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An indicator of monetary bias for emerging and partially dollarized economies: The case of Uruguay

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  • Brum-Civelli, Conrado
  • Garcia-Hiernaux, Alfredo

Abstract

The instability of the relationships between interest rates, amount of money, and exchange rate, and the transmission problems between different interest rates hinder the measurement of monetary policy through a single variable. This difficulty is particularly relevant in emerging and partially dollarized economies. This paper proposes a multivariate indicator of monetary bias for these economies in which the monetary and financial variables are considered according to the impact they have on inflation. We use a Factor Augmented Vector AutoRegressive Moving Average model with eXogenous variables (FAVARMAX) to estimate these effects in the case of Uruguay. Using the evolution of Monetary Conditions Index (MCI) proposed, we characterize the policy adopted by the Central Bank of Uruguay between 2010–2019, a period of inflation targeting.

Suggested Citation

  • Brum-Civelli, Conrado & Garcia-Hiernaux, Alfredo, 2023. "An indicator of monetary bias for emerging and partially dollarized economies: The case of Uruguay," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 206-219.
    • Handle: RePEc:eee:reveco:v:85:y:2023:i:c:p:206-219
    DOI: 10.1016/j.iref.2023.01.004
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    More about this item

    Keywords

    Monetary policy bias; Multivariate indicator; Inflation; FAVARMAX;
    All these keywords.

    JEL classification:

    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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