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We would like to thank, without implicating, David Goldsbrough, Steven Dunaway, Charles Kramer, and participants in a WHD Informal Seminar for their helpful comments and suggestions. Also, we would like to thank Ben Hunt for his guidance and assistance with the simulations in Multimod, Tulio Vera, from Merrill Lynch, for providing us with the country data on sovereign spreads; IMF desk economists for other country-specific data; and Victor Culiuc for his research assistance. Any errors are our own.
Calvo, Leiderman, and Reinhart (1993) note that flows to Latin America, and developing countries in general during the early 1990s, were triggered by “...falling interest rates, a continuing recession, and balance of payments developments in the United States...”
Cline and Barnes (1997) pointed out in addition that falling U.S. interest rates are generally associated with an abundance of capital in international markets, which tends to drive down yields.
See International Capital Markets Report (1995–96) and Private Market Financing for Developing Countries Report (1995) for a more detailed analysis and account of events.
It is also evident, however, that changes in this spread may not necessarily fully reflect expected changes in the stance of U.S. monetary policy, as was demonstrated during the Asian crisis and, to some extent, during the events associated with the default by Russia and the near demise of LTCM during the second half of 1998. Kamin and Kleist (1999) and Cline and Barnes (1997) use short-term interest rates as a proxy for global liquidity conditions. Eichengreen and Mody (1998) use the yield on the ten-year U.S. Treasury as a proxy for global economic conditions.
The alternative proxy suggested by Hardouvelis and Theodossiou, the standard deviation of the daily spread within a month, is not presented in the paper because it was not statistically significant in most equations, except in those for Argentina, Bulgaria, and Indonesia.
Notwithstanding these shortcomings, the autocorrelation coefficient is not highly persistent, as it declines to almost zero at the fourth lag.
When using the six-month moving average proxy for market volatility, the econometric estimates show that this variable is highly significant across countries.
We did not believe panel data estimation would have been more efficient than the chosen procedure. As the results show, homogeneity in the estimated parameters is highly rejected, as parameters differ significantly across countries and even within regions. With a relatively small number of countries and a large number of observations, it is more efficient to estimate the model for each country separately rather than impose some form of homogeneity through panel data estimation. In addition, panel data estimation would have severely restricted the sample period, given that data for most Asian countries was available starting only in 1997.
An alternative proxy for U.S. monetary policy is the federal funds futures rate. In using the target (spot) rate, we thought that market expectations of the federal funds rate would be reflected in the spot yield on the three-month Treasury bill, and as a result our proxy for market volatility would indirectly capture expectations about U.S. monetary policy.
Needless to say, the rise in the level of emerging market interest rates will not necessarily be as large as the sum of the rise in spreads and the rise in the U.S. federal funds rate. In the United States, the yield curve tends to flatten as monetary policy is tightened, so that a rise in short-term interest rates is usually not fully passed through to longer-term rates.
The results for Korea, Thailand, and Indonesia, especially the size of the U.S. interest rate elasticity, should be interpreted with some caution due to the relatively small sample size and the fact that the estimation mainly covers the period of an IMF arrangement. In the case of Poland, the model did not include any measure of indebtedness due to the lack of a time series from 1994, and as a result, may be biasing upwards the coefficient of the U.S. federal funds rate.
See International Capital Markets Report, September 1996, p. 16.
The results are available upon request.
See Laxton et al. (1998) for a discussion of Multimod, and Laxton and Prasad (2000) for a Multimod-based analysis of the effects of macroeconomic shocks in the United States on major industrial countries.
Goldfajn and Baig (1999), for example, find for selected countries that rises in U.S. interest rates tended to reduce capital flows. They interpret this as evidence that “push” rather than “pull” factors determine capital flows.
The ten-year period was chosen so as to allow an assessment of the medium-term effects. The size of the rate increase (100 basis points) is sufficiently representative because Multimod does not have significant nonlinearities regarding the effects of U.S. interest rate increases on developing countries. Alternative simulations with interest rate increases of 200 and 300 basis points suggest that the effects on macroeconomic variables in developing countries are roughly two and three times as large, respectively, as in the 100 basis point case (Table 3).
Because interest rates are assumed in Multimod to affect real activity and debt service with a lag, the effects of higher interest rates are larger over the medium term than immediately upon impact.
The increase in the trade balance for these countries relative to the baseline would be offset by higher interest payments, leaving the current account balance roughly unchanged.