Jeffrey Frankel (HKS, Harvard University ) & Dan Xie (Peterson Inst. for Internatl. Econ.)

1

Jeffrey Frankel (HKS, Harvard University )

& Dan Xie (Peterson Inst. for Internatl. Econ.)

Annual Meeting of the American Economics Association, Atlanta.Session on International Financial Markets, Ken West presiding;

January 4, 2010, 2:30; Atlanta Marriott Marquis, Marquis Ballroom – Salon C

Estimation of De Facto Flexibility Parameter and Basket Weights

in Evolving Exchange Rate

Regimes

2

As is by now well-known, the exchange rate regimes that countries follow in practice (de facto) often depart

from the regimes that they announce officially (de jure).

• Many countries that say they float in fact intervene heavily in the foreign exchange market. [1]

• Many countries that say they fix in fact devalue when trouble arises.[2]

• Many countries that say they target a basket of major currencies in fact fiddle with the weights.[3]

[1] “Fear of floating:” Calvo & Reinhart (2001, 2002); Reinhart (2000).

[2] “The mirage of fixed exchange rates:” Obstfeld & Rogoff (1995). Klein & Marion (1997).

[3] Parameters kept secret: Frankel, Schmukler & Servén (2000).

3

Economists have offered de facto classifications, placing countries

into the “true” categories• Important examples include Ghosh, Gulde & Wolf

(2000), Reinhart & Rogoff (2004), Shambaugh (2004a), and more to be cited.

• Tavlas, Dellas & Stockman (2008) survey the literature.

• Unfortunately, these classification schemes disagree with each other as much as they disagree with the de jure classification! [1]

• => Something must be wrong.

[1] Bénassy-Quéré, et al (Table 5, 2004); Frankel (Table 1, 2004); and Shambaugh (2004b).

4

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.)

5

Several things are wrong.

1) Attempts to infer statistically a country’s flexibility from the variability of its exchange rate alone ignore that some countries experience greater shocks than others.

That problem can be addressed by comparing exchange rate variability to foreign exchange reserve variability,

• Calvo & Reinhart (2002); Levy-Yeyati & Sturzenegger (2003, 05).

6

First approach 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

7

This 1st approach can be phrased in terms of Exchange Market Pressure:

– Define EMP = Δ value of currency + Δ reserves.

– EMP represents shocks in demand for the currency.

– Flexibility can be estimated as the propensity of the central bank to let shocks show up in the price of the currency (floating) ,vs. the quantity of the currency (fixed), or in between (intermediate exchange rate regime).

8

Several things are wrong, continued.

2) Those papers impose the choice of the major currency around which the country in question defines its value (often the $).

• It would be better to estimate endogenously whether the anchor currency is the $, the €, some other currency, or some basket of currencies.

• That problem has been addressed by a 2nd branch.

9

Second approach in 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),

Ogawa (2006), Frankel-Wei (2006, 2007), Frankel (2009)– Findings:

• RMB still pegged in 2005-06, with 95% weight on $.• Moved away from $ (weight on €) in 2007-08• Returned to approximate $ peg in mid 2008.

10

• 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 flexibility, as China, – or a higher degree, as with most others.

11

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?

12

Several things are wrong, continued.

3) The 2nd approach (inferring the anchor currency or basket) does not allow for flexibility around that anchor.

• Inferring de facto weights and inferring de facto flexibility are equally important,

• whereas most authors have hitherto done only one or the other.

13

The synthesis technique• A synthesis of the two approaches for statistically

estimating de facto exchange rate regimes: (1) the 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) the technique used by others to estimate de facto 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.

14

Several things are wrong, continued.

4) All these approaches, even the synthesis technique are plagued by the problem that many countries frequently change regimes or (for those with intermediate regimes) change parameters.

• E.g., Chile changed parameters 18 times in 18 years (1980s-90s) • Year-by-year estimation won’t work,

because parameter changes come at irregular intervals.• Chou test won’t work,

because one does not usually know the candidate dates.• Solution: Apply Bai-Perron econometric technique

for endogenous estimation of structural break point dates.

15

Statistical estimation of de facto exchange rate regimes

Synthesis: “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); Ohno (1999), F, Schmukler & Servén (2000), Bénassy-Quéré (1999, 2006)…

Estimation of degree of flexibility in managed float: Calvo & Reinhart (2002); Levi-Yeyati & Sturzenegger (2003)…;

also Reinhart & Rogoff …

Allow for parameter variation: “Estimation of De Facto Flexibility Parameter and Basket Weights in Evolving Exchange Rate Regimes” F & Xie (2010)

Application to RMB: Frankel (2009) Econometric estimation of structural break points: Bai & Perron (1998, 2003)

Application to RMB:Eichengreen (06), Ogawa (06), Frankel&Wei (07)

16

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)

17

Δ log Ht

= c + ∑ w(j) [Δ logX(j)t ]

= c + β(1) Δ log $ t + β(2) Δ log ¥t

+ β(3) Δ log €t + α Δ log £t

•If the exchange rate is truly governed by a strict basket peg, then we should recover the true weights, w(j), precisely; and the equation should have a perfect fit.

18

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.

19

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.

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.

21

Synthesis equation

Δ log H t = c + ∑w(j) [Δ logXt] + δ { ΔEMPt } + ut (3’)

= c + w(1) Δ log $ t + w (2) Δ log €t + w (3) Δ log ¥t + w (4) Δ log £t +

+ δ { Δ EMP t } + u t .

22

ttitji

k

jjiit uEMPXwcH

,,1

, loglog

1,...,1 ; ;0 ;,...,1 101 miTTTTTt mii

(6)

Now we introduce Bai-Perron technique for endogenous estimation

of m possible structural break points

23

Illustration using 5 currencies

• These are 5 emerging market currencies of interest all of which now make available their data on reserves on a weekly basis (which is necessary to get good estimates, if structural changes happen as often as yearly)

• Mexico (monetary base is also available weekly)

• Chile, Russia, Thailand, India (although reserves available weekly, denominator must be interpolated from monthly monetary base data)

24

Overview of findings

• For all five, the estimates suggest managed floats during most of the period 1999-2009.

• This was a new development for emerging markets.

• Most of the countries had some variety of a peg before the currency crises of the 1990s.

• But the Bai-Perron test shows statistically significant structural breaks for every currency,

• even when the threshold is set high, at the 1% level of statistical significance.

25

Table 1A reports estimation for the Mexican peso

• 5 structural breaks• The peso is known as a floater. • To the extent Mexico intervenes to reduce exchange rate

variation, $ is the primary anchor, but there also appears to have been some weight on € starting in 2003.

• Aug.2006 - Dec.2008, coefficient on EMP is essentially 0, surprisingly, suggesting heavier intervention around a $ target.

• But in the period starting Dec.2008, the peso once again moved away from the currency to the north, as the worst phase of the global liquidity crisis hit and $ appreciated.

26

Table 1A. Identifying Break Points in Mexican Exchange Rate Regime M1:1999-M7:2009

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

VARIABLES 1/21/1999-9/2/2001

9/9/2001-3/18/2003

3/25/2003-7/29/2006

8/5/2006-1/28/2008

2/4/2008- 12/15/2008

12/22/2008-7/29/2009

US dollar 0.92*** 0.88*** 0.62*** 1.11*** 0.96*** 0.20(0.09) (0.12) (0.07) (0.10) (0.19) (0.22)

euro 0.14 -0.09 0.30*** 0.20* 0.51*** 0.51***(0.08) (0.14) (0.09) (0.11) (0.16) (0.18)

Jpn yen -0.05 0.22*** 0.08 -0.34*** -0.33** 0.18(0.06) (0.07) (0.06) (0.06) (0.12) (0.13)

△EMP 0.14*** 0.32*** 0.17*** 0.02 0.07 0.28***(0.03) (0.03) (0.03) (0.02) (0.07) (0.04)

Constant 0.00 -0.00*** -0.00* -0.00 -0.00 0.00

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Observations 131 78 168 76 46 29

R-squared 0.62 0.86 0.69 0.67 0.54 0.78

Br. Pound -0.01 -0.01 -0.01 0.02 -0.14 0.11

27

Tables 1B-1E

• Chile (with 3 estimated structural breaks) appears a managed floater throughout. – The anchor is exclusively the $ in some periods, but puts significant

weight on the € in other periods.

• Russia (3 structural breaks) is similar, except that the $ weight is always significantly less than 1.

• For Thailand (3 structural breaks), the $ share in the anchor basket is slightly > .6, but usually significantly < 1. – The € and ¥ show weights of about .2 each Jan.1999-Sept. 2006.

• India (5 structural breaks) apparently fixed its exchange rate during two of the sub-periods, but pursued a managed float in the other four sub-periods. – $ was always the most important of the anchor currencies, but the € was

also significant in four out of six sub-periods, and the ¥ in two.

28

Future research

• Results for other currencies will be published in other papers – Often requiring weekly interpolation between

monthly reserve figures.– Including our China updates– And true basket/band/crawl currencies

• Econometric extension: use Threshold Autoregression for target zones.

29

Conclusion: It is harder to classify regimes than one would think

• It is genuinely difficult to classify most countries’ de facto regimes: intermediate regimes that change over time.

• Need techniques – that allow for intermediate regimes (managed floating and

basket anchors)

– and that allow the parameters to change over time.• Jarko Fidrmuc allows parameters to evolve gradually

over time, by means of a Kalman filter (session Jan. 5).

30

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.

31

32

Appendix 0: 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.

33

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.

34

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

cad80

cad87 cad94

cad01 hkd94 hkd01

myr80 myr87 myr94 myr01 cny05 jpy80 jpy87 jpy94 jpy01

lvl96 lvl03

png80 png87

png94

png01

bwp80

bwp87 bwp94 bwp01

vuv80

vuv87 vuv94

vuv01

fiji80 fiji87

fiji94 fiji01 mtl80 mtl87 mtl94 mtl01

scr80

scr87 scr94

scr01

wst81

wst88 wst95 wst02

nok80 nok87 nok94 nok01

aud80

aud87 aud94 aud01

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

∆ lo

g

Re

serve

s*

35

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.

36

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.

37

Appendix 1Testing out the synthesis technique,

first 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.

38

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.

39

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.

40

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.

41

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 $.

42

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 .

43

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).

44

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.

45

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.

46

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.

47

Appendix 2: Current applicationsusing higher-frequency data

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

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 for 13 countries offering weekly reserve data,

• Therefore allowing estimation intervals shorter than 1 year.

– Econometric techniques to estimate parameter shifts endogenously.

48

(2.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.

49

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

50

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

51

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 $.

52

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

53

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

$ in 2008 (like 2005) .

0. 08

0. 09

0. 1

0. 11

0. 12

0. 13

0. 14

0. 15

0. 16

6/1/

2005

8/1/

2005

10/1

/200

5

12/1

/200

5

2/1/

2006

4/1/

2006

6/1/

2006

8/1/

2006

10/1

/200

6

12/1

/200

6

2/1/

2007

4/1/

2007

6/1/

2007

8/1/

2007

10/1

/200

7

12/1

/200

7

2/1/

2008

4/1/

2008

6/1/

2008

8/1/

2008

10/1

/200

8

12/1

/200

8

2/1/

2009

4/1/

2009

/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

54

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.

55

(2.II) Results for 13 countries that offer weekly reserve data (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.

56

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

57

(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

58

59

Appendix 3: For countries without weekly reserve data, 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)– reinforces conclusion:

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

60

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

61

Appendix 4: 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.

62

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

63

Appendix 5 -- 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.

64

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 $.

65

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.