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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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Citations

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Cited by:
  1. 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.
  2. 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.
  3. Nikolay Gospodinov & Damba Lkhagvasuren, 2011. "A Moment-Matching Method for Approximating Vector Autoregressive Processes by Finite-State Markov Chains," Working Papers 11005, Concordia University, Department of Economics, revised 16 Dec 2011.
  4. Fabio Milani, 2011. "Expectation Shocks and Learning as Drivers of the Business Cycle," Economic Journal, Royal Economic Society, vol. 121(552), pages 379-401, 05.
  5. Pablo A. Guerrón-Quintana & James M. Nason, 2012. "Bayesian estimation of DSGE models," Working Papers 12-4, Federal Reserve Bank of Philadelphia.
  6. 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).
  7. 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.
  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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