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The Propagation Of Industrial Business Cycles

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  • Camacho, Maximo
  • Leiva-Leon, Danilo

Abstract

This paper examines the evolution of the distribution of industry-specific business cycle linkages, which are modeled through a multivariate Markov-switching model and estimated by Gibbs sampling. Using nonparametric density estimation approaches, we find that the number and location of modes in the distribution of industrial dissimilarities change over the business cycle. There is a relatively stable trimodal pattern during expansionary and recessionary phases characterized by highly, moderately, and lowly synchronized industries. However, during phase changes, the density mass spreads from moderately synchronized industries to lowly synchronized industries. This agrees with a sequential transmission of the industrial business cycle dynamics.

Suggested Citation

  • Camacho, Maximo & Leiva-Leon, Danilo, 2019. "The Propagation Of Industrial Business Cycles," Macroeconomic Dynamics, Cambridge University Press, vol. 23(1), pages 144-177, January.
  • Handle: RePEc:cup:macdyn:v:23:y:2019:i:01:p:144-177_00
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    Cited by:

    1. Maximo Camacho & Fernando Soto, 2018. "Consumer confidence’s boom and bust in Latin America," Working Papers 18/02, BBVA Bank, Economic Research Department.
    2. Lirios Alos-Simo & Antonio J. Verdu-Jover & Jose M. Gomez-Gras, 2020. "Knowledge Transfer in Sustainable Contexts: A Comparative Analysis of Periods of Financial Recession and Expansion," Sustainability, MDPI, Open Access Journal, vol. 12(12), pages 1-24, June.
    3. Amy Y. Guisinger & Michael T. Owyang & Daniel Soques, 2020. "Industrial Connectedness and Business Cycle Comovements," Working Papers 2020-052, Federal Reserve Bank of St. Louis.
    4. María Dolores Gadea-Rivas & Ana Gómez-Loscos & Danilo Leiva-Leon, 2017. "The evolution of regional economic interlinkages in Europe," Working Papers 1705, Banco de España.
    5. Funke, Michael & Leiva-Leon, Danilo & Tsang, Andrew, 2019. "Mapping China’s time-varying house price landscape," Regional Science and Urban Economics, Elsevier, vol. 78(C).
    6. Komla M. Agudze & Monica Billio & Roberto Casarin & Francesco Ravazzolo, 2021. "Markov Switching Panel with Endogenous Synchronization Effects," BEMPS - Bozen Economics & Management Paper Series BEMPS82, Faculty of Economics and Management at the Free University of Bozen.
    7. Pierre Guérin & Danilo Leiva-Leon, 2017. "Monetary policy, stock market and sectoral comovement," Working Papers 1731, Banco de España.
    8. Gadea-Rivas, María Dolores & Gómez-Loscos, Ana & Leiva-Leon, Danilo, 2019. "Increasing linkages among European regions. The role of sectoral composition," Economic Modelling, Elsevier, vol. 80(C), pages 222-243.
    9. Paflioti, Persa & Vitsounis, Thomas K. & Teye, Collins & Bell, Michael G.H. & Tsamourgelis, Ioannis, 2017. "Box dynamics: A sectoral approach to analyse containerized port throughput interdependencies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 396-413.
    10. Ductor, Lorenzo & Leiva-Leon, Danilo, 2016. "Dynamics of global business cycle interdependence," Journal of International Economics, Elsevier, vol. 102(C), pages 110-127.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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