Staying vigilant of uncertainty to velocity of money: an application for oil‐producing countries

• DOI: http://doi.org/10.1111/opec.12127
• Published date: 01 June 2018
• Author: Afsin Sahin
• Date: 01 June 2018

Staying vigilant of uncertainty to velocity of

money: an application for oil-producing

countries

Afsin Sahin

Department of Banking, School of Banking and Insurance, Ankara Haci Bayram Veli University, 06571

Besevler, Ankara, Turkey. Email: afsinsahin@gmail.com

Abstract

Uncertainty in economic and financial variables has substantially changed the dynamics in

monetary economics during the last two decades in oil-producing countries. However, there are

types of uncertainties which affect these countries heterogeneously in the short run and long run.

For this purpose, Autoregressive Distributed Lag Model (ARDL) is estimated for oil-producing

countries and non-homogenous results for money supply, income, oil, interest rate, US monetary

policy and exchange uncertainties have been obtained.

1. Introduction

Central Banks use several monetary and macro-prudential policy tools to sustain

the stability of output and prices. Therefore, they try to change and control the

attitudes, motives and economic behaviours of transactions. There are central

banks, such as European Central Bank, (ECB) which have announced that they

will benefit from negative interest rates as a policy tool to boost the economy

(Arteta et al., 2016). However, there is an essential part of the dynamics showing

that unexpected events such as shocks to the economy, uncertainties and

volatilities have essential roles in the economy which cannot be fully explained

by monetary policy tools.

The monetary policy tools central banks choose should be appropriate and efficient,

and this is in connection with the stability of the velocity of money. However,

uncertainty in economic variables changes the velocity of money. In this paper, several

economic and financial uncertainty variables are derived and their effects on velocity of

money are analysed using Autoregressive Distributed Lag (ARDL) Model.

1

This method

allows us to investigate stability and long-run equilibrium relationships and to estimate

the short-run dynamics (Pesaran, 1997).

JEL classification: E41, E52, C58.

©2018 Organization of the Petroleum Exporting Countries. Published by John Wiley & Sons Ltd, 9600 Garsington

Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

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Since it is an old topic, economic literature has investigated the relationship between

velocity of money and the other macroeconomic variables, its determinants using several

other methods. Major economic events and regime changes in monetary policy may

change the velocity of money. For instance, during recessions such as Great Depression

of 1929 and Recent Global Crises of 2007, we observed that the velocity of money

shrunk considerably (see Anderson et al., 2016). Moreover, in October 1979, the United

States adopted a new monetary regime by controlling money supply and shifting to

target Nonborrowed Reserves (NBR) during the Volcker period to reduce inflationary

pressures and according to Friedman (1984) this contributed to the slowdown of velocity

during 1980s.

Several studies tested Friedman’s hypothesis to question the role of monetary

uncertainty in the overall economy. Hall and Noble (1987) supported the Friedman’s

hypothesis by Granger causality. Brocato and Smith (1989) support Friedman for the

pre-1979 period but fail to reject Friedman’s hypothesis and explore the controversial

results for the latter period. Mehra (1987, 1989) reminds us to consider specification and

robustness problems in these estimations because of variable forms and the other

variables while interpreting the movements in volatility. Thornton (1995) finds, using the

Granger causality analysis, that Friedman’s hypothesis, that monetary growth volatility

(uncertainty) diminishes income velocity, is not valid for several developed countries.

Beg (1997) estimates a GARCH model for monetary growth and uses conditional

variance as an anticipated and its residuals for an unanticipated component. He claims

that an increase in uncertainty increases the effectiveness of monetary policy but the

anticipated monetary policy does not affect velocity. Holmes (2000) benefits from

principal components analysis and explores the integration properties of EU countries in

terms of their monetary velocity by considering different periods. Tan (2007) supports

Friedman’s hypothesis for Malaysia. Nie (1999) estimates a money demand function for

Iran, Sudan and Pakistan. Altintas (2008) uses ARDL model to estimate money demand

function for Turkey. Baunto et al. (2011) benefit from Granger causality method and

computes M1 volatility by a GARCH model and supports Friedman’s hypothesis.

Ahking (1984) finds that the change in money supply and the change in velocity are not

correlated in the long run. Pinno and Serletis (2016) apply VARMA, GARCH-in-Mean,

BEKK models and explore a positive effect of monetary volatility and stock market

volatility on velocity obtained from narrower Divisia money. Chowdhury (1988) finds

that money growth volatility affects velocity. Payne (1992) uses Granger causality and

finds that money growth uncertainty is beneficial to explain velocity. Darrat and Suliman

(1994), using a VAR model, claim that there is no relationship between money growth

uncertainty and velocity.

Ramsaran (1992) estimates velocity for the Caribbean countries and claims that an

income is the most important variable affecting velocity compared to interest rate and

©2018 Organization of the Petroleum Exporting Countries OPEC Energy Review June 2018

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