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Modelling the demand for money in New Zealand

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  • Choi, Daniel
  • Oxley, Les

Abstract

The paper reports on the results of estimating both the long- and short-run demand for money function in New Zealand, 1990–2000 using quarterly data and cointegration- and error-correction-based models. It is found that price, real income and interest rate variables are integrated of order 1 or I(1). Using Phillips and Hansen [Rev. Econ. Stud. 57 (1990) 99] fully modified estimation methods, we establish the existence of a long-run cointegrating relationship among these three variables. Using the residuals from this model to represent the error-correction mechanism (ECM) term, we identify a short-run model utilising Hendry’s general-to-specific (GTS) approach. The model is shown to satisfy the typical diagnostic requirements of a multiple regression model. Three event dummies are used to capture key events of relevance to monetary policy in New Zealand.

Suggested Citation

  • Choi, Daniel & Oxley, Les, 2004. "Modelling the demand for money in New Zealand," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(1), pages 185-191.
    • Handle: RePEc:eee:matcom:v:64:y:2004:i:1:p:185-191
    DOI: 10.1016/S0378-4754(03)00131-9
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    References listed on IDEAS

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    2. Yannick Roussel & Amjad Ali & Marc Audi, 2021. "Measuring The Money Demand In Pakistan: A Time Series Analysis," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 10(1), pages 27-41, March.
    3. Akhand Akhtar Hossain, 2015. "The Evolution of Central Banking and Monetary Policy in the Asia-Pacific," Books, Edward Elgar Publishing, number 14611.
    4. Saten Kumar & Don J. Webber, 2013. "Australasian money demand stability: application of structural break tests," Applied Economics, Taylor & Francis Journals, vol. 45(8), pages 1011-1025, March.
    5. Kumar, Saten & Rao, B. Bhaskara, 2012. "Error-correction based panel estimates of the demand for money of selected Asian countries with the extreme bounds analysis," Economic Modelling, Elsevier, vol. 29(4), pages 1181-1188.

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