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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 that are used to study business cycles typically assume that exogenous disturbances are independent autoregressions of order one. 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 and Gibbs sampling to make the estimation of DSGE models with correlated disturbances feasible. This provides a useful check for model misspecification in the search for models with structural disturbances. Our second contribution is a re-examination 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 treating government spending as exogenous in spite of its clear countercyclicality in the data is the main source of misspecification. 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 National Bureau of Economic Research, Inc in its series NBER Working Papers with number 15774.

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Date of creation: Feb 2010
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Handle: RePEc:nbr:nberwo:15774

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Cited by:
  1. Nikolay Gospodinov & Damba Lkhagvasuren, 2014. "A Moment‐Matching Method For Approximating Vector Autoregressive Processes By Finite‐State Markov Chains," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 29(5), pages 843-859, 08.
  2. Woong Yong Park & Jae Won Lee & Saroj Bhattarai, 2012. "Policy Regimes, Policy Shifts, and U.S. Business Cycles," 2012 Meeting Papers, Society for Economic Dynamics 287, Society for Economic Dynamics.
  3. István Kónya, 2011. "Convergence and Distortions: the Czech Republic, Hungary and Poland between 1996–2009," MNB Working Papers, Magyar Nemzeti Bank (the central bank of Hungary) 2011/6, Magyar Nemzeti Bank (the central bank of Hungary).
  4. Fève, Patrick & Sahuc, Jean-Guillaume, 2013. "On the Size of the Government Spending Multiplier in the Euro Area," TSE Working Papers, Toulouse School of Economics (TSE) 13-396, Toulouse School of Economics (TSE), revised Nov 2013.
  5. Pablo A. Guerrón-Quintana & James M. Nason, 2012. "Bayesian estimation of DSGE models," Working Papers 12-4, Federal Reserve Bank of Philadelphia.
  6. Fabio Milani, 2011. "Expectation Shocks and Learning as Drivers of the Business Cycle," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 121(552), pages 379-401, 05.
  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, European Central Bank 1411, European Central Bank.

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