Estimation of De Facto Exchange Rate Regimes: Synthesis of the Techniques as long as Inferring Flexibility in addition to Basket Weights With an Application to the Chinese Yuan in addition to other currencies The idea It is harder to classify a country’s regime than one would think

Estimation of De Facto Exchange Rate Regimes: Synthesis of the Techniques as long as Inferring Flexibility in addition to Basket Weights With an Application to the Chinese Yuan in addition to other currencies The idea It is harder to classify a country’s regime than one would think www.phwiki.com

Estimation of De Facto Exchange Rate Regimes: Synthesis of the Techniques as long as Inferring Flexibility in addition to Basket Weights With an Application to the Chinese Yuan in addition to other currencies The idea It is harder to classify a country’s regime than one would think

Greeley, Steve, Owner and Program Director has reference to this Academic Journal, PHwiki organized this Journal Jeffrey Frankel Harvard University To be presented at an ECB seminar, 17 June, 2009 Thanks to Danxia Xie & Shangjin Wei Estimation of De Facto Exchange Rate Regimes: Synthesis of the Techniques as long as Inferring Flexibility in addition to Basket Weights With an Application to the Chinese Yuan in addition to other currencies The idea A synthesis of two techniques as long as statistically estimating de facto exchange rate regimes: (1) a technique that we have used in the past to estimate implicit de facto weights when the hypothesis is a basket peg with little flexibility. + (2) a technique used by others to estimate the de facto degree of exchange rate flexibility when the hypothesis is an anchor to the $, but with variation around that anchor. A majority of currencies today follow variants of B in addition to -Basket-Crawl or a managed float. => We need a technique that can cover both dimensions: inferring weights in addition to inferring flexibility. Statistical estimation of de facto exchange rate regimes Synthesis technique: “Estimation of De Facto Exchange Rate Regimes: Synthesis of the Techniques as long as Inferring Flexibility in addition to Basket Weights” F & Wei (IMF SP 2008) Estimation of implicit weights in basket peg: Frankel (1993), Frankel & Wei (1993, 94, 95); F, Schmukler & Servén (2000), Bénassy-Quéré (1999, 2006), Ohno (1999) Estimation of degree of flexibility in managed float or b in addition to : Calvo & Reinhart (2002) ; Levi-Yeyati & Sturzenegger (2003) ; also Reinhart & Rogoff Application to RMB: Eichengreen (2006) ; Frankel & Wei (Econ.Policy, 2007) Allow as long as parameter variation: “Estimation of De Facto Flexibility Parameter in addition to Basket Weights in Evolving Exchange Rate Regimes” F & Xie (in progress) Application to RMB: Frankel (PER, 2009)

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De jure regime de facto Most “fixed” aren’t: Countries declaring a peg, often ab in addition to on it. “Mirage of Fixed Rates” – Obstfeld & Rogoff (1995). Most “floating” aren’t: “floaters’” Var.of E (vs. Res) not > fixers’ “Fear of Floating” – Calvo & Reinhart (2002). Most basket pegs aren’t. Weights are kept secret => It takes more than 100 observations as long as an observer to distinguish a true basket peg statistically – Frankel, Schmukler & Serven (2000) It is harder to classify a country’s regime than one would think As is by now well-known, de facto de jure. But it is genuinely difficult to classify most countries’ de facto regimes, which are intermediate regimes in addition to which change over time. Some currencies have basket anchors, often with some flexibility that can be captured either by a b in addition to or by leaning-against-the-wind intervention. Most basket peggers keep the weights secret. They want to preserve a degree of freedom from prying eyes, whether to pursue a lower degree of de facto exchange rate flexibility, as China, or a higher degree, as with most others. From the task of distinguishing de facto vs. de jure exchange rate regimes has come a lively literature. But inferring de facto weights in addition to inferring de facto flexibility are equally important, whereas most authors have hitherto done only one or the other.

First branch of the de facto regime literature estimates implicit basket weights: Regress value of local currency against values of major currencies. First examples: Frankel (1993) in addition to Frankel & Wei (1994, 95). More: Bénassy-Quéré (1999), Ohno (1999), Frankel, Schmukler, Servén & Fajnzylber (2001), Bénassy-Quéré, Coeuré, & Mignon (2004) . Example of China, post 7/05: Eichengreen (2006) , Shah, Zeileis, & Patnaik (2005), Yamazaki (2006) in addition to Frankel-Wei (2006, 07) . Finding: RMB still pegged in 2005-06, with 95% weight on $. Implicit basket weights method – regress value of local currency against values of major currencies – continued. Null Hypotheses: Close fit => a peg. Coefficient of 1 on $ => $ peg. Or significant weights on other currencies => basket peg. But if the test rejects tight basket peg, what is the Alternative Hypothesis Second way to estimate de facto regimes: estimate degree of flexibility, typically presuming, e.g., anchor currency = $ Calvo & Reinhart (2002): Variability of Exchange Rate (E) vs. Variability of Reserves. Levy-Yeyati & Sturzenegger (2005): cluster analysis based on Variability of E & E, in addition to of Reserves

But, the de facto classification schemes give very different answers among themselves. Why Different ways of quantifying flexibility The correct anchor currency may not always be the dollar Most currencies cannot be neatly categorized In particular, countries switch parameters in addition to regimes frequently. A preliminary look at the data First set of countries examined: 9 small countries that have been officially identified by the IMF as following basket pegs: Latvia, Papua New Guinea, Botswana, Vanuatu, Fiji, W.Samoa, Malta & the Seychelles. 4 known floaters: Australia, Canada in addition to Japan. 3 peggers of special interest: China, Hong Kong & Malaysia. Variances of E & Reserves are computed within the period 1980-2007, as long as 7-year intervals The aim in choosing this interval: long enough to generate reliable parameter estimates, in addition to yet not so long as inevitably to include major changes in each country’s exchange rate regime. All changes are logarithmic, throughout this research. We try subtracting imputed interest earnings from reported Reserves to get intervention.

Lessons from Figure 1 The folly of judging a country’s exchange rate regime – the extent to which it seeks to stabilize the value of its currency – by looking simply at variation in the exchange rate. E.g. Var(E) as long as 1980-86 A$ > 2001-07 ¥. But not because the A$ more flexible. It is rather because Australia was hit by much larger shocks. One must focus on Var(E) relative to Var(Res). Countries that specialize in mineral products tend to have larger shocks. Lessons from Figure 1, continued Even countries that float use FX reserves actively. E.g., Canada in the 1980s. A currency with a firm peg (e.g., Hong Kong) can experience low variability of reserves, because it has low variability of shocks.

Distillation of technique to infer flexibility When a shock increases international dem in addition to as long as korona, do the authorities allow it to show up as an appreciation, or as a rise in reserves We frame the issue in terms of Exchange Market Pressure (EMP), defined as % increase in the value of the currency plus increase in reserves (as share of monetary base). EMP variable appears on the RHS of the equation. The % rise in the value of the currency appears on the left. A coefficient of 0 on EMP signifies a fixed E (no changes in the value of the currency), a coefficient of 1 signifies a freely floating rate (no changes in reserves) in addition to a coefficient somewhere in between indicates a correspondingly flexible/stable intermediate regime. A limitation of papers that estimate flexibility They sometimes choose arbitrarily the major currency in terms of which flexibility in addition to stability are to be defined. The $ is the most common choice. This is fine as long as some countries. But as long as Europe, the € is more relevant. And as long as others – in Asia/Pacific, the Middle East & parts of Africa – the relevant as long as eign currency is neither the $ nor the € , but some basket. It would be better to let the data tell us what is the relevant anchor as long as a given country, rather than making the judgment a priori. The technique that estimates basket weights Assuming the value of the home currency is determined by a currency basket, how does one uncover the currency composition & weights This is a problem to which OLS is unusually well suited. We regress changes in the log of H, the value of the home currency, against changes in the log values of the c in addition to idate currencies. Algebraically, if the value of the home currency H is pegged to the values of currencies X1, X2, & Xn, with weights equal to w1, w2, & wn, then logH(t) =c+ w(j) [ logX(j)] (1) If the exchange rate is truly governed by a strict basket peg, then we should be able to recover the true weights, w(j), precisely, so long as we have more observations than c in addition to idate currencies; in addition to the equation should have a perfect fit.

The question of the numeraire Methodology question: how to define “value” of each currency.[1] In a true basket peg, the choice of numeraire currency is immaterial; we estimate the weights accurately regardless. [2] In practice, few countries take their basket pegs literally enough to produce such a tight fit. One must then think about non-basket factors in the regression (EMP, the trend term, error term): Are they better measured in terms of one numeraire or another We choose as numeraire the SDR. F&Wei checked how much difference numeraire choice makes. by trying the Swiss franc as a robustness check in addition to in Monte Carlo studies [1] Frankel(1993) used purchasing power over a consumer basket of domestic goods as numeraire; Frankel-Wei (1995) used the SDR; Frankel-Wei (1994, 06), Ohno (1999), in addition to Eichengreen (2006) used the Swiss franc; Bénassy-Quéré (1999), the $; Frankel, Schmukler in addition to Luis Servén (2000), a GDP-weighted basket of 5 major currencies; in addition to Yamazaki (2006), the Canadian $. [2] assuming weights add to1, in addition to no error term, constant term, or other non-currency variable. Synthesis equation logH(t) = c + w(j) [logX(j, t)] + ß { emp(t)} + u(t) (2) where emp(t) [logH (t)] + [Res (t) / MB (t) ]. We impose w(j) = 1, implemented by treating £ as the last currency. 4-year sub-samples estimated as long as each country Findings First we test out the synthesis technique on some known $ peggers RMB (Table 2.5): a perfect peg to the dollar during 2001-04 ($ coefficient =.99, flexibility coefficient insignificantly different from 0, & R2=.99). In 2005-07 the EMP coefficient suggested that only 90% of increased dem in addition to as long as the currency shows up in reserves, rather than 100%; but the $ weight & R2 were as high as ever. Hong Kong $ (Table 2.8): close to full weight on US$, 0 flexibility, & perfect fit.

A commodity-producing pegger Kuwaiti dinar shows a firm peg throughout most of the period: a near-zero flexibility parameter, & R2 > .9 (IV estimates in Table 3.5; IV= price of oil). A small weight was assigned to other currencies in the 1980s basket, but in the 2nd half of the sample, the anchor was usually a simple $ peg. A first official basket pegger which is on a path to the € The Latvian lat (Table 2.10) Flexibility is low during the 1990s, in addition to has disappeared altogether since 2000. R2 > .9 during 1996-2003. The combination of low flexibility coefficient in addition to a high R2 during 2000-03 suggests a particularly tight basket peg during these years. Initially the estimated weights include $-weight .4 ¥-weight .3; though both decline over time. DM-weight .3 until 1999, then transferred to €: .2 in 2000-03 in addition to .5 in 2004-07. A 2nd official basket pegger also on a path to the € The Maltese lira (Table 2.12) a tight peg during 1984-1991 in addition to 2004-07 (low flexibility coefficient & high R2). During 1980-2003, weight on the $ is .2 -.4. During 1980-1995, the European currencies garner .3-.4, the £ .2-.3 & the ¥ .1. At the end of the sample period, the weight on the € rises almost to .9.

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3rd official basket pegger Norwegian kroner (Table 2.14) The estimates show heavy intervention. Weights are initially .3 on the $ in addition to .4 on European currencies (+ perhaps a little weight on ¥ & £ ). But the weight on the European currencies rises at the expense of the $, until the latter part of the sample period shows full weight on the € in addition to none on the $. 4th official basket pegger Seychelles rupee (Table 2.17) confirms its official classification, particularly in 1984-1995: not only is the flexibility coefficient essentially 0, but R2 > .97. Estimated weights: .4 on the $, .3 on the European currencies, .2 on the ¥ in addition to .1 on the £. After 2004, the $ weight suddenly shoots up to .9 . 2 Pacific basket peggers Vanuatu (Table 2.19) low exchange rate flexibility in addition to a fairly close fit. roughly comparable weights on the $ , ¥, €, in addition to £ . Western Samoa (Table 2.20) heavy intervention during the first 3 sub-periods, around a basket that weights the $ most , in addition to the ¥ 2nd. More flexibility after 1992. Weights in the reference basket during 2000-2003 are similar, except the € now receives a large significant weight (.4).

A BBC country, rare in that it announced explicitly the parameters: basket weights, b in addition to width in addition to rate of crawl. Chile in the 1980s & 1990s (Table 2.4) R2 > .9. The $ weight is always high, but others enter too. Significant downward crawl 1980-99. Estimates qualitatively capture Chile’s shift from $ anchor alone in the 1980s, to a basket starting in 1992. move to full floating in 1999. Chile, continued But the estimates do not correspond perfectly to the policy shifts of 1992 & 99 Possible explanations as long as gap between official regime in addition to estimates include: De facto de jure Parameter changes more frequent than the 4-year sub-periods. The Chilean authorities announced 18 changes in regime parameters (weights, width, in addition to rate of crawl) during the 18-year period 1982 -1999. The difficulty is that we have only monthly data on reserves, as long as most countries => it is not possible to estimate meaningful parameter values if they change every year or so. Floaters Australian $ (Table 2.1) The coefficient on EMP shows less flexibility than one would have expected, given that the currency is thought to have floated throughout this period. Perhaps the problem is endogeneity of EMP. World commodity prices are a natural IV. (Table 3.1) For each sub-period, the estimated flexibility coefficient is indeed higher than it was under OLS, but still far below 1.

Recurrent finding: IV estimate on EMP is higher than OLS estimate (but lower in significance) Floaters: IV estimates as long as Canadian $, as with A$, show flexibility parameters in each sub-period higher than they were under OLS, but surprisingly insignificant statistically. IV also raises flexibility coefficient as long as Intermediate regimes: Thail in addition to (Table 3.11) IV = price of rice W.Samoa (Table 3.12) IV = price of coconuts. Jeffrey Frankel James W. Harpel Professor of Capital Formation & Growth Harvard Kennedy School http://ksghome.harvard.edu/~jfrankel/index.htm Blog: http://content.ksg.harvard.edu/blog/jeff-frankels-weblog/

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