Estimation of De Facto Exchange
Rate Regimes: Synthesis of the
Techniques for Inferring Flexibility
and Basket Weights
With an Application
to the Chinese Yuan
and other currencies
Jeffrey Frankel
Harvard University
To be presented at an ECB seminar, 17 June, 2009
Thanks to Danxia Xie & Shangjin Wei
1
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
2
dimensions: inferring weights and inferring flexibility.
Statistical estimation of de facto exchange rate regimes
Estimation of implicit weights
in basket peg: Frankel (1993),
Frankel & Wei (1993, 94, 95);
F, Schmukler & Servén (2000), BénassyQuéré (1999, 2006), Ohno (1999)…
Application to RMB: Eichengreen (2006) ;
Estimation of degree of
flexibility in managed float
or band: Calvo & Reinhart
(2002) ; Levi-Yeyati &
Sturzenegger (2003)…;
also Reinhart & Rogoff …
Frankel & Wei (Econ.Policy, 2007)
Synthesis technique: “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 (PER, 2009)
Allow for parameter variation: “Estimation of De Facto Flexibility Parameter
3
and Basket Weights in Evolving Exchange Rate Regimes” F & Xie (in progress)
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)
4
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.
5
• 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. 6
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 7$.
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?
8
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
9
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.
10
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.
11
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.
12
Figure 1: Comparison of Reserve Variability
Vs. Exchange Rate Variability
1980-2007, in 7-year intervals
Variance of ∆ log Reserves*
of
.06
vuv80
png94
scr80
wst81
bwp80
.04
scr94
scr87
scr01
.02
cad80
png80
cad87
0
hkd94
myr01
hkd01
cny05
0
aud80
png87
fiji80
fiji87
png01
vuv87
aud94 aud01
nok94
vuv94
cad94
nok80
aud87
wst88
lvl03
nok87
myr87myr80
fiji01
nok01
lvl96wst02
vuv01
fiji94
wst95
bwp87
mtl87 jpy87
mtl94
mtl01
mtl80
cad01
bwp94
jpy01
.0005
jpy80
jpy94
myr94
bwp01
.001
.0015
Variance of ∆ log Exchange Rate (in US$)
Country from year t
.002
to year t+6
Shocks line
13
Floating/Fix line
Lessons from Figure 1
1. The folly of judging a country’s exchange rate
regime – the extent to which it seeks to stabilize
the value of its currency – by looking simply at
variation in the exchange rate.
E.g. Var(ΔE) for 1980-86 A$ > 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.
14
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.
15
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.
16
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.
17
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.
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 nonbasket 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; BénassyQuéré (1999), the $; Frankel, Schmukler and Luis Servén (2000), a GDP-weighted basket of 5 major currencies; and
Yamazaki (2006), the Canadian $.
19
[2] assuming weights add to1, and no error term, constant term, or other non-currency variable.
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
20
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 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.
21
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.
22
A first official basket pegger
which 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.
23
A 2nd official basket pegger
also 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.
24
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 $.
25
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 .
26
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).
27
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.
28
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 18year 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 or29so.
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.
30
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
31
share of Monetary Base.
Current applications
using 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.
32
(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.
33
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)
0.006
0.068**
0.019
0.049**
0.070**
(0.038)
(0.027)
(0.026)
(0.020)
(0.028)
-0.023
0.020*
0.044***
-0.030
-0.022
(0.035)
(0.011)
(0.017)
(0.019)
(0.027)
0.000**
0.000
0.001***
0.000
0.000
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
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
eur
jpy
Constant
Observations
34
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
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
usd
jpy
eur
Δ emp
Constant
06M11
07M2
07M3
08M3
08M5
0.909***
0.756***
0.756***
0.613***
0.597***
(0.147)
(0.105)
(0.067)
(0.171)
(0.130)
-0.015
-0.095
-0.140
0.059
0.030
(0.098)
(0.085)
(0.089)
(0.081)
(0.083)
0.029
0.116
0.169**
0.357**
0.397***
(0.117)
(0.096)
(0.068)
(0.143)
(0.105)
0.137
0.179***
0.187***
0.290***
0.249**
(0.100)
(0.047)
(0.029)
(0.076)
(0.097)
-0.001
-0.003*
-0.004***
-0.006
-0.004
(0.001)
(0.003)
(0.003)
12
12
12
0.975
0.967
0.966
0.215
-0.030
-0.024
(0.003)
Observations
12
R-squared
0.984
krw
0.077
(0.002)
12
0.967
35
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 2008February 2009, estimates show that all the weight
36
has once again fallen on the $.
The weights in the RMB basket
shifted
toward € in 2007,
back to $ in 2008.
37
RMB was roughly flat against
basket of ½-$ + ½-€ in 2007;
$ in 2008 (like 2005) .
0.16
Appreciation vs. $ was due to weight
on € during period of $ weakening
0.15
0.14
RMB valued in terms of $
RMB valued in terms of ½$+½€
0.12
basket
0.11
0.1
0.09
RMB valued in terms of €
EUR/RMB
USD/RMB
½$+½€ basket
4/1/2009
2/1/2009
12/1/2008
10/1/2008
8/1/2008
6/1/2008
4/1/2008
2/1/2008
12/1/2007
10/1/2007
8/1/2007
6/1/2007
4/1/2007
2/1/2007
12/1/2006
10/1/2006
8/1/2006
6/1/2006
4/1/2006
2/1/2006
12/1/2005
10/1/2005
8/1/2005
0.08
6/1/2005
/RMB
0.13
38
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.
39
(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.
40
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)
0.480**
0.334***
0.019
-0.073
0.218
-0.294
(0.165)
(0.060)
(0.340)
(0.173)
(0.233)
(0.230)
0.602**
0.522***
0.226
0.774***
0.738**
0.886**
(0.274)
(0.091)
(0.277)
(0.180)
(0.343)
(0.347)
USD
EUR
Δ(emp)
Observations
GBP
0.160
0.447*** 0.688*** 0.931***
0.858*** 0.650***
(0.110)
(0.049)
(0.091)
(0.062)
(0.076)
(0.203)
21
22
14
15
20
21
-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
41
Turkey: Evolution of Basket Weights,
Quarterly Regressions with Weekly data
(1)
VARIABLES
JPY
USD
EUR
Δ(emp)
Observations
GBP
(3)
(5)
(7)
7/2007
1/2008
7/2008
-0.660
-0.250
-0.758**
0.899
(0.329)
(0.166)
(0.255)
(0.803)
0.397
-0.036
0.912
-0.961
(0.930)
(0.302)
(0.500)
(0.920)
0.906
1.931***
0.566
-0.088
(0.863)
(0.260)
(0.371)
(0.738)
0.149
0.231***
0.798***
0.825**
(0.083)
(0.056)
(0.204)
(0.279)
10
14
13
10
1/2007
0.357
-0.644
0.281
1.151
42
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)
43
Identifying
Break
Points in
China’s
Exchange
Rate
Regime
(1)
(2)
(3)
(4)
(5)
(6)
VARIABLES
1/6/20057/15/2005
7/29/20054/27/2007
5/4/200711/16/2007
11/23/20079/8/2008
9/15/200812/8/2008
12/15/20083/11/2009
US dollar
1.000***
0.893***
0.596***
0.685***
0.965***
0.929***
(0.000)
(0.030)
(0.066)
(0.091)
(0.058)
0.000
0.046*
0.241***
0.128
0.037
(0.000)
(0.025)
(0.050)
(0.082)
(0.049)
-0.000
0.014
0.059**
-0.065**
0.010
(0.000)
(0.013)
(0.022)
(0.025)
(0.021)
0.000
0.034
0.185***
0.165
0.042
(0.000)
(0.024)
(0.052)
(0.125)
(0.063)
-0.000
0.000
0.000
-0.000
0.000
(0.000)
(0.000)
(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
euro
Jpn yen
With weekly
exchange rate
data and
monthly
reserve data
(interpolations
are made to
get weekly
reserve data)
Δemp
Constant
(0.101)
0.087
(0.077)
0.063
(0.038)
0.129**
(0.060)
0.000
(0.001)
44
Note: *** p<0.01, ** p<0.05, * p<0.1 Robust standard errors in parentheses
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.
45
46
Appendix 1: Correlations Among
Regime Classification Schemes
IMF
GGW
LY-S
R-R
IMF
1.00
GGW
LY-S
R-R
(100.0)
0.60
1.00
(55.1)
(100.0)
0.28
0.13
1.00
(41.0)
(35.3)
(100.0)
0.33
(55.1)
0.34
0.41
1.00
(35.2)
(45.3)
(100.0)
(Frequency of outright coincidence, in %, given in parenthesis.)
GGW =Ghosh, Gulde & Wolf. LY-S = Levy-Yeyati & Sturzenegger. R-R = Reinhart & Rogoff
Sample: 47 countries.
From Frankel (2004).
47
Appendix 2: Monte Carlo study
on 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.
48
Monte Carlo exchange rate under
simulated basket+band regime
-.45
-.4
-.35
-.3
-.25
-.2
(with parameters from Papual New Guinea)
240
260
280
300
320
340
T
mcsa
upperb
mcsa_non
lowerb
49
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.
50
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 $.
51
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.
52
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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