1

Jeffrey FrankelHarvard University

To be presented at an ECB seminar, 17 June, 2009Thanks to Danxia Xie & Shangjin Wei

Estimation of De Facto Exchange Rate Regimes: Synthesis of the

Techniques for Inferring Flexibility and Basket Weights

With an Applicationto the Chinese Yuan

and other currencies

2

The idea• A synthesis of two techniques for 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 Band-Basket-Crawl – or a managed float.

• => We need a technique that can cover both dimensions: inferring weights and inferring flexibility.

3

Statistical estimation of de facto exchange rate regimes

Synthesis technique: “Estimation of De Facto Exchange Rate Regimes: Synthesis of the Techniques for Inferring Flexibility and 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 band: Calvo & Reinhart (2002) ; Levi-Yeyati & Sturzenegger (2003)…;

also Reinhart & Rogoff …Application to RMB: Eichengreen (2006) ;

Frankel & Wei (Econ.Policy, 2007)

Allow for parameter variation: “Estimation of De Facto Flexibility Parameter and Basket Weights in Evolving Exchange Rate Regimes” F & Xie (in progress)

Application to RMB: Frankel (PER, 2009)

4

De jure regime de facto

1. Most “fixed” aren’t: Countries declaring a peg, often abandon it.

≡ “Mirage of Fixed Rates” -- Obstfeld & Rogoff (1995).

2. Most “floating” aren’t: “floaters’” Var.of E (vs. Res) not > fixers’

≡ “Fear of Floating” -- Calvo & Reinhart (2002).

3. Most basket pegs aren’t. Weights are kept secret => It takes more than 100 observations for an observer to distinguish a true basket peg statistically -- Frankel, Schmukler & Serven (2000)

5

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 and which change over time.

6

• Some currencies have basket anchors, often with some flexibility that can be captured either by a band 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 and inferring de facto flexibility are equally important, whereas most authors have hitherto done only one or the other.

7

First branch of the de facto regime literature estimates implicit basket weights:

Regress Δvalue of local currency against Δ values of major currencies.

• First examples: Frankel (1993) and 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) and Frankel-Wei (2006, 07) .– Finding: RMB still pegged in 2005-06, with 95% weight on $.

8

Implicit basket weights method -- regress Δvalue of local currency against

Δ 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?

9

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 & Δ E, and of Δ Reserves

10

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

and regimes frequently.

11

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 and Japan.

– 3 peggers of special interest: China, Hong Kong & Malaysia.

12

Variances of Δ E & Δ Reserves are computed

• within the period 1980-2007,

• for 7-year intervals– The aim in choosing this interval: long enough to generate

reliable parameter estimates, and 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 Δ Reserves to get intervention.

13

Figure 1: Comparison of Reserve Variability Vs. Exchange Rate Variability

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wst88 wst95 wst02

nok80 nok87 nok94 nok01

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0

.02

.04

.06

0 .0005 .001 .0015 .002

Variance of ∆ log Exchange Rate (in US$)

Country from year t to year t+6

Floating/Fix line Shocks line

1980-2007, in 7-year intervals

Va

rian

ce o

f ∆ lo

g Re

serve

s*

of

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14

Lessons from Figure 1

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(ΔE) for 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(ΔE) relative to Var(ΔRes).

2. Countries that specialize in mineral products tend to have larger shocks.

15

Lessons from Figure 1, continued

3. Even countries that float use FX reserves actively. E.g., Canada in the 1980s.

4. A currency with a firm peg (e.g., Hong

Kong) can experience low variability of reserves, because it has low variability of shocks.

16

Distillation of technique to infer flexibility• When a shock increases international demand for 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) and – a coefficient somewhere in between indicates

a correspondingly flexible/stable intermediate regime.

17

A limitation of papers that estimate flexibility

• They sometimes choose arbitrarily the major currency in terms of which flexibility and stability are to be defined.

• The $ is the most common choice. – This is fine for some countries. – But for Europe, the € is more relevant. – And for others -- in Asia/Pacific, the Middle East &

parts of Africa -- the relevant foreign currency is neither the $ nor the € , but some basket.

• It would be better to let the data tell us what is the relevant anchor for a given country, rather than making the judgment a priori.

18

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 candidate 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

Δ logH(t) =c+ ∑ w(j) [Δ 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 candidate currencies; and the equation should have a perfect fit.

19

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 – and 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), and Eichengreen (2006) used the Swiss franc; Bénassy-Quéré (1999), the $; Frankel, Schmukler and Luis Servén (2000), a GDP-weighted basket of 5 major currencies; and Yamazaki (2006), the Canadian $. [2] assuming weights add to1, and no error term, constant term, or other non-currency variable.

20

Synthesis equation

Δ logH(t) = c + ∑ w(j) Δ[logX(j, t)] + ß {Δ emp(t)} + u(t) (2)where

Δ emp(t) ≡ Δ[logH (t)] + [ΔRes (t) / MB (t) ].

We impose ∑ w(j) = 1, implemented by treating £ as the last currency.

4-year sub-samples estimated for each country

21

FindingsFirst 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 demand for 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.

22

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.

23

A first official basket peggerwhich is on a path to the €

• The Latvian lat (Table 2.10)

– Flexibility is low during the 1990s, and has disappeared altogether since 2000. R2 > .9 during 1996-2003.

– The combination of low flexibility coefficient and 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 and .5 in 2004-07.

24

A 2nd official basket peggeralso on a path to the €

• The Maltese lira (Table 2.12)

– a tight peg during 1984-1991 and 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.

25

3rd official basket pegger

• Norwegian kroner (Table 2.14) – The estimates show heavy intervention.

– Weights are initially .3 on the $ and .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 € and none on the $.

26

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 ¥ and .1 on the £.

– After 2004, the $ weight suddenly shoots up to .9 .

27

2 Pacific basket peggers

• Vanuatu (Table 2.19) – low exchange rate flexibility and a fairly close fit. – roughly comparable weights on the $ , ¥, €, and £ .

• Western Samoa (Table 2.20)

– heavy intervention during the first 3 sub-periods, – around a basket that weights the $ most , and 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).

28

A BBC country,rare in that it announced explicitly the parameters:

basket weights, band width and 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.

29

Chile, continued

• But the estimates do not correspond perfectly to the policy shifts of 1992 & 99

• Possible explanations for gap between official regime and 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, and rate of crawl) during the 18-year period 1982 -1999.

• The difficulty is that we have only monthly data on reserves, for most countries => it is not possible to estimate meaningful parameter values if they change every year or so.

30

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.

31

Technical extensions• Allow coefficients to vary over time, even within

the 4-year sub-samples (Tables 4.1-4.10) ;• Relax constraint that [ΔlogH] and [Δ logRes]

enter with the same coefficient (Tables 5.1-5.8);

• Insert Δ Interest rate alongside Δ Reserves & Δ E (Tables 6.1-6.5) ;

• Check for robustness with respect to the numeraire unit used to define currency values: Sw.franc vs. SDR (Table 7)

• Alternate definitions of Exchange Market Pressure, defining Δ Reserves in % terms or as share of Monetary Base.

32

Current applicationsusing higher-frequency data

• (I) RMB– “New Estimation of China’s Exchange Rate Regime,”

forthcoming, Pacific Ec.Rev., 2009.– Updated through early 2009, on my weblog

http://content.ksg.harvard.edu/blog/jeff_frankels_weblog/2009/03/11/the-rmb-has-now-moved-back-to-the-dollar/ .

• (II) Estimation to allow for frequent parameter shifts– (Results not yet written up)– Results for 13 countries offering weekly reserve data,

• Therefore allowing estimation intervals shorter than 1 year.

– Econometric techniques to estimate parameter shifts endogenously.

33

(I) Estimation of RMB with updated technique and data (through early 2008)

• This approach reveals that the RMB basket had loosened link to the $ by late 2006, and switched substantial weight onto the € by mid 2007.

• An implication is that the appreciation of the RMB against the dollar observed during this period was due to the appreciation of the € against the $, not to any upward trend in the RMB relative to its basket.

34

Table 1: Evolution of RMB Basket Weights from 10-22-2006,

3-month windows of daily data, ending on the month shown

COEFFICIENT 12/2006 3/2007 2/2008 9/2008 11/2008

usd 1.005*** 0.814*** 0.878*** 0.992*** 0.971***(0.038) (0.035) (0.041) (0.027) (0.039)

eur 0.006 0.068** 0.019 0.049** 0.070**(0.038) (0.027) (0.026) (0.020) (0.028)

jpy -0.023 0.020* 0.044*** -0.030 -0.022(0.035) (0.011) (0.017) (0.019) (0.027)

Constant 0.000** 0.000 0.001*** 0.000 0.000

(0.000) (0.000) (0.000) (0.000) (0.000)

Observations 61 64 61 60 18

R-squared 0.95 0.94 0.96 1.00 1.00

krw 0.011 0.098 0.059 -0.011 -0.019

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

35

Table 2: Rolling 12-month regressions of value of RMB Δ (EMP) defined as [res(t)-res(t-1)]/mb(t-1) + [exr(t)-exr(t-1)]/exr(t-1)

12-month windows, ending on the month shown

COEFFICIENT 06M11 07M2 07M3 08M3 08M5

usd 0.909*** 0.756*** 0.756*** 0.613*** 0.597***(0.147) (0.105) (0.067) (0.171) (0.130)

jpy -0.015 -0.095 -0.140 0.059 0.030(0.098) (0.085) (0.089) (0.081) (0.083)

eur 0.029 0.116 0.169** 0.357** 0.397***(0.117) (0.096) (0.068) (0.143) (0.105)

Δ emp 0.137 0.179*** 0.187*** 0.290*** 0.249**(0.100) (0.047) (0.029) (0.076) (0.097)

Constant -0.001 -0.003* -0.004*** -0.006 -0.004(0.003) (0.002) (0.001) (0.003) (0.003)

Observations 12 12 12 12 12

R-squared 0.984 0.967 0.975 0.967 0.966

krw 0.077 0.215 -0.030 -0.024

36

In 2008, however, RMB policy changed again.

• The appreciation of the previous year had put unwelcome pressure on exporters.

• Chinese leaders changed policy– Naughton (2008).– Observing that putting half-weight on the € during a

downward $ trend had led to appreciation,– in mid-2008, they decided to switch back virtually

to a $ peg.– During the most recent period, September 2008-

February 2009, estimates show that all the weight has once again fallen on the $.

37

The weights in the RMB basket shifted toward € in 2007, back to $ in 2008.

38

RMB was roughly flat against basket of ½-$ + ½-€ in 2007;

$ in 2008 (like 2005) .

0. 08

0. 09

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/RMB

EUR/ RMB USD/ RMB ½$+½€ basket

RMB valued in terms of $

RMB valued in terms of €

RMB valued in terms of ½$+½€ basket

Appreciation vs. $ was due to weight on € during period of $ weakening

39

Ironically…

• The $ has appreciated vs. the € since 2008.

– So the $-pegged RMB is stronger than if the basket had been retained !

– Yet US Congressmen are still agitating for a more flexible exchange rate --• not realizing that, recently, a more flexible exchange

rate would have meant a weaker RMB !• especially since the PBoC lost reserves in January &

February 2009.

40

(II) Results for 13 countries that offer weekly data on reserves (1991-2008):

• Argentina, Brazil, Canada, Chile, Colombia, India, Indonesia, Mexico, Peru, Russia, Thailand, Turkey & Venezuela.

• E.g., – Colombia: during 2008, $ weight fell &

flexibility increased.– Turkey during 2008 & 2008 moved from high

euro weight with low flexibility to low euro weight with high flexibility.

41

Colombia: Evolution of Basket Weights,Monthly Regressions with Daily data, 2008

(2) (4) (5) (8) (9) (10)

VARIABLES 2/2008 4/2008 5/2008 8/2008 9/2008 10/2008

JPY -0.028 0.023 0.227 0.028 -0.139 0.319**(0.093) (0.063) (0.198) (0.134) (0.164) (0.121)

USD 0.480** 0.334*** 0.019 -0.073 0.218 -0.294(0.165) (0.060) (0.340) (0.173) (0.233) (0.230)

EUR 0.602** 0.522*** 0.226 0.774*** 0.738** 0.886**(0.274) (0.091) (0.277) (0.180) (0.343) (0.347)

Δ(emp) 0.160 0.447*** 0.688*** 0.931*** 0.858*** 0.650***(0.110) (0.049) (0.091) (0.062) (0.076) (0.203)

Observations 21 22 14 15 20 21

GBP -0.054 0.121 0.528 0.270 0.183 0.089

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

42

(1) (3) (5) (7)

VARIABLES 1/2007 7/2007 1/2008 7/2008

JPY -0.660 -0.250 -0.758** 0.899(0.329) (0.166) (0.255) (0.803)

USD 0.397 -0.036 0.912 -0.961(0.930) (0.302) (0.500) (0.920)

EUR 0.906 1.931*** 0.566 -0.088(0.863) (0.260) (0.371) (0.738)

Δ(emp) 0.149 0.231*** 0.798*** 0.825**(0.083) (0.056) (0.204) (0.279)

Observations 10 14 13 10

GBP 0.357 -0.644 0.281 1.151

Turkey: Evolution of Basket Weights, Quarterly Regressions with Weekly data

43

For countries that do not have weekly data on reserves, we can interpolate between months to take advantage of

high-frequency exchange rate data

• giving enough observations per year to allow the use of more sophisticated econometric techniques that estimate endogenously the dates at which parameters shift.

• Application to China – (next slide; thanks to Dan Xie)– reinforces conclusion:

RMB shifted back to $ peg 9/15/08 (through March 09)

44

Identifying Break

Points in China’s

Exchange Rate

Regime

With weekly exchange rate

data and monthly

reserve data(interpolations are made to get weekly

reserve data)

(1) (2) (3) (4) (5) (6)

VARIABLES 1/6/2005-7/15/2005

7/29/2005-4/27/2007

5/4/2007-11/16/2007

11/23/2007-9/8/2008

9/15/2008-12/8/2008

12/15/2008-3/11/2009

US dollar 1.000*** 0.893*** 0.596*** 0.685*** 0.965*** 0.929***

(0.000) (0.030) (0.101) (0.066) (0.091) (0.058)

euro 0.000 0.046* 0.087 0.241*** 0.128 0.037

(0.000) (0.025) (0.077) (0.050) (0.082) (0.049)

Jpn yen -0.000 0.014 0.063 0.059** -0.065** 0.010

(0.000) (0.013) (0.038) (0.022) (0.025) (0.021)

Δemp 0.000 0.034 0.129** 0.185*** 0.165 0.042

(0.000) (0.024) (0.060) (0.052) (0.125) (0.063)

Constant -0.000 0.000 0.000 0.000 -0.000 0.000

(0.000) (0.000) (0.001) (0.000) (0.001) (0.000)

Observations 28 92 29 42 13 13

Korean won -0.000 0.047 0.254 0.015 -0.027 0.023

R-squared 1.000 0.979 0.929 0.990 0.999 0.999

Note: *** p<0.01, ** p<0.05, * p<0.1 Robust standard errors in parentheses

45

Bottom line(s)

• The new synthesis technique is necessary to discern exchange rate regimes where both the anchor weights and the flexibility parameter are unknown.

• Weekly data are necessary to capture the frequency with which many countries’ exchange rate regimes evolve.

46

47

Appendix 1: Correlations Among Regime Classification Schemes

GGW =Ghosh, Gulde & Wolf. LY-S = Levy-Yeyati & Sturzenegger. R-R = Reinhart & RogoffSample: 47 countries. From Frankel (2004).

IMF GGW LY-S R-R

IMF 1.00 (100.0)

GGW 0.60 (55.1)

1.00 (100.0)

LY-S 0.28 (41.0)

0.13 (35.3)

1.00 (100.0)

R-R 0.33 (55.1)

0.34 (35.2)

0.41 (45.3)

1.00 (100.0)

(Frequency of outright coincidence, in %, given in parenthesis.)

48

Appendix 2: Monte Carlo studyon fabricated currency regimes

• Two kinds of flexibility– Leaning ½ -way against the wind of EMP fluctuations

(Table 8.1)

– Or else constrained to remain in a 5% band (Table 8.2)

• Two anchors– $ peg– Basket: 1/3 $, 1/3 €, 1/3 ¥

• The synthesis technique generally gives the right answer.

49

Monte Carlo exchange rate under simulated basket+band regime

(with parameters from Papual New Guinea)

-.4

5-.

4-.

35

-.3

-.2

5-.

2m

csa/m

csa_

no

n/u

ppe

rb/lo

we

rb

240 260 280 300 320 340T

mcsa mcsa_nonupperb lowerb

50

Appendix 3 -- One concern: endogeneity of the exchange market pressure variable

• One would prefer to observe changes in the international demand for the home currency known to originate in exogenous shocks.

• In the case of countries that specialize in the production of mineral or agricultural commodities, there is a ready-made IV: changes in the price of the commodity on world markets.

• Accordingly, Tables 3 repeat the synthesis estimation technique, but for the commodity producers it uses changes in the world price of the commodity in question as an IV for changes in EMP.

51

To address endogeneity of EMP, we use commodity prices as IV

• Malaysian ringgit (Table 2.11 ) OLS. – Only in 1996-99 is there evidence of

exchange rate flexibility (Asia crisis ). – During 2000-03 there is a perfect peg to the $

(coefficient $ R2 both =1). – In 2004-07 the peg is still fairly strong, but here the weight of

the US$ falls to .6, partially replaced by the Singapore $ (weight = .4) .

• IV = prices of tin & semiconductors (Table 3.6) – Again, a perfect $ peg during 2000-03, – followed by shift to a basket consisting of an average of the US

$ + the Singapore $.

52

Recurrent finding: IV estimate on EMP is higher than OLS estimate

(but lower in significance)

• Floaters: IV estimates for 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 for Intermediate regimes:– Thailand (Table 3.11) IV = price of rice – W.Samoa (Table 3.12) IV = price of coconuts.

53

Jeffrey FrankelJames 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/