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Determinants and Dynamics of Current Account Reversals: An Empirical Analysis

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  • Roman Liesenfeld
  • Guilherme Valle Moura
  • Jean-François Richard

Abstract

We use panel probit models with unobserved heterogeneity, state dependence and serially correlated errors in order to analyse 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. 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. Copyright (c) Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2010.

Suggested Citation

  • Roman Liesenfeld & Guilherme Valle Moura & Jean-François Richard, 2010. "Determinants and Dynamics of Current Account Reversals: An Empirical Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 486-517, August.
  • Handle: RePEc:bla:obuest:v:72:y:2010:i:4:p:486-517
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    Cited by:

    1. 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.
    2. Jean-François Richard, 2015. "Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables," Working Paper 5778, Department of Economics, University of Pittsburgh.
    3. 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.
    4. Martin Bijsterbosch & Tatjana Dahlhaus, 2015. "Key features and determinants of credit-less recoveries," Empirical Economics, Springer, vol. 49(4), pages 1245-1269, December.
    5. Bijsterbosch, Martin & Dahlhaus, Tatjana, 2011. "Determinants of credit-less recoveries," Working Paper Series 1358, European Central Bank.

    More about this item

    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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