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Correlated disturbances and U.S. business cycles

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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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  • Vasco Curdia & Ricardo Reis, 2010. "Correlated disturbances and U.S. business cycles," Staff Reports 434, Federal Reserve Bank of New York.
    • Handle: RePEc:fip:fednsr:434
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    Cited by:

    1. Giovanni Angelini & Luca Fanelli, 2016. "Misspecification and Expectations Correction in New Keynesian DSGE Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(5), pages 623-649, October.
    2. 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.
    3. 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.
    4. Pinter, Gabor, 2018. "Macroeconomic shocks and risk premia," LSE Research Online Documents on Economics 90370, London School of Economics and Political Science, LSE Library.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    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. Faust, Jon & Gupta, Abhishek, 2010. "Posterior Predictive Analysis for Evaluating DSGE Models," MPRA Paper 26721, University Library of Munich, Germany.
    13. Madeira, João & Palma, Nuno, 2018. "Measuring monetary policy deviations from the Taylor rule," Economics Letters, Elsevier, vol. 168(C), pages 25-27.
    14. 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).
    15. 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.
    16. 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).
    17. 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.
    18. 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).
    19. 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.
    20. Christoffel, Kai & Kilponen, Juha & Jaccard, Ivan, 2011. "Government bond risk premia and the cyclicality of fiscal policy," Working Paper Series 1411, European Central Bank.
    21. Bachmann, Rüdiger & Bayer, Christian, 2013. "‘Wait-and-See’ business cycles?," Journal of Monetary Economics, Elsevier, vol. 60(6), pages 704-719.
    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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    More about this item

    Keywords

    Government spending policy; Business cycles; Bayesian statistical decision theory; Fiscal policy; Vector autoregression; Equilibrium (Economics);
    All these keywords.

    JEL classification:

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

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