Transcript A meta-analysis of the effect of Common Currencies on

Andrew k. Rose and T.D. Stanley
Presented by:
María del Carmen Ramos Herrera.
Introduction
Meta- Analysis across studies
Publication Selection and MetaRegression Analysis
.
Conclusions
 My suggestions
Introduction:
The purpose of this paper is to use meta-analysis method to summarize,
investigate and more accurately estimate the common-currency trade
effect.
Meta-analysis can improve the assesment of this important economic
parameter by combining all of the estimates, investigating the sensitivity
of the overall estimate to variations in underlying assumptions,
identifying and filtering out publication bias and later on use
metaregression analysis( MRA).
.
This meta-analysis confirms a robust, economically important positive
trade effect from monetary union.
The current interest in the trade effect of common currencies began
with Rose (2000)
A panel of cross-country data covering bilateral trade between 186
different trading partners at 5-intervals between 1970 and 1990.
Since most of the variation is across pairs of countries rather time, Rose
uses a “gravity model of trade”.
This resulting equation for assesing trade effects is the following:
.
Tijt : the natural logarithm of trade between countries i and j at time t.
β : set of nuisance coefficients
 Dij: the log of the distance between i and j
 Y: the log of real GDP
 Z: other controls for bilateral trade
 CUijt: dummy
variable (currency union at t)
.
 U: well-behaved disturbance term
∂ : partial effect of currency union on trade (ceteris paribus)
 The surprising and interesting finding is that currency union seemed
to have a very large effect on trade.
 The coefficient for a currency union dummy variable has a point
estimate of around 1.2 (Rose 2000).
 This estimate implies that members of currency unions traded over
three times as much as otherwise similar pairs of countries, ceteris
paribus
.
(Why???)
 There was no previous benchmark in the literature, this estimate
seemed implausibly large .
 Almost all the subsequent research in this area has been motivated
by the belief that currency union cannot reasonably be expected to
triple trade.
 Meta- Analysis across Studies:
It is a set of quantitive techniques for evaluating and combining
empirical results from different studies.
 Different point estimates of a given coefficient may be treated as
individual observations.
 Once compiled
The hypothesis that the coefficient is 0
.
To estimate the coefficient
of interest more
accurately
 Rose and Stanley analyze 34 papers and 754 differents
estimates of gamma.
There are a sufficient number of studies that have provided estimates
of the effect of currency union trade and Meta- Analysis seems an
appropiate way to summarize the current state of the literature.
 Most of them are representative and each estimate is
weighted equally.
 The central concern of this method is to test the null
hypothesis: gamma = 0, where all estimates are combined.
 The classic test comes from Fisher and this hypothesis is
easily rejected at standard significance level.
 However, Fisher’s test for overall effect is inappropiate for this and
perhaps all areas of economic research.
Why???
Because:
 It is quite strict
 It is unlikely to be satisfied by empirical
economics.
Then other tests for overall effects are needed. (Table 1).
 We can see: fixed and random effects.
 Manifestly, there is considerable heterogeneity across studies.
 The fixed and random effects estimators differ greatly in
magnitude and their confidence intervals don’t overlap
The smaller fixed effects estimate of gamma indicates
currency union raises trade by 33%
 The random effects estimate indicates
effect is closer to 90%.
 Note that all confidence bounds exceed zero
this average
What does
mean?
Positive trade effect
 Table 2: Reports the fixed- effects estimates for gamma when studies
are omitted from meta-analysis one by one.
 There is little indication that any single studies is especially influential
in driving this result.
 Again, all confidence bounds are positive ( meaning positive trade
efect from monetary union).
 Another important isssue is that heterogeneity is present not only
across studies, but also within most of the individual studies.
 The random effect estimator is one way to accommodate
heterogeneity.
 And MRA is another way to do it.
 Publication
Selection and Meta-Regression
Analysis: (Critiques)
It is possible that these stong findings may be the artifact of selection for
statistical significance (publication bias).
 Publication Selection occurs when researchers, referees or editors have
preference for statistically significant results.
Why???
Because insignificant findings tend to be suppressed.
 The problem with such selection is that it will tend to exaggerate the
magnitude of the empirical effect in question, potentially making negligible
effects appear important.
 Funnel
graphs: (Important tool)
 It is conventional method to identify publication selection.
 It is a scatter diagram of precision (1/standard error(SE))
versus estimated effect.
 In the absence of publication selection, the diagram should
resemble an inverted funnel (wide at the bottom for small-sample
studies, narrowing as it rises)
 Asymmetry is the mark of publication bias.
Figure 2 show s lack of symmetry.
 Funnel graph of 678 individual estimates:
 To corroborate this pictographic identification of publication bias, we use
an MRA of the t-value versus precision.
 Publication bias is typically modelled as:
 The reason behind this model of publication selection begins with the
recognition that researchers will be forced to select larger effects when
the standard error is also large.
 Accounting for likely heterokedasticity leads to the weighted least
squares (WLS) version of the previous equation:
 In the absence of publication selection, beta o, will be zero.
 In table 4:
 Beta 0= 3.85, which is significantly positive, confirming the
asymmetry of the funnel graph.
 And we have a MODERATE corroboration of an authentically
positive common currency effect (t=1.97)
Then, after accommodating publication bias, an economically significant
trade effect of monetary union remains.
Results: A 95% confidence interval for gamma, after correcting for
publication bias, is 0.184-0.586
Trade is increased between 20 and
80%.
 Conclusions:
 First, the hypothesis that there is no trade effect from currency union is
robustly rejected when individual studies are pooled.
 Second, the pooled estimate is not only positive, but also economically
significant
Third, there is evidence of publication bias and after correcting this
problem we will have a lower trade effect from monetary union.
Fourth, as expected, a number of research characteristics are found
to have a significant effect on the reported common currency effect.
 Meta-analysis has an important limitation
If there is a common, systematic bias across the entire
literature, meta-analysis has no way to distinguish it
from an a unique empirical effect.
 My
suggestions:
 First, to clarify Rose and Stanley question:
How much would trade increase when countries have a common
currency?
(In this case, these countries have always shared it)
 Second, I wonder another question:
How much would trade increase if I introduce a new common
currency in these countries ?
(But in this case, these countries have never shared it)
 Finally, we could do this analysis in a dynamic way (paying attention to
the long term)
To see the effect on trade across time when currencies
are created and destroyed.
QUESTIONS AND
COMMENTS
THANK YOU VERY MUCH
FOR YOUR ATTENTION