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Correlated disturbances and U.S. business cycles

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  • Vasco Cúrdia
  • Ricardo Reis

Abstract

The dynamic stochastic general equilibrium (DSGE) models used to study business cycles typically assume that exogenous disturbances are independent first-order autoregressions. This paper relaxes this tight and arbitrary restriction by allowing for disturbances that have a rich contemporaneous and dynamic correlation structure. Our first contribution is a new Bayesian econometric method that uses conjugate conditionals to allow for feasible and quick estimation of DSGE models with correlated disturbances. Our second contribution is a reexamination of U.S. business cycles. We find that allowing for correlated disturbances resolves some conflicts between estimates from DSGE models and those from vector autoregressions and that a key missing ingredient in the models is countercyclical fiscal policy. According to our estimates, government spending and technology disturbances play a larger role in the business cycle than previously ascribed, while changes in markups are less important.

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Bibliographic Info

Paper provided by Federal Reserve Bank of New York in its series Staff Reports with number 434.

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Date of creation: 2010
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Handle: RePEc:fip:fednsr:434

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Keywords: Business cycles ; Equilibrium (Economics) ; Bayesian statistical decision theory ; Vector autoregression ; Fiscal policy ; Government spending policy;

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References

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  1. Alejandro Justiniano & Bruce Preston, 2006. "Can Structural Small Open Economy Models Account For The Influence Of Foreign Disturbances?," CAMA Working Papers 2006-12, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
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Citations

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Cited by:
  1. Milani, Fabio, 2010. "Expectation Shocks and Learning as Drivers of the Business Cycle," CEPR Discussion Papers 7743, C.E.P.R. Discussion Papers.
  2. Pablo A. Guerrón-Quintana & James M. Nason, 2012. "Bayesian estimation of DSGE models," Working Papers 12-4, Federal Reserve Bank of Philadelphia.
  3. Gospodinov, Nikolay & Lkhagvasuren, Damba, 2013. "A moment-matching method for approximating vector autoregressive processes by finite-state Markov chains," Working Paper 2013-05, Federal Reserve Bank of Atlanta.
  4. Kónya, István, 2011. "Növekedés és felzárkózás Magyarországon, 1995-2009
    [Growth and convergence in Hungary, 1995-2009]
    ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 393-411.
  5. István Kónya, 2011. "Convergence and Distortions: the Czech Republic, Hungary and Poland between 1996–2009," MNB Working Papers 2011/6, Magyar Nemzeti Bank (the central bank of Hungary).
  6. Woong Yong Park & Jae Won Lee & Saroj Bhattarai, 2012. "Policy Regimes, Policy Shifts, and U.S. Business Cycles," 2012 Meeting Papers 287, Society for Economic Dynamics.
  7. Fève, Patrick & Sahuc, Jean-Guillaume, 2013. "On the Size of the Government Spending Multiplier in the Euro Area," IDEI Working Papers 776, Institut d'Économie Industrielle (IDEI), Toulouse, revised Nov 2013.
  8. Christoffel, Kai & Jaccard, Ivan & Kilponen, Juha, 2011. "Government bond risk premia and the cyclicality of fiscal policy," Working Paper Series 1411, European Central Bank.

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