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Does exchange-rate volatility affect importflows in G-7 Countries? Evidence fromcointegration modelsA. C. Arize & S. S. ShwiffPublished online: 04 Oct 2010.

To cite this article: A. C. Arize & S. S. Shwiff (1998) Does exchange-rate volatility affect import flowsin G-7 Countries? Evidence from cointegration models, Applied Economics, 30:10, 1269-1276, DOI:10.1080/000368498324887

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1 Recently, Engel and Hakkio (1993) noted that the perception is widespread that foreign exchange rates are too volatile ¼ indeed, someobservers recommend that the United States, Japan, and Germany abandon their ¯ exible exchanges and adopt a target zone system inorder to reduce exchange-rate volatility.’2 Arize (1995) and Chowdhury (1993), among others, provide recent reviews and empirical results.3 For space and time reasons, we have not done the additional investigation of the short-run dynamics in an error-correction setting.According to the Granger Representation theorem developed in Engle and Granger (1987, p. 255), presence of cointegration implies theexistence of an error-correction model. It is important to establish whether cointegration exists prior to attempting to model the short-rundynamics.

Applied Economics, 1998, 30, 1269 ± 1276

Does exchange-rate volatility a� ect import¯ ows in G-7 Countries? Evidence fromcointegration models

A. C. ARIZE and S. S . SHWIFF

College of Business and T echnology, T exas A&M University ± Commerce, Commerce,T exas 75429, USA

This paper provides new evidence on the long-run relationship between imports andexchange-rate volatility in G-7 countries. The period examined is 1973 :2 through1995 :1. Cointegration analyses are based on Johansen’s (1991, 1994) approach androbust single-equation methods of Stock and Watson (1993) and Phillips and Loretan(1991). In conformity with theoretical considerations, the results indicate thatexchange-rate volatility has a signi® cant negative e� ect on the volume of imports ofmost G-7 countries whereas for Canada, it is positive and signi® cant. These ® ndingsare reasonably robust in terms of measures of exchange-rate volatility and di� erentestimation methods.

I . INTRODUCTION

There has been widespread concern among ® nancial marketparticipants, trade economists, the popular press, andpolicymakers over the high degree of volatility of mostmajor exchange rates since the inception of ¯ oating rates inMarch 1973.1 Much of this concern stems from the adversee� ects of increased uncertainty from high volatility in ex-change rates on international trade. Work by De Grauwe(1988, p. 63) notes that the growth rate of internationaltrade among industrial countries has declined by more thanhalf since the inception of ¯ oating rates.’ A number ofstudies also have examined this issue, and most conclude infavour of the existence of a negative and statistically signi® -cant relationship between exchange-rate volatility and

export ¯ ows.2 Except for Kenen and Rodrik (1986) andKoray and Lastrapes (1989), however, little attention hasbeen devoted to assessing the extent of such volatility onimport ¯ ows.

The main objective of this analysis, which focuses exclus-ively on the long-run, is to provide new evidence on thee� ects of exchange-rate volatility on the imports of G-7countries ± the United States, the United Kingdom, Japan,Italy, Germany, France and Canada.3 The evidence present-ed will add a dimension to this literature and determine theextent to which the conclusions reached by previous authorsmay be con® rmed by applying recently developed tech-niques of time-series analysis.

The approach followed in this paper is di� erent in severalways from that of previous papers in this area. First, it

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4 A number of authors (e.g., Campbell and Perron, 1991; Phillips and Loretan, 1991; Stock and Watson, 1993; Haug, 1996) give reasons forconsidering possibly more robust single-equation estimation methods.5 For example, on the US import side, prior to the September 1985 exchange-rate conference at the New York Plaza Hotel, there was anextended rise in the dollar, which resulted in a group of new foreign producers selling goods in the US market. Once some of theseproducers had met the ® xed cost of setting up distribution networks, gaining brand name recognition, and so forth, they were unwilling tostop exporting to the US market when exchange rates returned to their initial levels.

uses a longer and more recent sample period, 1973Q2through 1995Q1. In general, the studies by Kenen andRodrik (1986) and Koray and Lastrapes (1989) examinedthe period 1973 through 1985, and it is possible that tradeelasticities underlying recent data may be di� erent fromthose found in these earlier studies, especially in view of thehigh degree of volatility and uncertainty of exchange-ratemovements in the modern periods of the generalized ¯ oat-ing rates.

Secondly, we explicitly test the time-series propertiesof the variables in this study. The speci® cations used byKenen and Rodrik (1986) as well as Koray and Lastrapes(1989) implicitly assumed data stationarity. However, if thisassumption is incorrect, inferences made concerning in-come, price and exchange-rate volatility are potentiallyhighly misleading. Therefore, this paper focuses upon thecorrect representation of the data to avoid misleading in-ferences.

Thirdly, the study examines whether real imports arecointegrated with its determinants. Unlike all other studiesin this literature, this one represents the ® rst applicationof a system as well as robust single-equation tests for co-integration. We carried out cointegration tests based onJohansen’s (1991, 1994) FIML (Full Information MaximumLikelihood) approach and robust single-equation methodssuggested by Stock and Watson (1993) and Phillips andLoretan (1991).4 The advantage of carrying out cointegra-tion testing is that it provides evidence on the existence ofa stable long-run linear relationship among real imports,real income, relative prices and exchange-rate volatility,which is in itself interesting from a theoretical perspective.Furthermore, it is now known that evidence of cointegra-tion implies that the resulting parameter estimates are notsubject to the spurious regression phenomenon’ ® rst de-scribed in Granger and Newbold (1974).

Finally, we compare our results with those obtained byprevious authors. This comparison would provide usefulguidance in drawing conclusions regarding any general hy-pothesis.

The rest of the paper is organized as follows: Section IIcontains a brief discussion of the theory and methods usedto study the short- and long-run e� ects of exchange-ratevolatility on real import demand. Section III represents anddiscusses the empirical results. Section IV contains majorconclusions of the paper. The data used are quarterly andcover the period 1973Q2 through 1995Q1. Details of thedata de® nition and sources are presented in Appendix A.

II . THEORETICAL CONSIDERATIONS ANDMODEL SPECIFICATION

Import demand model

A traditional speci® cation of the long-run equilibrium im-port demand in the ¯ exible exchange-rate environment isthat of Gotur (1985); empirical evidence in favour of thisspeci® cation was presented by Kenen and Rodrick (1986);

V Mdt = t o + ft ¡ + t 3 ´ s (hi)t + e t (1)

where V Mdt denotes the logarithm of desired real imports,

s (hi)t is the logarithm of a moving sample standard devi-ation, ft comprising traditional variables (the logarithm ofthe gross domestic product in constant prices (yt) and thelogarithm of relative prices, proxied by the ratio of importprices to the domestic price level (pt)) and e t is a disturbanceterm.

Standard demand theory concerning the e� ects of realincome and relative prices on import demand is well knownand needs no elaboration here (see Arize and Ndubizu, 1992for details). Regarding the e� ects of exchange-rate volatility,it has been argued that higher exchange-rate volatility leadsto higher import cost for risk-averse traders and to lessforeign trade. This is because the exchange rate is agreed onat the time of the trade contract, but payment is not madeuntil the future delivery actually takes place. If changes inexchange rates become unpredictable, this creates uncer-tainty about the pro® ts to be made and, hence, reduces thebene® ts of international trade. Sercu (1992, p. 579) notesthat the argument views traders as bearing undiversi® edexchange risk; if hedging is impossible or costly and tradersare risk-averse, risk-adjusted expected pro® ts from tradeshould fall when exchange risk increases’ .

On the other hand, recent theoretical developments sug-gest that there are situations in which the volatility ofexchange rate could be expected to have either negative orpositive e� ects on trade volume. De Grauwe (1988) hasemphasized that the dominance of income e� ects over sub-stitution e� ects can lead to a positive relationship betweentrade and volatility.

Theoretical models of hysteresis in international trade(see Baldwin and Krugman, 1989, p. 635 and Dixit, 1989,p. 206) have shown that increased uncertainty from highvolatility in exchange rates can also in¯ uence foreign trade.Hysteresis refers to e� ects that continue steadily after theconditions that brought them about have been removed.5

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6 There may be sunk’ costs for research and development (R&D), relocation, marketing e� orts and capital investment.7 Following the suggestion of the referee, the BLS results are not reported here.

Although these theoretical models show that trade ¯ owsare characterized by hysteresis, they also suggest that hys-teresis in trade ¯ ows can be explained by the combination ofsunk costs (that are nonrecoverable by a foreign ® rm uponexit) and exchange-rate uncertainty.6 A foreign ® rm’s abilityto enter or exit is linked to the levels of exchange rate. Insum, their work suggests that, when signi® cant sunk costsare involved in international transactions, exchange-rateuncertainty can a� ect trade behaviour, including that ofrisk-neutral ® rms. However, it is di� cult to identify whichway trade will be a� ected. Dixit (1989) also shows that, inthe presence of sunk costs, the hysteresis band widens as theexchange rate becomes more volatile. In such a case, uncer-tainty engenders a wait-and-see attitude among agents.Therefore, trade can be a� ected because neither entry norexit of ® rms occurs.

To summarize, the discussion above suggests that theimpact of exchange-rate volatility on foreign trade is anempirical issue, because theory alone cannot determine thesign of the relation between foreign trade and exchange-ratevolatility.

Before presentation of the empirical results, four technicalnotes regarding Equation 1 and the method of estimationare in order. First, to make Equation 1 estimatable, wemake the assumption that, in the long-run, any deviation ofactual (observable) from desired (unobservable) real importsshould have disappeared (i.e., VMd

t = VMt).Second, it is necessary to derive an operational measure

of exchange-rate uncertainty. In this paper two measuresare used. The ® rst measure incorporates deviations of indi-vidual observations of real e� ective exchange-rate , Rt, fromthe predicted value, RÃ t, obtained from ® tting a fourth-orderautoregressive process. The proxy de® ned as s (h1 )t is cal-culated as an eight-term moving average deviation aroundthe predicted values of exchange rate. Speci® cally, ifDEV t = Rt - RÃ t , then s (h1 )t is calculated as

s (hi)t = ln 3 17

+i = 0

(Devt ± i)2 /82 4

0 . 5

(2)

where ln is the natural logarithm. The second measure,s (h2 )t , is obtained in a similar fashion, using the predictedvalues of the change in real e� ective exchange rate betweenquarter t - 1 and t. Both of the measures were utilized forall countries in an attempt to determine whether the rela-tionship between import demand and variation in theexchange rate was consistent across measures or countries.

The next step is to test the null hypothesis of no cointe-gration (against the alternative of cointegration) usingJohansen’s multivariate cointegration technique. The tech-nique provides two likelihood-ratio (LR) statistics for the

number of cointegrating vectors: the trace and the max-imum eigenvalue (l -max) statistics.

We have estimated our cointegration model under threealternative speci® cations (see Johansen, 1994) for the deter-ministic components of the system, namely H1 (r), H*1 (r) andH0 (r). Model H1 (r) allows for the presence of deterministictrends in the data, but the r cointegrating vectors annihilatethese stochastic and linear trends. Thus, the model allowsfor a linear trend in each variable, but not in the cointegrat-ing relations. Model H*1 (r) allows for no deterministic trendsin the data, but does allow a nonzero mean of the equilib-rium relationship (a constant in the cointegrating vector).Finally, model H0 (r) restricts the mean of the equilibriumrelationship to be zero (a constant term is not allowed in thecointegrating vector). Since models H*1 (r) and H0 (r) arenested within H1 (r) , we have tested these restrictions usingthe likelihood-ratio procedure described in Johansen (1994).We stopped the ® rst time the null hypothesis was rejected.Speci® cally, the test of H*1 (r) in H1 (r) was performed and, ifwe failed to reject the null hypothesis, then the test of H0 (r)in H*1 (r) was performed.

III . EMPIRICAL RESULTS

A prerequisite in applying the cointegration procedure is totest the unit root properties of the data; Table 1 presentsstatistics describing these properties of the data. We use twosets of statistics: (i) augmented Dickey± Fuller (ADF) t-stat-istics and (ii) 95% con® dence intervals for the largestautoregressive root. (These were constructed from the ADFstatistics using Stock’s (1991) procedure.)

The test proposed by Banerjee, Lumsdaine and Stock(BLS) (1992) show that with the exception of the real GDPand exchange-rate uncertainty proxies for the United King-dom, the calculated BLS test statistics are all greater thanthe critical values reported in BLS (1992, Table 1).7 Theseresults support the null hypothesis of no structural break.As a result of these ® ndings, we have estimated (below) thecointegration model for the United Kingdom using Indus-trial Production as the scale measure and a ® ve-termmoving standard deviation of exchange rate. These variablespassed all the unit root tests mentioned above. Table 2 re-ports the Johansen Cointegration tests results.

Focusing on the l -max test results, the null hypothesisr = 0 (no cointegration) is rejected in favour of r = 1 in eachcountry. The calculated test statistics range from a low of26.27 in Japan to a high of 44.42 in Germany. The criticalvalue from Osterwald-Lenum (1992) is 27.07 in all but two(Japan and France) at the 5% level. For Japan, the nullhypothesis r = 0 is rejected at the 10% level and the critical

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Table 1. Augmented Dickey± Fuller tests, and con® dence intervals for the largest autoregressive root

Variable N Lags ADF C 4 j 1 90% con® dence interval

USA V Mt 81 1 ± 6 - 2.74 6.35 1.78 (0.758, 1.035)yt 82 1 ± 8 - 3.11 2.96 1.71 (0.691, 1.029)pt 82 1 ± 5 - 1.86 2.10 0.38 (0.881, 1.049)s (h1 )t 75 1 ± 2 - 1.93 0.55 0.64 (0.870, 1.054)s (h2 )t 74 1 ± 2 - 1.93 4.47 0.24 (0.868, 1.055)

UK V Mt 82 1 ± 5 - 2.80 3.45 0.01 (0.745, 1.030)yt 81 1 ± 7 - 2.87 2.41 0.16 (0.724, 1.030)pt 83 1 ± 5 - 1.57 2.26 0.21 (0.827, 1.040)s (h1 )t 72 1 ± 8 - 3.26 4.51 0.22 (0.604, 1.020)s (h2 )t 71 1 ± 8 - 3.04 4.81 1.43 (0.664, 1.040)

Japan V Mt 80 1 ± 8 - 1.36 0.74 0.84 (0.942, 1.055)yt 76 1 ± 12 - 1.94 6.40 2.20 (0.856, 1.050)pt 87 1 ± 1 - 2.79 2.85 0.05 (0.760, 1.030)s (h1 )t 69 1 ± 8 - 3.02 2.89 0.11 (0.654, 1.040)s (h2 )t 68 1 ± 8 - 2.07 1.21 0.35 (0.823, 1.060)

Italy V Mt 81 1 ± 7 - 2.62 1.55 2.82 (0.776, 1.040)yt 74 1 ± 5 - 2.00 2.77 0.01 (0.853, 1.050)pt 83 1 ± 5 - 1.68 5.11 3.06 (0.908, 1.051)s (h1 )t 69 1 ± 7 - 0.95 1.62 0.34 (0.983, 1.070)s (h2 )t 67 1 ± 6 - 1.86 0.58 1.48 (0.854, 1.060)

Germany V Mt 83 1 ± 4 - 2.69 7.02 0.01 (0.765, 1.036)yt 81 1 ± 6 - 2.51 4.49 0.41 (0.797, 1.041)pt 82 1 ± 5 - 1.28 7.66 0.73 (0.954, 1.054)s (h1 )t 74 1 ± 2 - 1.21 3.42 0.24 (0.962, 1.060)s (h2 )t 73 1 ± 3 - 1.10 7.01 0.62 (0.973, 1.062)

France V Mt 74 1 ± 9 - 2.64 5.59 0.08 (0.754, 1.040)yt 80 1 ± 8 - 2.16 7.00 0.10 (0.853, 1.050)pt 81 1 ± 7 - 1.46 6.36 0.06 (0.931, 1.050)s (h1 )t 75 1 ± 2 - 1.17 1.12 0.16 (0.962, 1.060)s (h2 )t 72 1 ± 4 - 1.03 2.45 0.06 (0.984, 1.050)

Canada V Mt 83 1 ± 5 - 1.84 5.12 3.06 (0.896, 1.050)yt 83 1 ± 5 - 1.76 2.98 0.34 (0.896, 1.050)pt 81 1 ± 7 - 1.38 4.10 0.17 (0.943, 1.054)s (h1 )t 72 1 ± 5 - 1.61 1.48 0.01 (0.909, 1.060)s (h2 )t 72 1 ± 4 - 2.02 2.87 0.05 (0.848, 1.055)

Notes: The Dickey± Fuller regression includes a constant and a time trend. N denotes the number of observations. Lags denotesthe included augmentation lags. ADF is the augmented Dickey ± Fuller (1981) test. The critical value for ADF is approximately- 3.46 (MacKinnon, 1991). C 4 is the Breusch and Godfrey (1981) test for serial correlation (AR/MA). The critical value for

x 2 (4) is 9.48. j 1 is the Koenker and Bassett (1982) test for heteroscedasticity. The critical value for x 2 (1) is 3.84. The lags areobtained using Ng and Perron (1995) method.

8 Johansen and Juselius (1990, p. 192) point out that it seems reasonable in certain cases to follow a test procedure which rejects for higherp-value than the usual 5 per cent.’

value is 25.56.8 In the case of France, the null hypothesis ofno cointegration is rejected at the 5% level and the criticalvalue is 28.14. Furthermore, the null hypotheses ofr < 1, r < 2 and r < 3 cannot be rejected in favour ofthe alternative hypotheses of r = 2, r = 3 and r = 4, respec-tively. These results indicate the presence of one cointegrat-ing relationship for each country. In sum, this ® nding sug-gests that there is a long-run equilibrium relationship

among real imports, domestic economic activity, relativeprice and exchange-rate volatility in all the countries in oursample.

Panel A of Table 3 presents the estimated Johansen’smultivariate cointegrating vectors. The estimated cointe-grating vectors are given economic meaning by normalizingon real imports. The normalized equations are obtained bydividing each cointegrating vector by the negative of the

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Table 2. Multivariate cointegration tests

Maximum eigenvalue Trace statistics

H0 : r = 0 r < 1 r < 2 r < 3 r = 0 r < 1 r < 2 r < 3Country Ha: r = 1 r = 2 r = 3 r = 4 r > 1 r > 2 r > 3 r = 4

USA 41.38 13.69 5.01 0.54 70.63 29.25 5.56 0.54UK 29.87 10.82 7.70 1.00 49.40 19.53 8.70 1.00Japan* 26.27 7.33 3.83 2.45 39.87 13.60 6.27 2.45Italy 33.71 20.77 7.76 0.02 62.26 28.55 7.78 0.02Germany 44.42 7.57 4.70 0.07 56.75 12.34 4.77 0.07France* 39.62 4.80 4.50 0.80 49.72 10.10 5.30 0.80Canada 37.31 12.16 2.09 0.29 51.84 14.53 2.37 0.29

Critical values5% 27.07 20.97 14.07 3.76 47.21 29.68 15.41 3.765%* 28.14 22.00 15.67 9.24 53.12 34.91 19.96 9.24

10%* 25.56 19.77 13.75 7.53 49.65 32.00 17.85 7.53

Note: r denotes the number of cointegrating vectors. The critical values are for 5% and 10% level of signi® cance(Osterwald± Lenum, 1992). The LR test values are corrected for small sample biases using Reinsel and Ahn’s (1992)procedure. The lag order of the VAR system used is two in the USA, Italy, Germany and Canada; four in the UK and ® vein France and Japan. With these lags, all VAR models pass the Breusch-Godfrey test for white error.

Table 3. L ong-run import demand estimates for G-7 countries

TestCountry Normalized cointegrating vector H0 : t 3 = 0

A. Johansen estimatesUSA V Mt = 1.88 yt - 0.64 pt - 0.05 s (h1 )t 8.956 (0.003)UK V Mt = 2.08 yt - 0.61 pt - 0.36 s (h1 )t 23.744 (0.000)Japan V Mt = 0.98 yt - 0.20 pt - 0.34 s (h1 )t 21.556 (0.000)Italy V Mt = 1.11 yt - 0.42 pt - 0.09 s (h1 )t 4.748 (0.029)Germany V Mt = 1.95 yt - 0.15 pt - 0.01 s (h1 )t 0.229 (0.632)France V Mt = 1.16 yt - 0.07 pt - 0.13 s (h1 )t 40.574 (0.000)Canada V Mt = 1.93 yt - 0.56 pt - 0.25 s (h1 )t 11.235 (0.001)

B. Stock and Watson estimatesUSA V Mt = 1.67 yt - 0.85 pt - 0.04 s (h1 )t 1.72 (0.056)UK V Mt = 2.36 yt - 0.78 pt - 0.08 s (h1 )t 2.30 (0.013)Japan V Mt = 0.91 yt - 0.27 pt - 0.13 s (h1 )t 1.80 (0.038)Italy V Mt = 1.00 yt - 0.49 pt - 0.10 s (h1 )t 2.07 (0.023)Germany V Mt = 1.98 yt - 0.19 pt - 0.00 s (h1 )t 0.12 (0.452)France V Mt = 1.61 yt - 0.04 pt - 0.03 s (h1 )t 1.53 (0.066)Canada V Mt = 1.86 yt - 0.54 pt - 0.05 s (h1 )t 1.69 (0.048)

C. Phillips and Loretan estimatesUSA V Mt = 1.87 yt - 0.60 pt - 0.06 s (h1 )t 2.71 (0.009)UK V Mt = 2.26 yt - 0.68 pt - 0.09 s (h1 )t 3.86 (0.000)Japan V Mt = 1.01 yt - 0.25 pt - 0.27 s (h1 )t 3.59 (0.001)Italy V Mt = 1.12 yt - 0.43 pt - 0.08 s (h1 )t 1.85 (0.043)Germany V Mt = 1.87 yt - 0.21 pt - 0.02 s (h1 )t 0.68 (0.251)France V Mt = 1.60 yt - 0.11 pt - 0.09 s (h1 )t 1.81 (0.018)Canada V Mt = 1.82 yt - 0.62 pt - 0.21 s (h1 )t 2.13 (0.019)

Note: For the Johansen estimates, the test H0 : t 3 = 0 in the equation V Mt =t 0 + t 1 yt + t 2 pt + t 3 s (h1 )t has a x 2 (1) distribution under the null hypothesis. The critical valueat the 5% level is 3.84, whereas it is 2.71 at the 10% level. For Stock and Watson and Phillipsand Loretan estimates, the test H o : t 3 = 0 in the equation V Mt = t 0 + t 1 yt + t 2 pt + t 3 s (h1 )thas t-distributions, respectively, under the null hypothesis. All the values in parentheses are thep-values.

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9 Following a referee’s suggestion, the results employing s (h2 )t are not reported in this version of the paper because they are similar to thosereported in the text.

estimated real imports coe� cient. These normalized equa-tions yield estimates of the long-run equilibrium elasticities.

As can be seen in Panel A, The estimated domestic eco-nomic activity (proxied by either real income or industrialproduction) elasticity carries the expected positive sign in allcountries in our sample. In six of the seven countriesstudied, the elasticity estimates are greater than unity, andthey range from 1.11 to 2.08. The exception is Japan, whereit is only 0.98. This implies a fairly large response of importsto changes in domestic economic activity. Furthermore, testresults obtained by excluding domestic activity from thecointegration space (see Johansen and Juselius, 1990, p. 194)indicate that, in all cases, the domestic activity term issigni® cantly di� erent from zero at better than the 5% level.The estimated price elasticity has the expected negative signin all countries, and the estimates range from - 0.07 to- 0.64. For six of the seven countries studied, the price

elasticity is signi® cant at better than the 10% level. Theexception is France, where it is signi® cant at the 12% level.

An appealing aspect of the results is that exchange-rateuncertainty elasticity has a negative sign in the results of sixcountries ± the United States, the United Kingdom, Japan,Italy, Germany and France. The estimates range from- 0.01 to - 0.36. For Canada, it carries a positive sign, and

the coe� cient is + 0.25. A test of the null hypothesis thatthe exchange-rate uncertainty is zero (i.e., H0 : t = 0) isreported in Panel A. The computed values for six of thecountries are greater than the critical value of 3.84 at the 5%level. In the case of Germany, the estimated coe� cient onexchange-rate uncertainty is nonsigni ® cant. Re-estimatingthe equation for Germany without this variable did notchange the magnitude of either the scale variable or theprice variable. Taken together, our results from employingJohansen’s multivariate procedure point to an importantrole for exchange-rate uncertainty in determining the im-port-demand behaviour of G-7 countries.9

A check for the robustness of results

The most delicate part of the Johansen procedure is that theestimates of one equation may be sensitive to possible mis-speci® cation in another equation. To ensure that the con-clusions are fully coherent with the data, estimates of thecointegrating relations are obtained using both the Stockand Watson (1993) dynamic OLS procedure (DOLS) ap-proach and the Phillips and Loretan (1991) fully parametric(FP) procedure. The DOLS estimates with t-ratios based onrobust standard errors are presented in Panel B. The robuststandard errors are due to Newey and West (1987). PanelC contains estimates obtained from the Phillips andLoretan method. It is clear from the data that, in all cases,the sign and statistical signi® cance of the coe� cients were

very similar to those from the Johansen procedure. All in all,it is encouraging that the conclusions are not particularlya� ected by the method of estimation.

IV. COMPARISON WITH OTHER STUDIESAND CONCLUSIONS

It seems prudent to compare our results with those of earliersimilar studies of G-7 import-demand behaviour. Our em-pirical evidence corroborates the ® ndings of Kenen andRodrik (1986) and could be considered as adding strongersupport to the conclusions of Koray and Lastrapes (1989),who note that the e� ect of volatility on imports is weakalthough permanent shocks to volatility do have a negativeimpact on this measure of trade, and those e� ects are rela-tively more important over the ¯ exible rate period.’ Thework by Kenen and Rodrik (1986) examines the e� ects ofvarious measures of real e� ective exchange-rate volatility onmanufactured imports of eleven major industrialized coun-tries, using data solely from the post-Bretton Woods era.For the G-7 countries, their study reports activity elasticitiesof 1.19, 2.36, 1.14, 2.14, 1.08, 0.55 and 2.09 for the UnitedStates, the United Kingdom, Japan, Italy, Germany, Franceand Canada, respectively. All of these estimates (excludingthose for Italy and France) are generally consistent withthose reported here. In a similar vein, for the United States,the United Kingdom, Japan and Canada, they report priceelasticities of - 0.81, - 0.59, - 0.23 and - 0.89, respec-tively. These elasticities are consistent with the presentstudy. However, their price elasticity of - 2.55 for Italy,- 0.01 for Germany and - 1.39 for Italy di� er markedly

from the results reported here.Kenen and Rodrik (1986) report exchange-rate uncer-

tainty estimates of - 14.9, - 8.84, 3.04, 2.39, - 8.47,- 3.65 and - 5.15 for the United States, the United King-

dom, Japan, Italy, Germany, France and Canada, respec-tively. While our results support, at least in spirit, theconclusions of their study, mention should be made thattheir estimates are semi-elasticities, whereas those of thepresent study are elasticities; in addition, our results forJapan, Germany, Canada and Italy contrast with those ofKenen and Rodrik (1986).

This paper has provided a re-examination of the relation-ship between import ¯ ows and exchange-rate uncertaintyfor the G-7 countries over the quarterly period 1973± 95.This empirical analysis was characterized by two importantelements. First, the time-series properties of each variableare examined by unit root tests and Stock’s (1991) con® -dence interval method. Su� cient attention to the stochasticproperties of the data is important for appropriate for-mulation of a time-series model. Second, estimates of the

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cointegrating relations are obtained using Johansen’s (1991,1994) FIML (Full Information Maximum Likelihood) ap-proach. As a crosscheck, we also provide estimates fromStock and Watson’s (1993) DOLS procedure and the Phil-lips and Loretan approach.

The empirical evidence reported in this paper supportsthe position that there is a signi® cant long-run negativee� ect of exchange-rate uncertainty on the volume of importsin all G-7 countries (except Germany and Canada).

ACKNOWLEDGEMENTS

The authors would like to thank Ed Manton, Keith McFar-land and Lee Schmidt for helpful comments on an earlierdraft. We greatly appreciate the comments from NicoleMain (the editorial assistant) and a referee that signi® cantlyimproved the paper. The excellent research assistance ofKathleen Smith is also acknowledged. This research isfunded by a GSRFTAMU-C grant.

REFERENCES

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APPENDIX: DATA, DEFINITION ANDSOURCES

This appendix describes the raw data, sources and construc-tion of variables used in the empirical tests. All data wereobtained from the IMF’s International Financial Statistics

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(IFS), IMF’s Central Statistics O� ce, OECD Main Eco-nomic Indicators and Directions of Trade (DOT). Data forindividual countries’ real GDP, import volume, and unitvalues are taken from IFS. Data for US real income arefrom the Federal Reserve Bank of St Louis, the base periodis 1990 = 100. The relative price ratio was calculated asPt = ln PMt - ln Pdt where PM is import prices or unitvalue in local currency, and Pd is consumer price index. Tocompute measures for exchange-rate volatility, trade-weighted e� ective exchange rate (eer) and real e� ectiveexchange rate (R) were computed. For example, for theUK they were constructed as follows: the period averageexchange rates are in units of domestic currency perdollar. These period averages were then set in indexform (1990 = 1.0). The eer variable was calculated as:EXP[+ wj i ln E (i, $, t) - ln E (J, $, t)] where EXP = expo-

nent, ln = natural logarithm, E (i, $, t) = exchange-rate in-dex of country i at time t and E (J, $, t) = exchange-rateindex of UK at time t. Real e� ective exchange rate wascalculated as: R (J, t) = EXP[ - ln P (J, t) + ln E (J, $, t) ++ wj i ln P (i, t) - + wj i ln E (i, $, t)] where J stands for the UKand the exchange rate terms are in units of J or i currencyper US dollars in index form (1990 = 1.0). P is the consumerprice index of country J or i in index form (1990 = 1.0). Thetrading partners are (Australia, Austria, Belgium, Canada,Switzerland, Finland, Germany, Ireland, Spain, France,Japan, Netherlands, Norway, Sweden, Italy, USA). Notethat wj i is the share of UK imports from trading partner (i).That is, wj i = country j’s annual trade (imports) with coun-try i divided by country j’s annual trade (imports) with therest of the ® fteen countries over the 1973 ± 94 period. There-fore, + wj i = l and wj j = 0.

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