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Correlated Disturbances and U.S. Business Cycles

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  • Cúrdia, Vasco
  • Reis, Ricardo

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 to make the estimation of DSGE models with correlated disturbances feasible and quick. 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 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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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 7712.

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Date of creation: Feb 2010
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Handle: RePEc:cpr:ceprdp:7712

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Keywords: Bayesian estimation; DSGE; Robustness;

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  1. Del Negro, Marco & Schorfheide, Frank, 2005. "Monetary policy analysis with potentially misspecified models," Working Paper Series 0475, European Central Bank.
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Cited by:
  1. 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.
  2. 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.
  3. Saroj Bhattarai & Jae Won Lee & Woong Yong Park, 2012. "Policy regimes, policy shifts, and U.S. business cycles," Globalization and Monetary Policy Institute Working Paper 109, Federal Reserve Bank of Dallas.
  4. Pablo A Guerron-Quintana & James M Nason, 2012. "Bayesian Estimation of DSGE Models," CAMA Working Papers 2012-10, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  5. 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., vol. 29(5), pages 843-859, 08.
  6. Fève, Patrick & Sahuc, Jean-Guillaume, 2013. "On the Size of the Government Spending Multiplier in the Euro Area," TSE Working Papers 13-396, Toulouse School of Economics (TSE), revised Nov 2013.
  7. Christoffel, Kai & Jaccard, Ivan & Kilponen, Juha, 2011. "Government bond risk premia and the cyclicality of fiscal policy," Working Paper Series 1411, European Central Bank.
  8. 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).

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