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Determinants and dynamics of current account reversals: an empirical analysis

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  • Liesenfeld, Roman
  • Moura, Guilherme V.
  • Richard, Jean-François

Abstract

We use panel probit models with unobserved heterogeneity, state-dependence and serially correlated errors in order to analyze the determinants and the dynamics of current-account reversals for a panel of developing and emerging countries. The likelihood-based inference of these models requires high-dimensional integration for which we use Efficient Importance Sampling (EIS). Our results suggest that current account balance, terms of trades, foreign reserves and concessional debt are important determinants of current-account reversal. Furthermore, we find strong evidence for serial dependence in the occurrence of reversals. While the likelihood criterion suggest that state-dependence and serially correlated errors are essentially observationally equivalent, measures of predictive performance provide support for the hypothesis that the serial dependence is mainly due to serially correlated country-specific shocks related to local political or macroeconomic events.

Suggested Citation

  • Liesenfeld, Roman & Moura, Guilherme V. & Richard, Jean-François, 2009. "Determinants and dynamics of current account reversals: an empirical analysis," Economics Working Papers 2009-04, Christian-Albrechts-University of Kiel, Department of Economics.
    • Handle: RePEc:zbw:cauewp:200904
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    Cited by:

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    2. Martin Bijsterbosch & Tatjana Dahlhaus, 2015. "Key features and determinants of credit-less recoveries," Empirical Economics, Springer, vol. 49(4), pages 1245-1269, December.
    3. Yin-Wong Cheung & Sven Steinkamp & Frank Westermann, 2020. "A Tale of Two Surplus Countries: China and Germany," Open Economies Review, Springer, vol. 31(1), pages 131-158, February.
    4. Theofilakou, Nancy & Stournaras, Yannis, 2012. "Current account adjustments in OECD countries revisited: The role of the fiscal stance," Journal of Policy Modeling, Elsevier, vol. 34(5), pages 719-734.
    5. Farias, Eliene de Sá & Mattos, Leonardo Bornacki de & Vieira, Fabrício de Assis Campos, 2022. "Commodity prices and capital movement phenomena in emerging economies," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), August.
    6. Mesters, G. & Koopman, S.J., 2014. "Generalized dynamic panel data models with random effects for cross-section and time," Journal of Econometrics, Elsevier, vol. 180(2), pages 127-140.
    7. Bijsterbosch, Martin & Dahlhaus, Tatjana, 2011. "Determinants of credit-less recoveries," Working Paper Series 1358, European Central Bank.

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    More about this item

    Keywords

    Panel data; dynamic discrete choice; importance sampling; Monte Carlo integration; state dependence; spillover effects;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • F32 - International Economics - - International Finance - - - Current Account Adjustment; Short-term Capital Movements

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