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, Tamat Sarmidi, Fathin Faizah Said

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)17-30
Number of pages14
JournalRegional Science Inquiry
Volume10
Issue number2
Publication statusPublished - 1 Jan 2018

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money
innovation
demand
airborne thematic mapper
estimation method
credit
inclusion
regression
effect
Predictive power
Money demand
Demand for money
Financial innovation
Asynchronous transfer mode
Benchmark
Inclusion
Credit cards
Regression model
Coefficients
Payment

Keywords

  • Dynamic OLS
  • Financial innovations
  • Forecast
  • Fully modified OLS
  • Money demand

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Development
  • Sociology and Political Science
  • Economics and Econometrics

Cite this

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