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Modeling and Forecasting the Metical-Rand Exchange Rate


  • Samuel Zita
  • Rangan Gupta


This paper investigates the ability of the Dornbusch (1976) sticky-price model for the nominal metical-rand exchange rate, over the period 1994:1-2005:4 in explaining the exchange rate movements of Mozambique. Based on the model, the authors find that there is a stable relationship between the exchange rate and the fundamentals. Gross domestic product and inflation differentials between Mozambique and South Africa play the major role in explaining the metical-rand exchange rate. However, when the Dornbusch (1976) model is re-estimated over the period of 1994:1-2003:4, and the out-of-sample forecast errors are compared with the atheoretical, Classical and Bayesian variants of the Vector Autoregressive (VAR) and Vector Error Correction (VEC) models, and models capturing alternative forms of the Efficient Market Hypothesis (EMH) of exchange rates, the sticky-price model performs poorer. Overall, the Bayesian VEC models (BVECMs) with relatively tight priors, are best suited for forecasting the metical-rand exchange rate, both in terms of out-of-sample forecasting and predicting turning points.

Suggested Citation

  • Samuel Zita & Rangan Gupta, 2008. "Modeling and Forecasting the Metical-Rand Exchange Rate," The IUP Journal of Monetary Economics, IUP Publications, vol. 0(4), pages 63-90, November.
  • Handle: RePEc:icf:icfjmo:v:06:y:2008:i:4:p:63-90

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    References listed on IDEAS

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    Cited by:

    1. Mehmet Balcilar & Rangan Gupta & Clement Kyei & Mark E. Wohar, 2016. "Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test," Open Economies Review, Springer, vol. 27(2), pages 229-250, April.
    2. Riane de Bruyn & Rangan Gupta & Renee van Eyden, 2013. "Forecasting The Rand-Dollar And Rand-Pound Exchange Rates Using Dynamic Model Averaging," Working Papers 201307, University of Pretoria, Department of Economics.
    3. Rangan Gupta & Sonali Das, 2010. "Predicting Downturns in the US Housing Market: A Bayesian Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 41(3), pages 294-319, October.
    4. repec:mes:emfitr:v:51:y:2015:i:3:p:502-524 is not listed on IDEAS
    5. Hyeyoen Kim & Doojin Ryu, 2013. "Forecasting Exchange Rate from Combination Taylor Rule Fundamental," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S4), pages 81-92, September.
    6. Riané de Bruyn & Rangan Gupta & Reneé van Eyden, 2015. "Can We Beat the Random-Walk Model for the South African Rand--U.S. Dollar and South African Rand--UK Pound Exchange Rates? Evidence from Dynamic Model Averaging," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(3), pages 502-524, May.

    More about this item

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications


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