Baghestani, Hamid, and Tracy Mott, 1997, “A Cointegration Analysis of the U.S. Money Supply Process,” Journal of Macroeconomics, Vol. 19 (No. 2), pp. 269–83.
Beenstock, Michael, 1989, “The Determinants of the Money Multiplier in the United Kingdom,” Journal of Money, Credit, and Banking, Vol. 21 (No. 4), pp. 464–80.
Brunner, Karl, 1997, “High-powered money and the monetary base,” in: T. Lys (ed.), Monetary Theory and Monetary Policy: The Selected Essays of Karl Brunner (Cheltenham, UK/Northampton, MA: Edward Elgar).
Burger, Albert E., and Robert H. Rasche, 1977, “Revision of the Monetary Base,” Federal Reserve Bank of St. Louis Review, Vol. 59 (No. 7), pp. 13–27.
Celasun, Oya, and Mangal Goswami, 2002, “An Analysis of Money Demand and Inflation in the Islamic Republic of Iran,” IMF Working Paper No. 02/205 (Washington: International Monetary Fund).
Darbha, Gangadhar, 2002, “Testing for long-run stability—an application to money multiplier in India,” Applied Economics Letters, Vol. 9 (No. 1), p. 33–37.
Freeman, Scott, and Finn E. Kydland, 2000, “Monetary Aggregates and Output,” The American Economic Review, Vol. 90 (No. 5), pp. 1125–35.
Frost, Peter A., 1977, “Short-run Fluctuations in the Money Multiplier and Monetary Control”, Journal of Money, Credit and Banking, Vol. 9 (No. 1, part 2), p. 165–81.
Garfinkel, Michelle R., and Daniel L. Thornton, 1991, “The Multiplier Approach to the Money Supply Process: A Precautionary Note,” St. Louis Federal Reserve Bank Review, Vol. 73 (No. 4), pp. 47–64.
Hafer, R.W., and Scott E. Hein, 1984, “Predicting the Money Multiplier: Forecasts from Components and Aggregate Models,” Journal of Monetary Economics, Vol. 14 (No. 3), pp. 375–84
Hasan, Mohammad S., 2001, “The behaviour of the currency-deposit ratio in mainland China,” Applied Financial Economics, Vol. 11, pp. 659–68.
Howard, David H., 1982, “The British Banking System’s Demand for Cash Reserves,” Journal of Monetary Economics, Vol. 9 (No. 1), pp. 21–41.
International Monetary Fund, 2004, “Monetary Policy Implementation at Different Stages of Market Development,” available at www.imf.org/external/np/mfd/2004/eng/102604.htm.
Nachega, Jean-Claude, 2000, “Modeling Broad Money Demand in Rwanda”, in: IMF, Rwanda—Recent Economic Developments, IMF Staff Country Report No. 00/04 (Washington: International Monetary Fund).
Rasche, Robert H., and James M. Johannes, 1987, Controlling the Growth of Monetary Aggregates (Boston: Kluwer Academic Publisher).
Zaki, Mokhlis Y., 1995, “Forecasting the Money Multiplier and the Control of Money Supply in Egypt,” The Journal of Development Studies, Vol. 32 (No. 1), pp. 97–111.
The authors are in the Fiscal Affairs Department and Policy Development and Review Department, respectively. The paper was written when they were in the African Department. Thanks are due to Rodolphe Blavy, Francesco Caramazza, Bernard Laurens, Rodolfo Maino, Kenneth K. Meyers, and Laurean W. Rutayisire for helpful suggestions. Of course, any remaining errors are the authors’ responsibility.
Unfortunately, collection of parallel exchange market data has started only recently.
Agricultural production is estimated per season (approximately every six months) and the estimates are published by the ministry of agriculture. However, this series also has missing entries and some inconsistencies in the methodology used for its compilation.
We limit our analysis in this section to the post-1994 period owing to data limitations and because substantial interest rate controls limit the usefulness of interest rate data prior to 1995.
Rasche and Johannes (1987) show that the expected utility loss from deviations of actual money around its targeted level in an interest rate regime versus a reserve aggregate regime mostly depends on the uncertainty of the estimated interest rate elasticity versus the variance of the multiplier forecasts.
For the definitions of the monetary aggregates see the data section.
While non-bank deposits in the central bank do not, strictly speaking, constitute high-powered money, a large part of “non-bank deposits” are deposits by (money-creating) non-bank financial institutions, most importantly the Union des Banques Populaires Rwandaises (UBPR).
An alternative approach would have been to use the reserve adjustment magnitude (RAM) developed by Brunner and Meltzer (see, for example, in Burger and Rasche, 1977, and Frost, 1977). We regard RAM as overly sophisticated for the present context, because the NBR only very rarely changed the reserve requirement in past years, and our concern here is mostly the short-run predictability of the multipliers. Ex post, the ARIMA results below support this decision, as the longest lag in the preferred model for the r-ratio is an MA(1) term.
The downward spike in early 1997 reflects an extremely abrupt expansion in the monetary base (through net credit to government and net foreign assets at the same time) during that period.
Potential measures include closing the commercial bank accounts at the central bank later in the day, and promoting the interbank market by reducing credit risk through more transparency (for example, an automated book-entry system).
We tried several models and kept those (i) whose coefficients were all significant at the 1 percent level, and (ii) for which the LM and Ljung-Box tests did not reject the null hypothesis of no serial correlation in the residuals. Among the short-listed models, we used the Akaike and Schwarz criterions to guide the selection of the most parsimonous model.
The equations of the restricted and unrestricted regressions are yt = α1fa + (1- α1) fc and yt = α1 + α2fa + α3fc, respectively, where faand fc are the aggregate and component forecasts, respectively.
Many of the coefficients on the components approach forecasts are not significant at the 10 percent level, another indicator of the weak power of the components approach.
The variance proportion indicates how far the variation of the forecast is from the variation of the actual series, while the bias proportion indicates how far the mean of the forecast is from the mean of the actual series.
This also seems to indicate difficulties to convert domestic currency into foreign currency as a form to adjust to excess money supply; in these cases, the adjustment seemed to have been through exchange rate movements.
The null that the first log difference of the GDP deflator (dlgdpdef) has a unit root cannot be rejected at usual significance levels. Building on that, several models using dlgdpdef as a measure of opportunity cost in the cointegrating relationship were tested, some of them with positive results with regard to the economic significance of the coefficients found. However, the goodness of fit was less impressive, in particular for the period after 1998.
Unit-root tests indicate that all the variables considered are I(1). Alternative models combined some of the variables included in equation (7) with various other variables, including end-of-period instead of quarterly average exchange rates, interest rates, a dummy to account for differences in interest rates regimes, and the consideration of the real money demand as a unique variable forcing the coefficient on the price level to unity.
Structural innovations in lerya are assumed to simultaneously affect the innovations in the other variables, while structural innovations in lm1, do not. This ordering seems reasonable as the exchange rate remained fixed during a significant part of the sample period, and, central bank interventions continued after its liberalization.
In the particular case of Rwanda, the failure of nominal money balances to adjust to disequilibrium in the market for real balances could also be related to the existence of dormant accounts in the wake of the genocide.
It is possible that the alphas of the CPI, the exchange rate and M1 are underestimated, as the one of GDP may be overestimated for two reasons. First, a significant part of the variance observed in the GDP series is related to weather-related shocks that are, initially, non-monetary in nature; second, the strong recovery in both real money balances and GDP after the genocide in 1994 may have biased the results.
The same model was estimated using different subsamples (excluding the last year and the last two years of the main sample). The parameters obtained for the cointegrating equation are not statistically different from those in (7) at usual confidence levels. Thus, out of sample forecasts resemble those shown in Figure 4 above.