The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Bayesian Methods in Macroeconometrics

  • Frank Schorfheide
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2367

Abstract

This article discusses how Bayesian methods can be used to cope with challenges that arise in the econometric analysis of dynamic stochastic general equilibrium models and vector autoregressions.

Keywords

Bayes’ theorem Bayesian methods in macroeconometrics Business cycles Calibration Cowles Commission Dynamic macroeconomics Dynamic stochastic general equilibrium (DSGE) models Economic growth Estimation Expectations Identification Statistical inference Intertemporal optimization problems Joint probability distributions Likelihood functions Macroeconometrics Misspecification Monetary policy shocks Neoclassical growth model Probability Rational expectations Structural change System-of-equations models Technology shocks Vector autoregressions Linear models Markov chain Monte Carlo methods Stochastic growth models Habit formation Maximum likelihood Posterior probability Regime-switching models Latent state variables State-space models 

JEL Classifications

D4 D10 
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Bibliography

  1. An, S., and F. Schorfheide. 2007. Bayesian analysis of DSGE models. Econometric Reviews 26: 113–172.CrossRefGoogle Scholar
  2. Beyer, A., and R. Farmer. 2004. On the indeterminacy of new-Keynesian economics. Working Paper No. 323, European Central Bank.Google Scholar
  3. Canova, F. 1994. Statistical inference in calibrated models. Journal of Applied Econometrics 9: S123–SS44.CrossRefGoogle Scholar
  4. Canova, F., and L. Sala. 2006. Back to square one: Identification issues in DSGE models. Working Paper No. 583, European Central Bank.Google Scholar
  5. Christiano, L., and M. Eichenbaum. 1999. Monetary policy shocks: What have we learned and to what end? Handbook of macroeconomics, vol. 1A, ed. J. Taylor and M. Woodford. Amsterdam: North-Holland.Google Scholar
  6. Christiano, L., M. Eichenbaum, and C. Evans. 2005. Nominal rigidities and the dynamic effects of a shock to monetary policy. Journal of Political Economy 113: 1–45.CrossRefGoogle Scholar
  7. Cochrane, J. 1994. Shocks. Carnegie Rochester Conference Series 41: 295–364.CrossRefGoogle Scholar
  8. Cogley, T., and T. Sargent. 2005. Drifts and volatilities: Monetary policies and outcomes in the post WWII U.S. Review of Economic Dynamics 8: 262–302.CrossRefGoogle Scholar
  9. DeJong, D., B. Ingram, and C. Whiteman. 1996. A Bayesian approach to calibration. Journal of Business Economics and Statistics 14: 1–9.Google Scholar
  10. DeJong, D., B. Ingram, and C. Whiteman. 2000. A Bayesian approach to dynamic macroeconomics. Journal of Econometrics 98: 201–223.CrossRefGoogle Scholar
  11. Del Negro, M., and F. Schorfheide. 2004. Priors from equilibrium models for VARs. International Economic Review 45: 643–673.CrossRefGoogle Scholar
  12. Del Negro, M., F. Schorfheide, F. Smets, and R. Wouters. 2006, forthcoming. On the fit of new Keynesian models. Journal of Business and Economic Statistics.Google Scholar
  13. Doan, T., R. Litterman, and C. Sims. 1984. Forecasting and conditional projections using realistic prior distributions. Econometric Reviews 3: 1–100.CrossRefGoogle Scholar
  14. Fair, R. 1994. Testing macroeconomic models. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
  15. Fernández-Villaverde, J., and Rubio-Ramírez, J. 2006, forthcoming. Estimating macroeconomic models: A likelihood approach. Review of Economic Studies.Google Scholar
  16. Geweke, J. 1999. Computational experiments and reality. Computing in Economics and Finance, No. 401. Society for Computational Economics, Department of Economics, Boston College.Google Scholar
  17. Ingram, B., and C. Whiteman. 1994. Supplanting the Minnesota prior – Forecasting macroeconomic time series using real business cycle model priors. Journal of Monetary Economics 34: 497–510.CrossRefGoogle Scholar
  18. King, R., C. Plosser, and S. Rebelo. 1988. Production, growth, and business cycles: I. Neoclassical model. Journal of Monetary Economics 81: 819–840.Google Scholar
  19. Kydland, F., and E. Prescott. 1996. The computational experiment: An econometric tool. Journal of Economic Perspectives 10(1): 69–85.CrossRefGoogle Scholar
  20. Landon-Lane, J. 1998. Bayesian comparison of dynamic macroeconomic models. PhD thesis, University of Minnesota.Google Scholar
  21. Levin, A., A. Onatski, J. Williams, and N. Williams. 2006. Monetary policy under uncertainty in micro-founded macroeconometric models. In NBER macroeconomics annual 2005, ed. M. Gertler and K. Rogoff. Cambridge, MA: MIT Press.Google Scholar
  22. Lubik, T., and F. Schorfheide. 2004. Testing for indeterminacy: An application to U.S. monetary policy. American Economic Review 94: 190–217.CrossRefGoogle Scholar
  23. Lubik, T., and F. Schorfheide. 2006. A Bayesian look at new open economy macroeconomics. In NBER macroeconomics annual 2005, ed. M. Gertler and K. Rogoff. Cambridge, MA: MIT Press.Google Scholar
  24. Lucas, R. Jr. 1976. Econometric policy evaluation: A critique. In The Phillips curve and labor markets, ed. K. Brunner and A. Meltzer. Amsterdam: North-Holland.Google Scholar
  25. Otrok, C. 2001. On measuring the welfare cost of business cycles. Journal of Monetary Economics 47: 61–92.CrossRefGoogle Scholar
  26. Sargent, T., N. Williams, and T. Zha. 2006. Shocks and government beliefs: The rise and fall of American inflation. American Economic Review 96: 1193–1224.CrossRefGoogle Scholar
  27. Schorfheide, F. 2000. Loss function-based evaluation of DSGE models. Journal of Applied Econometrics 15: 645–670.CrossRefGoogle Scholar
  28. Sims, C. 1980. Macroeconomics and reality. Econometrica 48: 1–48.CrossRefGoogle Scholar
  29. Sims, C., and T. Zha. 2006. Were there regime switches in U.S. monetary policy? American Economic Review 96: 54–81.CrossRefGoogle Scholar
  30. Smets, F., and R. Wouters. 2003. An estimated stochastic dynamic general equilibrium model of the Euro area. Journal of the European Economic Association 1: 1123–1175.CrossRefGoogle Scholar
  31. Stock, J., and M. Watson. 2001. Vector autoregressions. Journal of Economic Perspectives 15(4): 101–116.CrossRefGoogle Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Frank Schorfheide
    • 1
  1. 1.