Can key interest rates decrease output gaps?


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The difference in the GDP levels is crucial for the macroeconomic forecasting to develop adequate and supportive fiscal and monetary policies. Most mismeasurements under current geoeconomics challenges can be explained by the difficulty in predicting recessions and the overestimation of the economy’s potential capacity. The research aims to consider the GDP gap’s effectiveness for the possible forecasting of the monetary policy, particularly the central bank’s interest rate. The study uses quantitative methods, particularly VAR modeling. The VAR model is chosen as a proven useful tool for describing the dynamic behavior of economic time series and forecasting. The data sample is chosen as Eurozone, the United States, and Japan. The similarity is detected on output gaps implementation in the considered states; however, the variety in the responses to the financial crisis is revealed. This difference is due to the different sensitivity of economies on the impact of monetary instruments. In particular, the Japanese economy has a relatively low level of sensitivity to changes in monetary instruments. In terms of the reactions of central banks to the current economic crisis caused by COVID-19, then due to the global lockdown and the incredible decline in economic activity, almost all countries are in a situation of negative GDP gap according the paper’s approach. However, the measures to mitigate it will vary in different states.

The paper is done in the framework of scientific faculty research 16КF040-04 “Steady-state security assessment: a new framework for analysis” (2016–2021), Taras Shevchenko National University of Kyiv (Ukraine).

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    • Figure 1. Dynamics of GDP indices and key interest rates of the considered countries
    • Figure 2. Dynamics of GDP gaps of the considered countries
    • Figure 3. Dependence of the GDP gap and the dynamics of the key rate in selected countries
    • Figure 4. Impulse functions for the EU
    • Figure 5. Impulse functions for Japan
    • Figure 6. Impulse functions for the USA
    • Table 1. Estimation of a long-term trend
    • Table A1. Variables for the models
    • Table A2. Data set for the models
    • Table A3. The stationarity test
    • Table A4.1. VAR model assessed for the EU
    • Table A4.2. VAR model assessed for Japan
    • Table A4.3. VAR model assessed for the USA
    • Conceptualization
      Andriy Stavytskyy
    • Data curation
      Andriy Stavytskyy, Viktoriia Kulish
    • Formal Analysis
      Andriy Stavytskyy, Ganna Kharlamova
    • Investigation
      Andriy Stavytskyy, Ganna Kharlamova
    • Methodology
      Andriy Stavytskyy
    • Writing – original draft
      Andriy Stavytskyy
    • Project administration
      Ganna Kharlamova, Vincentas Giedraitis
    • Validation
      Ganna Kharlamova, Vincentas Giedraitis
    • Visualization
      Ganna Kharlamova, Valeriy Osetskyi, Viktoriia Kulish
    • Writing – review & editing
      Ganna Kharlamova
    • Supervision
      Valeriy Osetskyi