74th International Atlantic Economic Conference

October 04 - 07, 2012 | Montréal, Canada

Modelling short-run money demand by cumulative error correction models

Friday, October 5, 2012: 9:40 AM
Marcus Scheiblecker, Dr. , Macro economics, Austrian Institute of Economic Research, Vienna, Austria
Modelling short-run money demand by cumulative error correction models

Marcus Scheiblecker a

a Austrian Institute of Economic Research, Arsenal Objekt 20, 1030 Vienna, Austria, marcus.scheiblecker@wifo.ac.at

Nowadays, modeling long-term money demand is largely unambiguous. There is a vast amount of empirical evidence concerning a cointegrating relationship between money demand, some kind of interest rate and income (see e.g. Hofman and Rasche, 1991, or Stock and Watson, 1993). In contrast to this, short-run dynamics are still opaque. In the existing literature, the return to steady state is modeled quite differently. Simple error correction models have failed in some cases to explain short-run dynamics adequately. Partial-adjustment models - as used by Goldfeld (1973) or Ball (2002) - allow for a smooth return to equilibrium as costs for adjusting real money balances lead to a sticky behavior of actual money. Other authors like Teräsvirta and Eliasson (2001), Escribano (2004) or Chen and Wu (2005) model the return to steady state in a non-linear error correction form, instead.

All these models consider disequilibria by the gap between money demand and its steady state of only the last period, disregarding disequilibria in periods before. Ignoring deviations from steady state occurred further in the past miss to account for money stockpiling activities of economic agents. Positive deviations from steady state of past periods can be accrued and used for transaction or saving purposes in the current period without making necessary to balance the deviation of the most recent past.

Granger and Lee (1989) presented a model where past deviations from steady state are summed up with equal weights till the starting period of the time series. The new series emerging from this accumulation form a further cointegrating relationship called multicointegration.  Instead, I use a model where weights for cumulation are geometrically decreasing the more they are located in the past. According to Koyck (1954) such models possess an ARMA (1,1) representation. The combination of the Koyck-model with the error correction approach leads to an ARMAX model which is shown to be capable in some cases to track money demand short-run dynamics better and more parsimony than partial –adjustment models or non-linear specifications.