چکیده:
his paper examines the impact of modern technology including credit cards, automatic teller machines (ATM) and electronic funds oftransfer at the Point-Of-Sale (POS) on money demand for Iran Usingseasonal data for Iran 2001-2008. For this purpose, money demand function has been estimated on the basis of Rinaldian model (2001) by ARDL approach. Our findings indicate that the long-run impact of modern technology on demand for money is strongly greater than short-run. We have also shown that, as a result of increase in the number ATMs and credit cards, the demand for currency will increase in both short and long runs. Whereas the impact of increase in POS on demand for currency is negative. In addition, the error correction coefficient is 0.49 indicates that49 percent of short-run fluctuations in money demand will be settled in long-run
خلاصه ماشینی:
Considering The Rinaldi model in the time-series method, the following model is recommended for estimating the money demand function in Iran: Logcut = β0 + β1 LogGDPt + β2 LogIt + β3 LogPt + β4 LogArzt + β5 ATMt + β6 POSt + β7Cardt + β8 D2004 + T + εt Where: (1) cut = Real volume of coins and banknotes handled by the people CURR P (where, CURR is volume of coins and banknotes handled, and P, is the price level) GDPt = gross domestic product, in constant price of 1997 (per 1000 people) Pt = inflation rate It = short-run interest rate of bank deposits Arzt = exchange rate, in constant price of 1997 ATMt = number of automatic teller machines (per 1000 active people) POSt = number of point-of-sale terminals (per 1000 active people) Cardt = number of credit cards (per 1000 active people) D2005 = dummy variable of Shetab system which has been used since 2002 in the country's banking system T = trend variable ɛt = disturbance component Hence, the gross domestic product has been considered based on Cambridge approach to transaction money demand; and short-run interest rate, based on Keynes speculation demand for money theory.
Considering Rinaldi's money demand model, number of automatic teller machines, point-of-sale terminals, debit and credit cards are considered as alternative variables of the new electronic payment instruments.
To examine short- and long-run effects of electronic payment instruments on the demand for cash, method of auto regressive distributed lag (ARDL) econometrics is used.