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Unveiling endogeneity and temporal dependence between tourism revenues/expenditures and macroeconomic variables in Brazil: A stochastic hidden Markov model approach

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Listed:
  • Peter Wanke

    (Federal University of Rio de Janeiro, Brazil)

  • Otávio Henrique dos Santos Figueiredo

    (Federal University of Rio de Janeiro, Brazil)

  • Jorge Junio Moreira Antunes

    (Federal University of Rio de Janeiro, Brazil)

Abstract

Thus far, a comprehensive analysis on the feedback processes that may exist between macroeconomic variables and tourism activity in Brazil is still missing. This article fills this literature gap by analyzing the endogenous and temporally dependent pattern between Brazilian monthly tourism revenue/expenditures and macroeconomic variables over 20 years. A novel stochastic hidden Markov model approach reveals the feedback processes that exist between them. While tourism revenues are autocorrelated and impacted by gross domestic product (GDP) growth, tourism expenditures are detached from any macroeconomic variable, but are rather directly dependent on tourism revenues past performance, which also exert an impact on exchange rates and GDP growth, thus indirectly benefiting tourism expenditures abroad. Policy implications in terms of a specific tourism exchange rate for Brazil are derived in order to sustain tourism expenditures apart from tourism revenue flows.

Suggested Citation

  • Peter Wanke & Otávio Henrique dos Santos Figueiredo & Jorge Junio Moreira Antunes, 2019. "Unveiling endogeneity and temporal dependence between tourism revenues/expenditures and macroeconomic variables in Brazil: A stochastic hidden Markov model approach," Tourism Economics, , vol. 25(1), pages 3-21, February.
    • Handle: RePEc:sae:toueco:v:25:y:2019:i:1:p:3-21
    DOI: 10.1177/1354816618787578
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    References listed on IDEAS

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    Cited by:

    1. Yong Tan & Peter Wanke & Jorge Antunes & Ali Emrouznejad, 2021. "Unveiling endogeneity between competition and efficiency in Chinese banks: a two-stage network DEA and regression analysis," Annals of Operations Research, Springer, vol. 306(1), pages 131-171, November.

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