Abstract
Vector autoregressions are a class of dynamic multivariate models introduced by Sims (1980) to macroeconomics. These models have been primarily used to bring empirical regularities out of the time series data, to provide forecasting and policy analysis, and to serve as a benchmark for model comparison. Economic applications often impose more restrictions on vector autoregressions than originally thought necessary. Recent econometric developments have made it feasible to handle vector autoregressions with a wide class of restrictions and have narrowed the gap between these models and dynamic stochastic general equilibrium models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
Bernanke, B. 1986. Alternative exploration of the money-income correlation. Carnegie-Rochester Conference Series on Public Policy 25: 49–99.
Blanchard, O., and D. Quah. 1993. The dynamic effects of aggregate demand and supply disturbances. American Economic Review 83: 655–673.
Blanchard, O., and M. Watson. 1986. Are business cycles all alike? In The American business cycle: Continuity and change, ed. R. Gordon. Chicago: University of Chicago Press.
Chib, S. 1995. Marginal likelihood from the Gibbs output. Journal of the American Statistical Association 90: 1313–1321.
Christiano, L., M. Eichenbaum, and C. Evans. 1999. Monetary policy shocks: What have we learned and to what end? In Handbook of Macroeconomics, ed. J. Taylor and M. Woodford, Vol. 1A. Amsterdam: North-Holland.
Christiano, L., M. Eichenbaum, and C. Evans. 2005. Nominal rigidities and the dynamics effects of a shock to monetary policy. Journal of Political Economy 113: 1–45.
Cogley, T., and J. Nason. 1995. Output dynamics in real business cycle models. American Economic Review 85: 492–511.
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.
Cushman, D., and T. Zha. 1997. Identifying monetary policy in a small open economy under flexible exchange rates. Journal of Monetary Economics 39: 433–448.
Davig, T., and E. Leeper. 2005. Fluctuating macro policies and the fiscal theory. Working Paper No. 11212. Cambridge, MA: NBER.
Del Negro, M., and F. Schorfheide. 2004. Priors from general equilibrium models for VARs. International Economic Review 45: 643–673.
Evans, C., and D. Marshall. 2002. Economic determinants of the nominal treasury yield curve. Working paper. Federal Reserve Bank of Chicago.
Faust, J., and E. Leeper. 1997. When do long-run identifying restrictions give reliable results? Journal of Business and Economic Statistics 15: 345–353.
Fernandez-Villaverde, J., J. Rubio-Ramirez, and T. Sargent. 2005. A, B, C’s (and D’s) for understanding VARs. Working Paper No. 2005–9. Federal Reserve Bank of Atlanta.
Gali, J. 1992. How well does the IS-LM model fit postwar U.S. data? Quarterly Journal of Economics 107: 709–738.
Geweke, J. 1999. Using simulation methods for Bayesian econometric models: Inference, development, and communication. Econometric Reviews 18: 1–73.
Geweke, J., and C. Whiteman. 2006. Bayesian forecasting. In The handbook of economic forecasting, ed. G. Elliott, C. Granger, and A. Timmermann. Amsterdam: North-Holland.
Gordon, D., and E. Leeper. 1994. The dynamic impacts of monetary policy: An exercise in tentative identification. Journal of Political Economy 102: 1228–1247.
Hamilton, J. 1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57: 357–384.
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.
Leeper, E., and T. Zha. 2003. Modest policy interventions. Journal of Monetary Economics 50: 1673–1700.
Leeper, E., C. Sims, and T. Zha. 1996. What does monetary policy do? Brookings Papers on Economic Activity 2: 1–78.
Litterman, R. 1986. Forecasting with Bayesian vector autoregressions – Five years of experience. Journal of Business and Economic Statistics 4: 25–38.
Nason, J., and T. Cogley. 1994. Testing the implications of long-run neutrality for monetary business cycle models. Journal of Applied Econometrics 9: S37–S70.
Nason, J., and J. Rogers. 2006. The present-value model of the current account has been rejected: Round up the usual suspects. Journal of International Economics 68: 159–187.
Robertson, J., and E. Tallman. 1999. Vector autoregressions: Forecasting and reality. Federal Reserve Bank of Atlanta Economic Review 84(1): 4–18.
Robertson, J., and E. Tallman. 2001. Improving federal-funds rate forecasts in VAR models used for policy analysis. Journal of Business and Economic Statistics 19: 324–330.
Rubio-Ramirez, J., D. Waggoner, and T. Zha. 2005. Markov-switching structural vector autoregressions: Theory and applications. Working Paper No. 2005–27. Federal Reserve Bank of Atlanta.
Sims, C. 1980. Macroeconomics and reality. Econometrica 48: 1–47.
Sims, C. 1982. Policy analysis with econometric models. Brookings Papers on Economic Activity 1: 107–152.
Sims, C. 1986. Are forecasting models usable for policy analysis. Federal Reserve Bank of Minneapolis Quarterly Review 10(1): 2–16.
Sims, C., and T. Zha. 1998. Bayesian methods for dynamic multivariate models. International Economic Review 39: 949–968.
Sims, C., and T. Zha. 1999. Error bands for impulse responses. Econometrica 67: 1113–1155.
Sims, C., and T. Zha. 2006a. Does monetary policy generate recessions? Macroeconomic Dynamics 10(2): 231–272.
Sims, C., and T. Zha. 2006b. Were there regime switches in US monetary policy? American Economic Review 96: 54–81.
Smets, F., and R. Wouters. 2003. An estimated dynamic stochastic general equilibrium model of the euro area. Journal of the European Economic Association 1: 1123–1175.
Stock, J., and M. Watson. 2003. Has the business cycle changed? Evidence and explanations. Prepared for the federal reserve bank of Kansas city symposium ‘Monetary policy and uncertainty: Adapting to a changing economy’, Jackson Hole, Wyoming, 28–30 August.
Waggoner, D., and T. Zha. 2003a. Likelihood-preserving normalization in multiple equation models. Journal of Econometrics 114: 329–347.
Waggoner, D., and T. Zha. 2003b. A Gibbs simulator for structural vector autoregressions. Journal of Economic Dynamics & Control 28: 349–366.
Zha, T. 1999. Block recursion and structural vector autoregressions. Journal of Econometrics 90: 291–316.
Author information
Authors and Affiliations
Editor information
Copyright information
© 2018 Macmillan Publishers Ltd.
About this entry
Cite this entry
Zha, T. (2018). Vector Autoregressions. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2452
Download citation
DOI: https://doi.org/10.1057/978-1-349-95189-5_2452
Published:
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-1-349-95188-8
Online ISBN: 978-1-349-95189-5
eBook Packages: Economics and FinanceReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences