Incorporating Conjunctural Analysis in Structural Models

  • Domenico Giannone
  • Francesca Monti
  • Lucrezia Reichlin

This volume celebrates the work of Michael Woodford and his many contributions to economics.

One of Mike's most influential papers is the 1997 paper (co-authored with Julio Rotemberg) “An optimization-based econometric framework for the evaluation of monetary policy.” This paper constituted the first attempt at estimation of a small scale dynamic stochastic general equilibrium model (DSGE) in which prices are set by monopolistically competitive firms, and prices cannot be instantaneously and costlessly adjusted. Since the work of Rotemberg and Woodford, these models have become more complex and increasingly large [see Christiano, Eichenbaum and Evans (2005), Smets and Wouters (2003), and, more recently, Christoffel, Coenen and Warne (2008) and Adolfson, Laséen, Lindé and Svensson (2008)]. By explicitly taking into account forwardlooking behavior on the part of the agents, DSGEs provide a useful framework to analyze the effects of alternative policies. These models are now routinely used in many central banks, including the European Central Bank, and knowledge has been built up on their reliability, their forecasting performance and on what are the reasonable values for calibrated parameters and the setting of the priors.

Keywords

Monetary Policy Total Factor Productivity Euro Area Capacity Utilization Total Factor Productivity Growth 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aastveit, K. A., and T. G. Trovik. 2008. “Nowcasting Norwegian GDP: The role of asset prices in a small open economy.” Norges Bank Working Paper 2007/09.Google Scholar
  2. Adolfson, M., S. Laséen, J. Lindé, and L. E. O. Svensson. 2008. “Opti mal Monetary Policy in an Operational Medium-Sized DSGE Model.” National Bureau of Economic Research Working Paper 14092.Google Scholar
  3. Anderson, B. D. O., and J. B. Moore. 1979. Optimal Filtering. Prentice-Hall.Google Scholar
  4. Anderson, E., L. P. Hansen, E. R. McGrattan, and T. J. Sargent. 1996. “Mechanics of Forming and Estimating Dynamic Linear Economies.” In Handbook of Computational Economics, Volume 1, ed. D. A. K. Hans, M. Amman, and J. Rust, 171–252. North-Holland.Google Scholar
  5. Angelini, E., G. Camba-Méndez, D. Giannone, G. Rünstler, and L. Reichlin. 2008. “Short-term forecasts of euro area GDP growth.” ECB mimeo.Google Scholar
  6. Banbura, M., D. Giannone, and L. Reichlin. 2008. “Large Bayesian VARs.” European Central Bank Working Paper Series 966.Google Scholar
  7. Blanchard, O. J., and C. M. Kahn. 1980. “The Solution of Linear Difference Models under Rational Expectations.” Econometrica, 48(5): 1305–1311.CrossRefGoogle Scholar
  8. Boivin, J., and M. Giannoni. 2006. “DSGE Models in a Data-Rich Environment.” National Bureau of Economic Research Working Paper 12772.Google Scholar
  9. Christiano, L. J., M. Eichenbaum, and C. L. evans. 2005. “Nominal Rigi dities and the Dynamic Effects of a Shock to Monetary Policy.” Journal of Political Economy, 113 (1): 1–45.CrossRefGoogle Scholar
  10. Christoffel, K., G. Coenen, and A. Warne. 2008. “The new area-wide model of the euro area — a micro—founded open-economy model for forecasting and policy analysis.” European Central Bank Working Paper Series 944.Google Scholar
  11. Del negro, M., and F. Schorfheide. 2004. “Priors from General Equilibrium Models for VARs.” International Economic Review, 45: 643–673.CrossRefGoogle Scholar
  12. Del negro, M., F. Schorfheide, F. Smets, and R. Wouters. 2007. “On the Fit of New Keynesian Models.” Journal of Business and Economic Statistics, 25: 123–143.CrossRefGoogle Scholar
  13. Forni, M., M. Hallin, M. Lippi, and L. Reichlin. 2000. “The Generalized Dynamic Factor Model: Identification and Estimation.” Review of Economics and Statistics, 82(4): 540–554.CrossRefGoogle Scholar
  14. Giacomini, R., and H. White. 2006. “Tests of Conditional Predictive Ability.” Econometrica, 74(6): 1545–1578.CrossRefGoogle Scholar
  15. Giannone, D., L. Reichlin, and D. Small. 2008. “Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases.” Journal of Monetary Economics, 55(4): 665–676.CrossRefGoogle Scholar
  16. Giannone, D., F. Monti, and L. Reichlin. 2008. “Short-term Analysis with Structural Models.” London Business School Mimeo.Google Scholar
  17. Klein, P. 2000. “Using the Generalized Schur Form to Solve a System of Linear Expectational Difference Equations.” Journal of Economic Dynamics and Control 24(10): 1405–1423.CrossRefGoogle Scholar
  18. Matheson, T. 2007. “An analysis of the informational content of New Zealand data releases: The importance of business opinion surveys.” Reserve Bank of New Zealand Discussion Paper Series DP2007/13, revised.Google Scholar
  19. Rotemberg, J., and M. Woodford. 1997. “An optimization-based econometric framework for the evaluation of monetary policy.” In NBER macroeconomics annual 1997, ed. B. S. Bernanke and J. J. Rotemberg, 297–346.Google Scholar
  20. Smets, F., and R. Wouters. 2003. “An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area.” Journal of the European Economic Association, 1 (5): 1123–175.CrossRefGoogle Scholar
  21. Sims, C. A. 2002. “Solving Linear Rational Expectations Models.” Computational Economics, 20(1–2): 1–20.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Domenico Giannone
    • 1
  • Francesca Monti
    • 2
  • Lucrezia Reichlin
    • 3
  1. 1.European Central BankFrankfurt am MainGermany
  2. 2.ECARES Université Libre de BruxellesBruxellesBelgium
  3. 3.London Business SchoolLondonUnited Kingdom

Personalised recommendations