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The Demand for Money in China: A Reassessment Using the Bounds Testing Approach

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  • Lee, Chien Chiang

    (Department of Finance, National Sun Yat-sen University Kaohsiung, Taiwan.)

  • Chang, Chun Ping

    (Department of Marketing Management, Shih Chien University Kaohsiung Campus, Kaohsiung, Taiwan.)

Abstract

This paper investigates the demand for money in China using annual data covering 1977-2006. To this end, we apply a newly-developed bounds testing technique to overcome the inherent limitations in testing for unit roots prior to testing for the existence of a level relationship between a dependent variable and a set of regressors. Our results clearly identify the long-run money demand relationship among real narrow money (or real broad money), real income, and nominal interest rates for China. The estimated long-run income elasticity and interest semi-elasticity are, respectively, 0.884 (0.915) and -0.034 (-0.002), using the real M1 (M2) equation. Our estimates of the long-run elasticity are consistent with previous studies, but they are towards the lower end of existing estimates. The results of the parameter stability test reveal that both M1 and M2 money demand are stable for China.

Suggested Citation

  • Lee, Chien Chiang & Chang, Chun Ping, 2012. "The Demand for Money in China: A Reassessment Using the Bounds Testing Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 74-94, March.
  • Handle: RePEc:rjr:romjef:v::y:2012:i:1:p:74-94
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    More about this item

    Keywords

    Money demand; China; ARDL bounds test; CUSUM test;
    All these keywords.

    JEL classification:

    • E41 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Demand for Money
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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