Tag: Money demand

INVESTIGATING THE IMPACT OF ATM AND POS TERMINALS ON MONEY DEMAND IN NINE EUROPEAN COUNTRIES IN THE CONTEXT OF A RANDOM EFFECT MODEL AS THE APPROPRIATE PANEL DATA MODEL

Payam MOHAMMAD ALIHA

Ph.D student, Universiti Kebangsaan Malaysia (UKM), Malaysia

payammaliha@gmail.com

Tamat SARMIDI

Associate Professor Dr. at Faculty of Economics and Management, Universiti Kebangsaan Malaysia (UKM), Malaysia

tamat@ukm.edu.my

Fathin FAIZAH SAID

Associate Professor Dr. at Faculty of Economics and Management, Universiti Kebangsaan Malaysia (UKM), Malaysia

fatin@ukm.edu.my

Abstract

This study investigates the effects of financial innovations on the demand for money using panel data for 9 European countries from 2014 to 2018. Such models assist in controlling for unobserved heterogeneity when this heterogeneity is constant over time and correlated (fixed effects) or uncorrelated (random effects) with independent variables. Hausman test and Breusch and Pagan Lagrangian multiplier test (LM) both indicate that the random effects model is appropriate. We use the conventional money demand that is enriched with the number of automated teller machines (ATM) and the number of point-of-sale (POS) terminals to proxy for the financial innovations. The estimation result of the chosen random effects regression indicate that the elasticity of the demand for real money to POS is about 10 percent meaning that money demand is not elastic with regard to POS. Also, the estimated coefficient of ATM is not significant.

Keywords: EU, money demand, random effects, fixed effects, financial innovation, panel data

JEL classification: C13, C40, C51, E40, E44

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COMPARING THE FORECASTS OF THE DEMAND FOR MONEY IN MALAYSIA WITH THE INCLUSION OF FINANCIAL INNOVATION USING DIFFERENT ESTIMATION METHODS

Payam MOHAMMAD ALIHA

Ph.D candidate, Universiti Kebangsaan Malaysia (UKM), Malaysia

payammaliha@gmail.com

Tamat SARMIDI

Associate Professor Dr. at Faculty of Economics and Management, Universiti Kebangsaan Malaysia (UKM), Malaysia

tamat@ukm.edu.my

Fathin FAIZAH SAID

Dr. at Faculty of Economics and Management, Universiti Kebangsaan Malaysia (UKM), Malaysia

fatin@ukm.edu.my

Abstract

In this paper, we compare the forecasting performance of multivariate models (ARDL/VECM/DOLS/FMOLS) versus univariate models (ARIMA/ETS) for the purpose of forecasting the real demand for money in Malaysia using monthly data during 2010Q1-2018Q4. This study overcomes the issue of misspecification by incorporating financial innovation in the money demand function using separate measures of payment instruments (credit card, charge card, debit card, e-money), payment channels (Real Time Electronics Transfer of Funds and Securities or RENTAS, Interbank GIRO, Financial Process Exchange or FPX and direct debit) and payment channels (Automated Teller Machines or ATM, mobile banking) to capture the effect of financial innovations. The multivariate models which are categorized into structural models (relying on a structural relationship between money demand and other variables) are also cointegration based models meaning that variables have long-run associationship and move together in the long-run while non-structural (non-cointegration) based techniques (ARIMA and ETS model) do not rely on such a structural relationship. We conclude that structural models are better for longer term forecasting. Non-structural models (notably ARIMA) have better forecasting performance for short term horizons such as one year than they do for long term horizons. However, our findings indicate that even for short term horizons, structural models do better than non-structural models but the gap between forecasting accuracy for these two kinds of models is much narrower in the short term horizon compared to long term horizon. The results also indicate that FMOLS has the most predictive power among cointegration/structural/multivariate based models for both short (12-months) and long-time (60-months) horizons. In the context of this model (FMOLS), financial innovation have positive yet small impact on money demand in Malaysia. Finally, we do out-of-sample forecast using FMOLS.

Keywords: Malaysia, Money Demand, Financial Innovations, Multivariate, Univariate, Cointegration

JEL classification: E41, E42, E52
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INVESTIGATING THE EFFECT OF FINANCIAL INNOVATIONS ON THE DEMAND FOR MONEY IN AUSTRALIA USING DOLS AND FMOLS AND COMPARING THEIR PREDICTIVE POWERS

Payam MOHAMMAD ALIHA

Ph.D candidate, Universiti Kebangsaan Malaysia (UKM), Malaysia

payammaliha@gmail.com

Tamat SARMIDI

Associate Professor Dr. at Faculty of Economics and Management, Universiti Kebangsaan Malaysia (UKM), Malaysia

tamat@ukm.edu.my

Fathin FAIZAH SAID

Dr. at Faculty of Economics and Management, Universiti Kebangsaan Malaysia (UKM), Malaysia

fatin@ukm.edu.my

Abstract

In this paper we apply two different estimation methods, namely DOLS and FMOLS to estimate real demand for money in Australia with the inclusion of financial innovations. We use a conventional money demand function that was enriched with a proxy for financial innovations. This sum of the number of cheques, credit cards, charge cards, ATM and direct entry payment was included in the regression model to proxy the effect of financial innovations on the money demand. The results indicate that the estimated coefficient of TPI using DOLS is not significant yet it is highly significant using FMOLS and it bears positive sign so that 1 percent increase in TPI leads to the increase of money demand by 0.24 percent. Also, using “Root Mean Squared Error” as the benchmark for predictive power, we conclude that FMOLS is superior to DOLD when it comes to forecasting.

Keywords: financial innovations, money demand, dynamic OLS, fully modified OLS, forecast

JEL classification: E41, E42, E52
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