Exchange Rate Flexibility: How Should We Measure It?
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Exchange Rate Flexibility: How Should We Measure It? Michael Bleaney 1 & Mo Tian 2 # The Author(s) 2020
Abstract This paper first examines some recent exchange rate classification schemes. There is little evidence of a trend towards greater agreement between schemes. There is a probability of between 16 and 28% that a peg in one classification scheme is coded as a float in a different scheme, or vice versa. This probability is much smaller for the tightest forms of peg and the most volatile floats. Continuous indices of exchange rate flexibility are analysed and shown to have significant potential, despite the lack of interest in them shown in previous research. Keywords Exchange rates regimes . Inflation JEL Classifications F31
1 Introduction Since the 1990s, when it was recognised that central banks had sometimes been misreporting their exchange rate regimes, there has been intensive work on finding new methods of classification (e.g. Ghosh et al. 2002; Klein and Shambaugh 2010; LevyYeyati and Sturzenegger 2005; Reinhart and Rogoff 2004; Shambaugh 2004). This research has almost entirely followed the path of defining a number of “fine” (disaggregated) or “coarse” (aggregated) categories to which to allocate each observation, rather than develop a numerical measure of exchange rate flexibility. Although Ghosh et al. (2002, pp. 49–51) suggest a simple numerical flexibility index, they make no use of it except to define three categories of regime (pegged, intermediate and floating). Reviews of this research effort in the classification of exchange rate regimes have generally concluded that it has been unsatisfactory, at least in so far as that is measured by the degree of
* Michael Bleaney [email protected]
1
School of Economics, University of Nottingham, Nottingham NG7 2RD, UK
2
Business School, University of Nottingham, Nottingham, UK
Bleaney and Tian
agreement between alternative schemes (Bleaney et al. 2017; Eichengreen and RazoGarcia 2013; Tavlas et al. 2008). The purpose of the present paper is twofold: to investigate whether this remains true of the more recent classification efforts, and to assess the value of numerical alternatives. The recent exchange rate regime classifications that we consider are those of Ilzetzki et al. (2017), Obstfeld et al. (2010) and Bleaney and Tian (2017), together with numerical measures of exchange rate flexibility associated with the last two. Ilzetzki et al. (2017) have updated the Reinhart-Rogoff classification up to 2016 without amending the classification algorithm in any way. Obstfeld et al. (2010) have relaxed the rather stringent definitions of a peg used by Shambaugh (2004) and Klein and Shambaugh (2010) to be more in line with other classifications, and the data have been updated to 2014. Bleaney and Tian (2017) have suggested a method of measuring exchange rate flexibility and identifying exchange rate regimes by a regression similar to that previously used by Frankel and Wei (1995) and Slavov (2013) to identify the basket of anc
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