Estimation of the Money Demand Function in Russia

Kirill, A Sosunov

doi:10.2139/ssrn.2191941

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…More recent publications on the topic include Drake and Chrystal (1997), Serletis and Gogas (2014), Dahalan et al (2005), and Belongia and Ireland (2019). Furthermore, the existing research on money demand in Russia is limited to the simple-sum monetary aggregates, creating space for improvement (Korhonen and Mehrotra, 2010;Mehrotra and Ponomarenko, 2010;Sosunov, 2013;Krupkina and Ponomarenko, 2013;Gilenko, 2018).…”

Section: Model Specification and Estimation Strategymentioning

confidence: 99%

Divisia Monetary Aggregates for Russia: Money Demand, GDP Nowcasting, and the Price Puzzle

El‐Shagi

Tochkov

2021

SSRN Journal

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The lack of developed financial markets and well-functioning transmission channels assigns monetary aggregates in emerging economies the potential role of nominal anchor, intermediate target, or informational variable for monetary policy. The effectiveness of this approach relies crucially on the correct measurement of money, which is not fulfilled by the conventional index based on the simple sum of financial assets. This paper calculates alternative Divisia monetary aggregates for Russia over the period 1998-2019, which account for the level of liquidity of a given monetary asset by assigning weights according to the usefulness of that asset for transaction services. Divisia is found to follow a growth pattern markedly different from the simple sum, whereby deviations between the two series are even more pronounced when foreign-currency accounts are included. We conduct three empirical exercises to demonstrate the advantages of Divisia over the simple sum. Divisia confirms the stability of the money demand function and reflects portfolio shifts in response to changes in the opportunity cost of money. Divisia-based GDP nowcasting performs better in times of financial turmoil than the simple sum. Lastly, Divisia mitigates the price puzzle phenomenon relative to the conventional measure. We conclude that Divisia monetary aggregates would improve the effectiveness of monetary policy in Russia.

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Section: Model Specification and Estimation Strategymentioning

confidence: 99%

Divisia Monetary Aggregates for Russia: Money Demand, GDP Nowcasting, and the Price Puzzle

El‐Shagi

Tochkov

2021

SSRN Journal

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“…A relationship between money circulation and price level is established, in one form or another, in classical papers on the quantitative theory of money [14,17,18,20,26] and in modern works [5,8,10,12,19,27,29,31,33]. The papers [3,32] also provide an overview and analysis of studies of this issue by the example of Asian, African and other countries.…”

Section: Variables Of the Model For Forecastingmentioning

confidence: 99%

Forecasting the Amount of Cash in Circulation Based on a Regression Model with Lagged Variables

Batova

2020

Wseas Transactions on Business and Economics

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Despite the rather positive dynamics in developing the non-cash payment infrastructure, cash in Russia has been and remains the widespread instrument of payment. The purpose of this paper is the development of a model for forecasting the amount of cash in circulation in the country, namely the value of the monetary aggregate M0, by the example of the Russian Federation. At the same time, the objectives were set so that the resulting model should be suitable for a sufficiently accurate rapid estimation of М0 value, should be easy to use, and should not be overloaded with many variables. The author succeeded in achieving these purposes in the research using a formal approach based on a model with lagged variables, autoregression and time series analysis, also using numerical methods. The lagged variables used were the inflation rate and the value of the monetary aggregate for a previous period. The high quality, accuracy and forecasting power of the model are substantiated. The average approximation error of the model did not exceed 5%.Testing of the model using statistical data of the current year showed high accuracy of forecasting. Statistical data of official sources of the Russian Federation were used for the model development

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“…Gregory–Hansen cointegration test and residual analysis of ECM equation test provided results that do not support stable money demand function. Sosunov (2013) evaluated the money demand function for Russia using money stock/price as a dependent variable and income and interest rate as independent variables for time period from 2003 to 2013. According to the results, money demand is stable in both the long run and short run.…”

Section: Review Of Literaturementioning

confidence: 99%

Financial Development and Money Demand Function: Cointegration, Causality and Variance Decomposition Analysis for Pakistan

Ahad

2017

Global Business Review

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This study has investigated money demand function incorporating financial development, industrial production, income and exchange rate over the period of 1972-2012 for Pakistan. The newly introduced cointegration approach (Bayer-Hanck combined cointegration) and Johansen cointegration approach have been used to test cointegration among variables. The Vector Error Correction Model (VECM) model has applied to explain the direction of causality in the long run and short run. The Unit root problem has been tested by ADF and PP unit root tests. The results reveal that long run relationship exists between money demand, financial development, income, industrial production and exchange rate. Financial development is the main factor to determine the money demand function in both long and short run. The results indicate that feedback effect is found between financial development and money demand.

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Estimation of the Money Demand Function in Russia

Cited by 4 publications

References 14 publications

Divisia Monetary Aggregates for Russia: Money Demand, GDP Nowcasting, and the Price Puzzle

Divisia Monetary Aggregates for Russia: Money Demand, GDP Nowcasting, and the Price Puzzle

Forecasting the Amount of Cash in Circulation Based on a Regression Model with Lagged Variables

Financial Development and Money Demand Function: Cointegration, Causality and Variance Decomposition Analysis for Pakistan

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