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Linkages between financial and macroeconomic indicators in emerging markets and developing economies

Author

Listed:
  • Biswas, Rita
  • Loungani, Prakash
  • Liang, Zhongwen
  • Michaelides, Michael

Abstract

This paper provides empirical evidence on the finance-growth nexus, making key contributions by focusing on previously understudied Emerging Markets and Developing Economies (EMDEs) and employing mixed-frequency data. Utilizing panel forecasting models for 50 countries from 1990 to 2019, we examine the empirical link between macroeconomic indicators (e.g., aggregate production) and financial indicators (e.g., stock market indexes). Our results support the notion that financial indicators can indeed serve as robust predictors of macroeconomic indicators. Further, the use of mixed data sampling (MIDAS) models enhances the results, confirming the presence of valuable predictive information in higher-frequency data, even for lower-income countries. These findings bear particular significance for policymakers and investors, given the persistent challenge of accessing timely and reliable data on real indicators in EMDEs.

Suggested Citation

  • Biswas, Rita & Loungani, Prakash & Liang, Zhongwen & Michaelides, Michael, 2024. "Linkages between financial and macroeconomic indicators in emerging markets and developing economies," Global Finance Journal, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:glofin:v:62:y:2024:i:c:s1044028324000796
    DOI: 10.1016/j.gfj.2024.101007
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    More about this item

    Keywords

    Finance-growth nexus; Macrofinance; MIDAS; EMDEs; Financial indicators; Predicting macroeconomic indicators;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

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