assess the ability of our model to replicate the volatility slowdown of the mid 1980s. First, we conclude that the stochastic growth model outperforms the deterministic growth model in accounting for the Great Moderation. Finally, we obtain that even though the neutral technology shock is the main driving force in the volatility slowdown, allowing for a larger financial flexibility in the form of a smaller volatility for the investment-specific innovation improves the ability of our model to account for the magnitude of the Great Moderation.
|Item Type:||MPRA Paper|
|Institution:||University of Pennsylvania|
|Original Title:||Technology Shocks, Statistical Models, and The Great Moderation|
|Keywords:||Business Cycle; Aggregate fluctuations; Technology Shocks; Unit Roots|
|Subjects:||O - Economic Development, Technological Change, and Growth > O3 - Technological Change; Research and Development; Intellectual Property Rights > O30 - General
E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations; Cycles
C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C32 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
|Depositing User:||Cristina Fuentes-Albero|
|Date Deposited:||16. Jun 2007|
|Last Modified:||18. Feb 2013 14:19|
Ahmed, S., A. Levin, and B. A. Wilson (2004): “Recent US Macroeconomic Stability: Good Policies, Good Practices, or Good Luck?,” Review of Economics and Statistics, 86(3), 824–832. Arias, A., G. D. Hansen, and L. E. Ohanian (2007): “Why Have Business Cycle Fluctuations Become Less Volatile?,” Economic Theory, Forthcoming. Beveridge, S., and C. Nelson (1981): “A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the ’business cycle’,” Journal of Monetary Economics, 7, 151–174. Blanchard, O., and J. Simon (2001): “The long and large decline in U.S. output volatility,” Brookings Papers on Economic Activity, 1, 135–164. Burnside, C., and M. Eichenbaum (1996): “Factor-Hoarding and the Propagation of Business-Cycles Shocks,” The American Economic Review, 86(5), 1154–1174. Campbell, J. R., and Z. Hercowitz (2005): “The Role of Collateralized Household Debt in Macroeconomic Stabilization,” Working Paper, Federal Reserve Bank of Chicago. Chang, Y., T. Doh, and F. Schorfheide (2007): “Non-stationary Hours in a DSGE Model,” Journal of Money, Credit, and Banking, Forthcoming. Chari, V., P. J. Kehoe, and E. McGrattan (2004): “Business Cycle Accounting,” NBER Working Paper No. 10351. Christiano, L. J. (1988): “Why does inventory investment fluctuate so much?,” Journal of Monetary Economics, 21, 247–280. Christiano, L. J., M. Eichenbaum, and R. Vigfusson (2003): “What happens after a technology shock?,” NBER Working Paper 9819. Cooley, T. F., and E. C. Prescott (1995): “Economic Growth and Business Cycle,” in Frontiers of Business Cycle Research, ed. by T. F. Cooley, chap. 1. Princeton University Press, Princeton. Dynan, K. E., D. W. Elmendorf, and D. E. Sichel (2005): “Can Financial Innovation Help to Explain the Reduced Volatility of Economic Activity?,” Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. (2006): “Financial Innovation and the Great Moderation: What Do Household Data Say?,” Mimeo. Fern´andez-Villaverde, J., and J. F. Rubio-Ram´ırez (2006): “Estimating Macroeconomic Models: A Likelihood Approach,” Mimeo, Duke University. Fisher, J. D. (2006): “The Dynamic Effects of Neutral and Investment-Specific Technology Shocks,” Journal of Political Economy, 114(3), 413–451. Gal´ı, J. (1999): “Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?,” The American Economic Review, 89(1), 249–271. Gal´ı, J., and P. Rabanal (2004): “Technology Shocks and Aggregate Fluctuations: How Well Does the Real Business Cycle Model Fit Postwar US Data?,” IMF Working Paper WP/04/234. Gomme, P., and P. Rupert (2007): “Theory, Measurement, and Calibration of Macroeconomic Models,” Journal of Monetary Economics, 54, 460–497. Greenwood, J., Z. Hercowitz, and P. Krusell (1997): “Long-Run Implications of Investment-Specific Technological Change,” The American Economic Review, 83, 342–362. (2000): “The role of investment-specific technological change in the business cycle,” European Economic Review, 44, 91–115. Guerron, P. A. (2006): “Financial Innovations: An Alternative Explanation to the Great Moderation,” Mimeo. Hamilton, J. D. (1994): Time Series Analysis. Princeton University Press. Hansen, G. D. (1997): “Technical progress and aggregate fluctuations,” Journal of Economic Dynamics and Control, 21, 1005–1023 Jermann, U., and V. Quadrini (2006): “Financial Innovations and Macroeconomic Volatility,” NBER Working Paper 12308. Justiniano, A., and G. E. Primiceri (2006): “The time varying volatility of macroeconomic fluctuations,” NBER Working Paper 12022. Kahn, J. A., M. M. McConnell, and G. P´erez-Quir´os (2002): “On the Causes of the Increased Stability of the U.S. Economy,” Federal Reserve Bank of New York Economic Policy Review, 8(1), 183–202. Kim, C.-J., J. C. Morley, and J. Piger (2004): “A Bayesian Approach to Counterfactual Analysis of Structural Change,” FRB St Louis Working Paper No. 2004-014C. Kim, C.-J., and C. R. Nelson (1999): “Has the U.S. Economy Become More Stable? A Bayesian Approach Based on a Markov-Switching Model of Business Cycle,” Review of Economics and Statistics, 81(4), 608–616. King, R. G., C. I. Plosser, and S. T. Rebelo (1988): “Production, Growth, and Business Cycles II. New Directions,” Journal of Monetary Economics, 21, 309–341. Leduc, S., and K. Sill (2006): “Monetary Policy, Oil Shocks, and TFP: Accounting for the Decline in U.S. Volatility,” Board of Governors of the Federal Reserve System. International Finance Discussion Papers N. 873. McConnell, M. M., and G. P´erez-Quir´os (2000): “Output fluctuations in the United States: what has changed since the early 1980’s?,” American Economic Review, 90(5), 1464–1476. Nelson, C., and C. Plosser (1982): “Trends and random walks in macroeconomic time series: Some evidence and implications,” Journal of Monetary Economics, 10, 139–162. Prescott, E. C. (1986): “Response to a Skeptic,” Quarterly Review. Minneapolis Federal Reserve Bank, 10, 28–33. Stock, J. H., and M. W. Watson (2002): “Has the business cycle changed and why?,” NBER Working Paper 9127.