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Dissecting Brazilian agriculture business cycles in high-dimensional and time-irregular span contexts

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  • André Nunes Maranhão

    (São Paulo School of Economics, FGV
    Credit Directory, Bank of Brazil, BB)

  • Nicole Rennó Castro

    (University of São Paulo (USP))

Abstract

Business cycle analysis faces the challenge of high-dimensional databases with a time-irregular span. This study addresses these issues to estimate the Brazilian agricultural business cycle by proposing the use of an entropic test of relative information and the estimation of a generalized dynamic factor model in the context of a time-irregular span. In addition, we assess the co-movements between the estimated cycle and a wide range of weather, macroeconomic, and sectoral variables. The main results are: (i) the sharpest crises of the agricultural cycle are associated more with weather events than with Brazil’s economic crises, reinforcing that agriculture has a stabilizing role in the aggregated cycles; (ii) among all the variables analyzed, those related to weather (minimum and maximum temperature and precipitation) present the greatest commonalities with the agricultural cycle and are leading indicators; (iii) minimum temperatures and precipitation in states are essentially pro-cyclical, while maximum temperatures are pro-cyclical in typically colder states and counter-cyclical in warmer states; (iv) following the weather variables, credit variables had the highest average commonalities, with phases of expansion in the agricultural cycle leading to contractions in the indicators of arrears and defaults and in the interest rate of rural credit, with some time lag; (v) the behavior of the Brazilian economy and global demand for imports are also antecedent and pro-cyclical, but with relatively smaller commonalities; and (vi) expansions of the agricultural cycle lead to contractions in the unemployment rate of the economy. The contribution of this study is twofold: (1) its methodological innovation and (2) its application to the Brazilian agricultural business cycle, both of which are unprecedented.

Suggested Citation

  • André Nunes Maranhão & Nicole Rennó Castro, 2023. "Dissecting Brazilian agriculture business cycles in high-dimensional and time-irregular span contexts," Empirical Economics, Springer, vol. 65(4), pages 1543-1578, October.
    • Handle: RePEc:spr:empeco:v:65:y:2023:i:4:d:10.1007_s00181-023-02391-0
    DOI: 10.1007/s00181-023-02391-0
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    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    2. Jose Maria Da Rocha & Diego Restuccia, 2006. "The Role of Agriculture in Aggregate Business Cycles," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 9(3), pages 455-482, July.
    3. Stock, James H. & Watson, Mark W., 2014. "Estimating turning points using large data sets," Journal of Econometrics, Elsevier, vol. 178(P2), pages 368-381.
    4. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(6), pages 1113-1141, December.
    5. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2004. "The generalized dynamic factor model consistency and rates," Journal of Econometrics, Elsevier, vol. 119(2), pages 231-255, April.
    6. Alexandre Belloni & Victor Chernozhukov, 2011. "High Dimensional Sparse Econometric Models: An Introduction," Papers 1106.5242, arXiv.org, revised Sep 2011.
    7. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
    8. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    9. Estrella, Arturo & Mishkin, Frederic S., 1997. "The predictive power of the term structure of interest rates in Europe and the United States: Implications for the European Central Bank," European Economic Review, Elsevier, vol. 41(7), pages 1375-1401, July.
    10. Rachel Male, 2011. "Developing Country Business Cycles: Characterizing the Cycle," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(0), pages 20-39, May.
    11. Minella, André, 2003. "Monetary Policy and Inflation in Brazil (1975-2000): A VAR Estimation," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 57(3), July.
    12. Chauvet, Marcelle, 2002. "The Brazilian Business and Growth Cycles," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 56(1), January.
    13. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
    14. Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, vol. 111(471), pages 62-85, May.
    15. Neumeyer, Pablo A. & Perri, Fabrizio, 2005. "Business cycles in emerging economies: the role of interest rates," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 345-380, March.
    16. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
    17. Alvin H. Hansen, 1932. "The Business Cycle and Its Relation to Agriculture," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 14(1), pages 59-67.
    18. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
    19. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
    20. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    21. R. H. Shumway & D. S. Stoffer, 1982. "An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(4), pages 253-264, July.
    22. Louzis, Dimitrios P. & Vouldis, Angelos T. & Metaxas, Vasilios L., 2012. "Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1012-1027.
    23. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference for High-Dimensional Sparse Econometric Models," Papers 1201.0220, arXiv.org.
    24. Millner, Antony & Dietz, Simon, 2015. "Adaptation to climate change and economic growth in developing countries," Environment and Development Economics, Cambridge University Press, vol. 20(3), pages 380-406, June.
    25. Watson, Mark W. & Stock, James H., 2014. "Estimating turning points using large data sets," Scholarly Articles 33192198, Harvard University Department of Economics.
    26. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
    27. Buckle, Robert A. & Kim, Kunhong & Kirkham, Heather & McLellan, Nathan & Sharma, Jarad, 2007. "A structural VAR business cycle model for a volatile small open economy," Economic Modelling, Elsevier, vol. 24(6), pages 990-1017, November.
    28. Millner, Antony & Dietz, Simon, 2015. "Adaptation to climate change and economic growth in developing countries," LSE Research Online Documents on Economics 57863, London School of Economics and Political Science, LSE Library.
    29. Florence Contré & Ian Goldin, 1990. "Agriculture and the Economic Cycle: An Economic and Econometric Analysis with Special Reference to Brazil," OECD Development Centre Working Papers 15, OECD Publishing.
    30. Song, Song & Bickel, Peter J., 2011. "Large vector auto regressions," SFB 649 Discussion Papers 2011-048, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    31. Darrel Cohen, 2001. "Linear data transformations used in economics," Finance and Economics Discussion Series 2001-59, Board of Governors of the Federal Reserve System (U.S.).
    32. Lucas, Robert E, Jr, 1975. "An Equilibrium Model of the Business Cycle," Journal of Political Economy, University of Chicago Press, vol. 83(6), pages 1113-1144, December.
    33. Jan Jacobs & Pieter Otter, 2008. "Determining the Number of Factors and Lag Order in Dynamic Factor Models: A Minimum Entropy Approach," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 385-397.
    34. Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
    35. Fabio Kanczuk, 2004. "Real Interest Rates and Brazilian Business Cycles," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 7(2), pages 436-455, April.
    36. Long, John B, Jr & Plosser, Charles I, 1983. "Real Business Cycles," Journal of Political Economy, University of Chicago Press, vol. 91(1), pages 39-69, February.
    37. Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of Economics.
    38. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    39. Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, vol. 23(3), pages 385-400, December.
    40. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-996, November.
    41. Döhrn, Roland & an de Meulen, Philipp, 2015. "Weather, the Forgotten Factor in Business Cycle Analyses," Ruhr Economic Papers 539, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    42. Barro, Robert J., 1976. "Rational expectations and the role of monetary policy," Journal of Monetary Economics, Elsevier, vol. 2(1), pages 1-32, January.
    43. William D. Nordhaus, 1993. "Reflections on the Economics of Climate Change," Journal of Economic Perspectives, American Economic Association, vol. 7(4), pages 11-25, Fall.
    44. Belloumi, Mounir, 2014. "Investigating the impact of climate change on agricultural production in eastern and southern African countries," AGRODEP working papers 3, International Food Policy Research Institute (IFPRI).
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