Chinese Money Demand Equation

References

Gang Yi, "Towards Estimating the Demand for Money in China," Economics of Planning 26 (1993), 243-270. (Paper)

Variables

Models

Two models (annual and quarter) based on three price deflators are estimated and evaluated in the paper.

Annual Model

ln(m) = b0 + b1ln(y) + b2ln(UP) + b3D + e

where m is M2 in real per capita term; y is real per capita income; D is a dummy variable for representing the reform period since 1979; UP is the percentage of urban population (an indicator of monetarization).

Quarterly Model

ln(m) = b0 + b1ln(rs) + b2p + b3ln(UP) + e

where m is M2 in real per capita term; rs is retail sales in real per capita term (a proxy for income variable); p is the expected inflation rate (defined as the actual inflation rate in the previous period); UP is the percentage of urban population (an indicator of monetarization).

Homework

Part I

  1. Based on the price deflator using mixed price index, duplicate (as close as possible) Yi's empirical results of both annual and quarterly money demand equations.

  2. Formulate and estimate the Box-Cox specification of the quarterly model:

    m(q) = a0 + a1 rs(l) + a2 exp(p)(l) + a3 UP(l) + e

    Note that the exponential form of p (i.e. exp(p)) is used in the regression before applying the Box-Cox transformation to the variable in order to avoid taking a power of negative values of p. Test and justify that the Yi's equation is a special case of the Box-Cox model in which both q, l approach 0.

Part II

  1. Is the annual money demand equation described above, or:

    ln(m) = b0 + b1ln(y) + b2ln(UP) + b3D + e

    an adequate (non-spurious) "long-run" model? Answer this question according to the following steps:

  2. Based on the reciprocal-log specification of the quarterly money demand equation derived from Box-Cox variable transformation:

    m-1 = (1-a0) - a1 ln(rs) - a2 p - a3 ln(UP) + e

    We assume the first-order autocorrelated model errors in mean (AR(1), MA(1), ARMA(1,1)) and in variance (ARCH(1), GARCH(1,1), ARCH-M(1)), respectively.

    (1). Estimate and evaluate all the six estimated error structures in details. (2). Present and interpret the most preferred model.


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(last updated: April 26, 2006)