“Never argue with the data!”
—Sheen, in “Jimmy Neutron, Boy Genius”
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Independent Evaluation Office, IMF; Ministry of Agriculture, Colombia; and Clemson University, respectively. This paper was prepared while the authors were in the Policy Review Division in the Policy Development and Review Department. The authors thank Tim Lane, Marc Hofstetter, and participants in the 9th Annual Meeting of the Latin American and Caribbean Economic Association (San José, Costa Rica, November 4-6, 2004) for helpful comments and suggestions.
Other work aimed at identifying the ERBS syndrome includes Végh (1992) and Calvo and Végh (1994). Rebelo and Végh (1996) assess quantitatively the explanatory power of several theories put forth to explain the ERBS syndrome.
Following Sargent (1982), the possibility of an abrupt end to hyperinflations at little or no output cost became increasingly accepted. But this would not be the case for non-hyperinflations, where inertia in wages and/or prices would lead to output losses.
Twelve of the 17 de jure ERBS are classified as de facto ERBS.
This excludes transition economies in Eastern Europe; the Baltics and other republics of the former Soviet Union; the Lao People’s Democratic Republic; Vietnam; and Cambodia.
We used year-on-year monthly inflation rates, rather than annualized monthly rates, in order to avoid or dampen seasonal patterns and occasional spikes in monthly inflation rates.
Only a handful of additional episodes were picked up by lowering
As explained later, nominal base money did not fall during the first 12 months in any of the 53 stabilizations studied here. For this reason we did not attempt to identify cases in which the following conditions were met: a declining monetary base; Δr>1; Δc<0; and |Δc| >1.
It is important to stress here that in our sample of stabilizations Case 3 would have to refer to those episodes in which the credit growth/exchange rate mix, although ultimately inconsistent, was able to produce a reduction in inflation that met criteria (i)-(iii). Instances where these this inconsistency led to a currency crisis and a surge in inflation would not have been picked up by our episode selection algorithm.
The procedure described is similar to the one used by Levy-Yeyati and Sturzenegger (2002) to classify exchange rate regimes in a general setting (as opposed to during disinflation episodes, as done here).
The source of the data was in all cases the IMF’s International Financial Statistics (IFS). However, because the latest available versions of IFS contained gaps for some episodes, we filled these gaps with data from older (printed) editions of IFS. It is entirely possible that these data are of lesser quality than that in current versions of IFS and/or that they were compiled using different methodologies. In a few cases, where data were available only quarterly; monthly observations were obtained by simple interpolation.
The remaining de jure ERBS, Ecuador, September 1993, could not be classified in any of the first three experiments.
We note, however, that if the first two clusters (both deemed de jure ERBS) are taken together, we could no longer assess whether ERBS tend to be preceded by longer history, since the remaining cluster is unclassifiable.
The reason for not measuring Δrt as month-to-month changes or deviations from the stabilization date T, is the strong seasonality embedded in the series for base money and its components, which is not exhibited by the other variables used here.
Brazil, January 1991; Dominican Republic, September 1991; Ecuador, September 1993; Ghana, July 1978 and November 1996; Guinea-Bissau, August 1993 and September 1997; Iceland, January 1976 and March 1984; Lebanon, April 1988; Mozambique, October 1996; Turkey, February 1986, May 1995, and December 1998; and Uruguay, September 1991.
Output gaps were computed as deviations from a Hodrick-Prescott trend (λ=100), expressed as percentages of the trend (or potential output) value.
We do not show a separate figure for these cases, but one is available from the authors upon request.