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The business cycle in Brazil: identification via heteroskedasticity

Author

Listed:
  • Thiago Drummond de Mendonça Giudici

    (Rio de Janeiro State University (UERJ))

  • Elcyon Caiado Rocha Lima

    (Rio de Janeiro State University (UERJ))

Abstract

This article analyzes the Brazilian business cycle from Jan 2000 to February 2020 using a structural vector autoregression (SVAR) model. In Brazilian literature, articles aiming to obtain stylized facts using SVAR models adopt controversial identification hypotheses. The identification via heteroskedasticity emerges as an alternative, eliminating the need for such restrictions. Despite the limited sample size of Brazilian data, we exogenously select regimes with sufficient changes in the variance of the residuals over time. This allows us to identify a SVAR model via heteroskedasticity, as proposed by Brunnermeier et al. (Am Econ Rev 111(6):1845–79, 2021), using a different set of macro variables. The results from this approach are similar to those of a model identified via sign restrictions, providing support for the economic theory used by the latter to identify the shocks. The agnostic approach of identification via heteroskedasticity has enabled the identification of a new shock. We interpret it as a “pessimism” shock, particularly related to future expectations about economic activity.

Suggested Citation

  • Thiago Drummond de Mendonça Giudici & Elcyon Caiado Rocha Lima, 2024. "The business cycle in Brazil: identification via heteroskedasticity," International Economics and Economic Policy, Springer, vol. 21(3), pages 649-684, July.
    • Handle: RePEc:kap:iecepo:v:21:y:2024:i:3:d:10.1007_s10368-024-00615-x
    DOI: 10.1007/s10368-024-00615-x
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    1. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    2. Markku Lanne & Helmut Lütkepohl, 2008. "Identifying Monetary Policy Shocks via Changes in Volatility," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(6), pages 1131-1149, September.
    3. José De Gregorio, 2012. "Commodity Prices, Monetary Policy, and Inflation†," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 60(4), pages 600-633, December.
    4. Marcos Stockl & Ricardo Ramalhete Moreira & Ana Carolina Giuberti, 2017. "O impacto das commodities sobre a dinâmica da inflação no Brasil e o papel amortecedor do câmbio: evidências para o CRB Índex e Índice de Commodities Brasil [The impact of commodities on Brazilian inf," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 27(1), pages 173-207, January-A.
    5. Hutchison, Michael & Walsh, Carl E., 1992. "Empirical evidence on the insulation properties of fixed and flexible exchange rates : The Japanese experience," Journal of International Economics, Elsevier, vol. 32(3-4), pages 241-263, May.
    6. Sims, Christopher A & Uhlig, Harald, 1991. "Understanding Unit Rooters: A Helicopter Tour," Econometrica, Econometric Society, vol. 59(6), pages 1591-1599, November.
    7. José de Gregorio, 2012. "Commodity Prices, Monetary Policy and Inflation," Working Papers wp359, University of Chile, Department of Economics.
    8. Lanne, Markku & Lütkepohl, Helmut, 2010. "Structural Vector Autoregressions With Nonnormal Residuals," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 159-168.
    9. Cooley, Thomas F. & Leroy, Stephen F., 1985. "Atheoretical macroeconometrics: A critique," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 283-308, November.
    10. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    11. 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.
    12. Lütkepohl, Helmut & Woźniak, Tomasz, 2020. "Bayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    13. Christiane Baumeister & James D. Hamilton, 2015. "Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information," Econometrica, Econometric Society, vol. 83(5), pages 1963-1999, September.
    14. Gordon, David B & Leeper, Eric M, 1994. "The Dynamic Impacts of Monetary Policy: An Exercise in Tentative Identification," Journal of Political Economy, University of Chicago Press, vol. 102(6), pages 1228-1247, December.
    15. Rocha Lima, Elcyon Caiado & Martinez, Thiago Sevilhano & Cerqueira, Vinícius Santos, 2018. "Monetary Policy and Exchange Rate: Effects on Disaggregated Prices in a FAVAR Model for Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 38(1), May.
    16. Guido Turnip, 2017. "Identification of Small Open Economy SVARs via Markov-Switching Heteroskedasticity," The Economic Record, The Economic Society of Australia, vol. 93(302), pages 465-483, September.
    17. Lanne, Markku & Lütkepohl, Helmut & Maciejowska, Katarzyna, 2010. "Structural vector autoregressions with Markov switching," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 121-131, February.
    18. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
    19. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    20. Markus Brunnermeier & Darius Palia & Karthik A. Sastry & Christopher A. Sims, 2021. "Feedbacks: Financial Markets and Economic Activity," American Economic Review, American Economic Association, vol. 111(6), pages 1845-1879, June.
    21. Jonas E. Arias & Juan F. Rubio‐Ramírez & Daniel F. Waggoner, 2018. "Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications," Econometrica, Econometric Society, vol. 86(2), pages 685-720, March.
    22. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    23. Sylvia Fruhwirth-Schnatter, 2004. "Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 143-167, June.
    24. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, November.
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    More about this item

    Keywords

    Structural vector autoregression; Identification via heteroskedasticity; Macroeconomic shocks; Business cycle;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E40 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - General
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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