Some empirical evidence on the demand for money in the Pacific Island countries

Abstract

PurposeThe purpose of this paper is to analyze narrow money demand functions for the Pacific Island countries (PICs) and evaluate their stability. The selected PICs are Fiji, Vanuatu, Samoa (SAM), Solomons and the Papua New Guinea. The stability of the demand for money is vital for the formulation of the monetary policy.Design/methodology/approachThe augmented Dicky‐Fuller method is employed to test the time series properties of the variables. Alternative time series techniques such as general to specific (GETS) and Johansen maximum likelihood (JML) are used with annual data from 1974 to 2004 (except for SAM with data from 1980 to 2004) to estimate the narrow money demand equations. To draw inferences relative to the stability of the parameters, the study applies the cumulative recursive sum of recursive residuals (CUSUM) and the cumulative sum of squares of recursive residuals (CUSUMSQ).FindingsThe results from the time series approaches of GETS and JML suggest that real income, nominal rate of interest and real narrow money are cointegrated. The CUSUM and CUSUMSQ stability test results indicate that the demand for money functions for these countries are stable and, therefore, the respective monetary authorities may consider targeting money supply in their conduct of monetary policy. It is argued that the financial sector reforms and liberalization is yet to have any significant effects on the money demand in the PICs.Research limitations/implicationsThe methods of estimation does not allow for structural breaks in the cointegrating relationship. It is hoped that future research may focus on using the structural break techniques and also investigate the stability of the demand for broad money in the PICs. Further due to limitations in the data, the authors were only able to select five PICs.Originality/valueThis is the first paper in the literature that provides long‐run estimates and stability results of the narrow money demand using the newest time series techniques for a group of PICs over the period 1974‐2004.