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Estimating Madagascar economic growth using the Mixed Data Sampling (MIDAS) approach

Author

Listed:
  • Andrianady, Josué R.
  • Rajaonarison, Njakanasandratra R.
  • Razanajatovo, Yves H.

Abstract

In this document, we introduce a forecasting model for the Gross Domestic Product (GDP) to estimate the economic growth of Madagascar in 2022. Normally, important macroeconomic variables are reported at different frequencies. For instance, GDP and foreign trade figures are typically provided on a quarterly and monthly basis respectively. However, traditional econometric models necessitate data to be harmonized to a common frequency by aggregating at the highest available frequency, which is known as temporal aggregation. Nonetheless, this approach has a disadvantage of losing information. Consequently, we propose the Mixed Data Sampling (MIDAS) method as an alternative.

Suggested Citation

  • Andrianady, Josué R. & Rajaonarison, Njakanasandratra R. & Razanajatovo, Yves H., 2023. "Estimating Madagascar economic growth using the Mixed Data Sampling (MIDAS) approach," MPRA Paper 118267, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:118267
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    References listed on IDEAS

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    1. Michaely, Michael, 1977. "Exports and growth : An empirical investigation," Journal of Development Economics, Elsevier, vol. 4(1), pages 49-53, February.
    2. Konstantins Benkovskis, 2008. "Is there a Bank Lending Channel of Monetary Policy in Latvia? Evidence from Bank Level Data," Working Papers 2008/01, Latvijas Banka.
    3. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    4. Albert K. Tsui & Cheng Yang Xu & Zhaoyong Zhang, 2018. "Macroeconomic forecasting with mixed data sampling frequencies: Evidence from a small open economy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 666-675, September.
    5. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
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    More about this item

    Keywords

    MIDAS; economic growth; Madagascar;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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