Economic Analysis: Theory and Practice
 

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Statistical investigation into relations between the monetary policy tools

Vol. 18, Iss. 1, JANUARY 2019

Received: 7 November 2018

Received in revised form: 17 November 2018

Accepted: 30 November 2018

Available online: 29 January 2019

Subject Heading: MATHEMATICAL METHODS AND MODELS

JEL Classification: Е17, Е47, F30

Pages: 179–196

https://doi.org/10.24891/ea.18.1.179

Mitin I.N. VTB Bank, Moscow, Russian Federation
mitin.ivan@gmail.com

ORCID id: not available

Grachev I.D. Central Economics and Mathematics Institute, RAS, Moscow, Russian Federation
idg19@mail.ru

ORCID id: not available

Nevolin I.V. Central Economics and Mathematics Institute, RAS, Moscow, Russian Federation
i.nevolin@cemi.rssi.ru

ORCID id: not available

Subject The study deals with interrelations between the nominal exchange rate of Russian ruble, the key interest rate and the price of oil.
Objectives The purpose is to verify the hypothesis about coordinated use of instruments of the Central Bank of the Russian Federation, namely the rate of exchange and the key interest rate in changing external environment.
Methods We employ the statistical analysis tools, i.e. evaluation of linear relationship, verification of cointegration of time series of the rate of exchange and oil price. To check the accuracy of the constructed models, we used the Granger causality test, the Dickey–Fuller test (for availability of trend), the Durbin–Watson test statistic and the Jarque-Bera test (for analysis of residuals from a statistical regression), and the Johansen test (for cointegration).
Results We confirmed the link between the exchange rate of Russian ruble and the oil price, revealed a short-term relation between them in cointegration models. The absence of long-term relation between the exchange rate of Russian ruble and the oil price in cointegration models is somewhat unexpected and contradicts the previous studies. It was reasonable to include the key interest rate in the multi-factor model of the exchange rate of Russian ruble along with the oil price in the form of dammy variables. This model allows identifying the periods of more active use of the key interest rate in the Bank of Russia policy.
Conclusions The presented model enables to estimate quantitative relations between the instruments of the Bank of Russia.

Keywords: oil price, interest rate, time series, cointegration

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