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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.

Suggested Citation

  • Vasco Cúrdia & Ricardo Reis, 2010. "Correlated Disturbances and U.S. Business Cycles," NBER Working Papers 15774, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:15774
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    2. Bask, Mikael & Madeira, João, 2011. "The Increased Importance of Asset Price Misalignments for Business Cycle Dynamics," Working Paper Series 2011:12, Uppsala University, Department of Economics.
    3. Mehkari, M. Saif, 2016. "Uncertainty shocks in a model with mean-variance frontiers and endogenous technology choices," Journal of Macroeconomics, Elsevier, vol. 49(C), pages 71-98.
    4. Monti, Francesca, 2015. "Can a data-rich environment help identify the sources of model misspecification?," LSE Research Online Documents on Economics 86320, London School of Economics and Political Science, LSE Library.
    5. Fabio Milani, 2011. "Expectation Shocks and Learning as Drivers of the Business Cycle," Economic Journal, Royal Economic Society, vol. 121(552), pages 379-401, May.
    6. Inoue, Atsushi & Kuo, Chun-Hung & Rossi, Barbara, 2020. "Identifying the sources of model misspecification," Journal of Monetary Economics, Elsevier, vol. 110(C), pages 1-18.
    7. Patrick Fève & Jean-Guillaume Sahuc, 2015. "On the size of the government spending multiplier in the euro area," Oxford Economic Papers, Oxford University Press, vol. 67(3), pages 531-552.
    8. Faust, Jon & Gupta, Abhishek, 2010. "Posterior Predictive Analysis for Evaluating DSGE Models," MPRA Paper 26721, University Library of Munich, Germany.
    9. Madeira, João & Palma, Nuno, 2018. "Measuring monetary policy deviations from the Taylor rule," Economics Letters, Elsevier, vol. 168(C), pages 25-27.
    10. István Kónya, 2011. "Convergence and Distortions: the Czech Republic, Hungary and Poland between 1996–2009," MNB Working Papers 2011/6, Magyar Nemzeti Bank (Central Bank of Hungary).
    11. Pablo A. Guerrón-Quintana & James M. Nason, 2013. "Bayesian estimation of DSGE models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 21, pages 486-512, Edward Elgar Publishing.
    12. Christoffel, Kai & Kilponen, Juha & Jaccard, Ivan, 2011. "Government bond risk premia and the cyclicality of fiscal policy," Working Paper Series 1411, European Central Bank.
    13. Bachmann, Rüdiger & Bayer, Christian, 2013. "‘Wait-and-See’ business cycles?," Journal of Monetary Economics, Elsevier, vol. 60(6), pages 704-719.
    14. Saroj Bhattarai & Jae Won Lee & Woong Yong Park, 2016. "Policy Regimes, Policy Shifts, and U.S. Business Cycles," The Review of Economics and Statistics, MIT Press, vol. 98(5), pages 968-983, December.
    15. 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, August.
    16. Pinter, Gabor, 2018. "Macroeconomic shocks and risk premia," LSE Research Online Documents on Economics 90370, London School of Economics and Political Science, LSE Library.
    17. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 57-140, Elsevier.
    18. Meyer-Gohde, Alexander & Neuhoff, Daniel, 2015. "Solving and estimating linearized DSGE models with VARMA shock processes and filtered data," Economics Letters, Elsevier, vol. 133(C), pages 89-91.
    19. Corbo, Vesna & Strid, Ingvar, 2020. "MAJA: A two-region DSGE model for Sweden and its main trading partners," Working Paper Series 391, Sveriges Riksbank (Central Bank of Sweden).
    20. Tan, Fei & Walker, Todd B., 2015. "Solving generalized multivariate linear rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 60(C), pages 95-111.
    21. Çekin, Semih Emre & Ivashchenko, Sergey & Gupta, Rangan & Lee, Chien-Chiang, 2024. "Real-time forecast of DSGE models with time-varying volatility in GARCH form," International Review of Financial Analysis, Elsevier, vol. 93(C).
    22. Nikolaos Kokonas & Paulo Santos Monteiro, 2020. "The Ins and Outs of Unemployment in General Equilibrium," Discussion Papers 2014, Centre for Macroeconomics (CFM).
    23. 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.
    24. Chollete, Loran & Ismailescu, Iuliana & Lu, Ching-Chih, 2014. "Dependence between Extreme Events in the Real and Financial Sectors," UiS Working Papers in Economics and Finance 2014/12, University of Stavanger.

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    JEL classification:

    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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