Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-10T16:19:27.264Z Has data issue: false hasContentIssue false
  • English
  • Français

FAT-TAIL DISTRIBUTIONS AND BUSINESS-CYCLE MODELS

Published online by Cambridge University Press:  08 October 2013

Guido Ascari ,
Giorgio Fagiolo  and
Andrea Roventini
Guido Ascari
Affiliation:
University of Pavia
Giorgio Fagiolo*
Affiliation:
Sant'Anna School of Advanced Studies
Andrea Roventini
Affiliation:
University of Verona
*
Address correspondence to: Giorgio Fagiolo, Sant'Anna School of Advanced Studies, Pisa, Italy; e-mail: giorgio.fagiolo@sssup.it.
Get access Rights & Permissions [Opens in a new window]

Abstract

Recent empirical findings suggest that macroeconomic variables are seldom normally distributed. For example, the distributions of aggregate output growth-rate time series of many OECD countries are well approximated by symmetric exponential-power (EP) densities with Laplace fat tails. In this work, we assess whether real business cycle (RBC) and standard medium-scale New Keynesian (NK) models are able to replicate this statistical regularity. We simulate both models, drawing Gaussian- vs Laplace-distributed shocks, and we explore the statistical properties of simulated time series. Our results cast doubts on whether RBC and NK models are able to provide a satisfactory representation of the transmission mechanisms linking exogenous shocks to macroeconomic dynamics.

Keywords

Growth-Rate DistributionsNormalityFat TailsTime SeriesExponential-Power DistributionsLaplace DistributionsDSGE ModelsRBC Models
Type
Notes
Information
Macroeconomic Dynamics , Volume 19 , Issue 2 , March 2015 , pp. 465 - 476
Copyright
Copyright © Cambridge University Press 2013 

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

Agrò, G. (1995) Maximum likelihood estimation for the exponential power function parameters. Communications in Statistics 24, 523–536.CrossRefGoogle Scholar
Ascari, G., Flamini, A., and Rossi, L. (2012) Nominal Rigidities, Supply Shocks and Economic Stability. DEM working paper 24, Department of Economics and Management, University of Pavia.Google Scholar
Barro, R.J. (2006) Rare disasters and asset markets in the twentieth century. Quarterly Journal of Economics 1213, 823–866.CrossRefGoogle Scholar
Bottazzi, G. and Secchi, A. (2003) Common properties and sectoral specificities in the dynamics of U.S. manufacturing firms. Review of Industrial Organization 23, 217–232.CrossRefGoogle Scholar
Canning, D., Amaral, L.A.N., Lee, Y., Meyer, M., and Stanley, H.E. (1998) Scaling the volatility of GDP growth rates. Economic Letters 60, 335–341.CrossRefGoogle Scholar
Castaldi, C. and Dosi, G. (2009) The patterns of output growth of firms and countries: Scale invariances and scale specificities. Empirical Economics 373, 475–495.CrossRefGoogle Scholar
Castaldi, C. and Sapio, S. (2008) Growing like mushrooms? Sectoral evidence from four large European economies. Journal of Evolutionary Economics 18, 509–527.CrossRefGoogle Scholar
Christiano, L.J. (2007) Comment on “On the Fit of New Keynesian Models” by Del Negro, Schorfheide, Smets and Wouters. Journal of Business and Economic Statistics 252, 143–151.CrossRefGoogle Scholar
Christiano, L.J., Eichenbaum, M., and Evans, C.L. (2005) Nominal rigidities and the dynamic effects of a shock to monetary policy. Journal of Political Economy 113, 1–45.CrossRefGoogle Scholar
Cogley, T. and Nason, J.M. (1995) Output dynamics in real-business-cycle models. American Economic Review 85, 492–511.Google Scholar
De Grauwe, Paul (2012) Booms and busts in economic activity: A behavioral explanation. Journal of Economic Behavior & Organization 83 (3), 484–501.CrossRefGoogle Scholar
Embrechts, P., Küppelberg, C., and Mikosch, T. (1997) Modelling Extremal Events for Insurance and Finance. Berlin: Springer.CrossRefGoogle Scholar
Fagiolo, G., Napoletano, M., Piazza, M., and Roventini, A. (2009) Detrending and the distributional properties of U.S. output time series. Economics Bulletin 29, 3155–3161.Google Scholar
Fagiolo, G., Napoletano, M., and Roventini, A. (2008) Are output growth-rate distributions fat-tailed? Some evidence from OECD countries. Journal of Applied Econometrics 23, 639–669.CrossRefGoogle Scholar
Fernandez-Villaverde, P. Guerròn-Quintana, and Rubio-Ramìrez, J. (2012) Estimating Dynamic Equilibrium Models with Stochastic Volatility. NBER Working Paper 18399, National Bureau of Economic Research.CrossRefGoogle Scholar
Fernández-Villaverde, J. and Rubio-Ramìrez, J. F. (2007) Estimating macroeconomic models: A likelihood approach. Review of Economic Studies 744, 1059–1087.CrossRefGoogle Scholar
Fu, D., Pammolli, F., Buldyrev, S., Riccaboni, M., Matia, K., Yamasaki, K., and Stanley, H. (2005) The growth of business firms: Theoretical framework and empirical evidence. Proceedings of the National Academy of Science 102, 18,801–1806.CrossRefGoogle ScholarPubMed
Gertler, M. and Kiyotaki, N. (2010) Financial intermediation and credit policy in business cycle analysis. In Friedman, B.M. and Woodford, M. (eds.), Handbook of Monetary Economics, pp. 547–599. Amsterdam: North Holland Elsevier.Google Scholar
Greenwald, B. and Stiglitz, J. (1993) Financial market imperfections and business cycles. Quarterly Journal of Economics 108, 77–114.CrossRefGoogle Scholar
Justiniano, A. and Primiceri, G. (2008) The time-varying volatility of macroeconomic fluctuations. American Economic Review 983, 604–641.CrossRefGoogle Scholar
Lee, Y., Amaral, L.A.N., Canning, D., Meyer, M., and Stanley, H.E. (1998) Universal features in the growth dynamics of complex organizations. Physical Review Letters 81, 3275–3278.CrossRefGoogle Scholar
Mishkin, F.S. (2011) Monetary Policy Strategy: Lessons from the Crisis. NBER working paper 16755, National Bureau of Economic Research.CrossRefGoogle Scholar
Posch, O. (2009) Structural estimation of jump-diffusion processes in macroeconomics. Journal of Econometrics 153, 196–210.CrossRefGoogle Scholar
Rietz, T.A. (1988) The equity risk premium: A solution. Journal of Monetary Economics 22, 1002–1037.CrossRefGoogle Scholar
Rotemberg, J. and Woodford, M. (1996) Real-business-cycle models and forecastable movements in output, hours, and consumption. American Economic Review 86, 71–89.Google Scholar
Schmitt-Grohé, S. and Uribe, M. (2006) Optimal fiscal and monetary policy in a medium-scale macroeconomic model. In Gertler, M. and Rogoff, K. (eds.), NBER Macroeconomics Annual, pp. 383–425. Cambridge, MA: MIT Press.Google Scholar
Smets, F. and Wouters, R. (2003) An estimated dynamic stochastic general equilibrium model of the euro area. Journal of the European Economic Association 1, 1123–1175.CrossRefGoogle Scholar