Jeffrey Frankel (HKS, Harvard University )

1
Estimation of De Facto Flexibility Parameter and
Basket Weightsin Evolving Exchange Rate
Regimes

• 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, 230 Atlanta Marriott Marquis,
  Marquis Ballroom Salon C

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
• gt 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).

5
Several things are wrong.

• 1) Attempts to infer statistically a countrys
  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 ? E, and
  of ? Reserves

7
This 1st approach can be phrased in terms of
Exchange Market Pressure

• Define EMP ? value of currency ? 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 ?value of local currency against
  ? 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 ?value of local currency against ?
  values of major currencies -- continued.
• Null Hypotheses Close fit gt a peg.
• Coefficient of 1 on gt peg.
• Or significant weights on other currencies
  gt 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.
• gt 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 wont work, because
  parameter changes come at irregular intervals.
• Chou test wont 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
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
Application to RMBEichengreen (06), Ogawa (06),
FrankelWei (07)

• Synthesis Estimation of De Facto Exchange
  Rate Regimes Synthesis of the Techniques for
  Inferring Flexibility and Basket Weights F
  Wei (IMF SP 2008)

Application to RMB Frankel (2009)
Econometric estimation of structural break
points Bai Perron (1998, 2003)
Allow for parameter variation Estimation of De
Facto Flexibility Parameter and Basket Weights in
Evolving Exchange Rate Regimes F Xie (2010)
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
• ? logH(t) c ? w(j) ? logX(j)
  (1)

17

• ? log Ht
• c ? w(j) ? logX(j)t
• c ß(1) ? log t ß(2) ? log t
  ß(3) ? log t a ? 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.
• FWei 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

• ? 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.

21
Synthesis equation

• ? log H t c ?w(j) ? logXt d
  ?EMPt ut
  (3)
• c w(1) ? log t w (2) ? log t w
  (3) ? log t w (4) ? log t
  
  
• d ? EMP
  t u t .

22
Now we introduce Bai-Perron technique for
endogenous estimation of m possible structural
break points
(6)
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 M11999-M72009
(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 gt .6, but
  usually significantly lt 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
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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 ? E ? 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 countrys exchange rate regime.
• All changes are logarithmic, throughout this
  research.
• We try subtracting imputed interest earnings from
  reported ? Reserves to get intervention.

34
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35
Lessons from Figure 1

1. The folly of judging a countrys 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) for 1980-86 A gt 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).
2. Countries that specialize in mineral products
   tend to have larger shocks.

36
Lessons from Figure 1, continued

1. Even countries that float use FX reserves
   actively. E.g., Canada in the 1980s.
2. 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 gt .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 gt .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 gt
  .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 gt .9.
• The weight is always high, but others enter
  too.
• Significant downward crawl 1980-99.
• Estimates qualitatively capture Chiles
• 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 gt 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 Chinas Exchange Rate Regime,
  Pacific Ec.Rev., 2009.
• Updated through early 2009, on my weblog
• http//content.ksg.harvard.edu/bl
  og/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
plt0.01, plt0.05, plt0.1
50
Table 2 Rolling 12-month regressions of value of
RMB ? (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)
? 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
toward in 2007,
shifted
back to in 2008.
53
RMB was roughly flat against basket of ½-
½- in 2007 in 2008 (like 2005) .
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)
?(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
plt0.01, plt0.05, plt0.1
57
Turkey Evolution of Basket Weights, Quarterly
Regressions with Weekly data
Turkey Evolution of Basket Weights, Quarterly
Regressions with Weekly data
(1) (3) (5) (7)
VARIABLES 1/2007 1/2007 7/2007 1/2008 7/2008

JPY JPY -0.660 -0.250 -0.758 0.899
(0.329) (0.166) (0.255) (0.803)
USD USD 0.397 -0.036 0.912 -0.961
(0.930) (0.302) (0.500) (0.920)
EUR EUR 0.906 1.931 0.566 -0.088
(0.863) (0.260) (0.371) (0.738)
?(emp) ?(emp) 0.149 0.231 0.798 0.825
(0.083) (0.056) (0.204) (0.279)
Observations Observations 10 14 13 10
GBP GBP 0.357 -0.644 0.281 1.151

Robust standard errors in parentheses
plt0.01, plt0.05, plt0.1
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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)

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Identifying Break Points in Chinas 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)
?emp 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 plt0.01, plt0.05, plt0.1 Robust
standard errors in parentheses
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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.

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Monte Carlo exchange rate under simulated
basketband regime(with parameters from Papual
New Guinea)
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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.

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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 .

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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.