The annual time series for bilateral export and import flows among regions are from the IMF Direction of Trade data bank. In these series, trade with nonreporting countries, notably the Sino–Soviet group, is excluded both in reporter and in partner records. Special category trade and trade with “countries not specified” are necessarily excluded from regional flows. The latter, but not the former, is included in global totals; importer records of special category trade are relatively incomplete. Data from national sources have been substituted for the DOT partner entries for Malaysia and Singapore for the years 1958–66. The monthly time series for global exports and imports are from the IMF International Financial Statistics data bank. Exports to and imports from nonreporters are included.
The effective U. S. dollar exchange rates calculated for this study are weighted averages of the period–average dollar exchange rates (inverse of line rf in IFS) of the countries in the exporting region (those having exports valued at $0.5 billion or greater in 1975). The weights are the shares of the exporting countries in that region’s total exports to the importing region. The weights vary over time, being linear interpolations of the shares at the beginning and end of the sample period, 1959–60 and 1974–75, respectively. For the “forecasting version” of the monthly equation, the effective exchange rate given in IFS (line amx on the page for the United States) was substituted for the world weighted–average rate just described.
Mr. Hemphill, economist in the Current Studies Division of the Research Department when this study was prepared, is now in the European Department. He is a graduate of Monmouth College (Illinois) and Princeton University.
The author is indebted to colleagues in the Fund for unusually generous interest in, and correspondingly numerous comments on, earlier drafts. The result remains the responsibility of the author.
The asymmetry is similar to the central bank “float” in that both arise because of a transit delay. However, the float refers to a stock of items while the asymmetry is a flow—in effect, the change in the “international trade float” over a specified period.
A standard reference on this topic is John S. Smith, “Asymmetries and Errors in Reported Balance of Payments Statistics,” Staff Papers, Vol. 14 (July 1967), pp. 211–36; see especially p. 223. Smith explains that the lag would not arise if trade data were collected on a balance of payments basis, according to which a shipment is counted as exports by one partner and as imports by the other at the moment when ownership changes. However, in practice, trade data are generally collected as goods move through customs, and the information required for adjusting more than a few unusual shipments to the balance of payments basis is not available.
For an earlier description of the problem, see Herbert B. Woolley, “On the Elaboration of a System of International Transaction Accounts,” Ch. 3 in Problems in the International Comparison of Economic Accounts: Studies in Income and Wealth, Vol. 20, National Bureau of Economic Research (Princeton University Press, 1957), pp. 217–90, especially p. 263. Also, it is noted regularly in the introductory notes contained in International Monetary Fund, Direction of Trade Yearbook, various issues.
See, for example, Organization for Economic Cooperation and Development, OECD Economic Outlook, No. 23 (July 1978), Table 27 and the discussion on pp. 42–43 and 117.
The residual asymmetry in recent years is smaller and shows a slightly different movement if the trade balances of countries that are not members of the Fund, other than Switzerland and Hong Kong, are taken into account (that is, if “world” trade is defined on a truly global basis); see the final column of Table 1. Of course, increased lumpiness of any component of the residual asymmetry, such as Soviet agricultural imports, would reduce the prospective sufficiency of the timing asymmetry as an explanation of the irregular part of the total.
See Sven Grassman, “A Fundamental Symmetry in International Payment Patterns,” Journal of International Economics, Vol. 3 (May 1973), pp. 105–16, and Stephen P. Magee, “U. S. Import Prices in the Currency–Contract Period,” Brookings Papers on Economic Activity: 1 (1974), pp. 117–68.
These estimates can be shown to be the same as those resulting from nonlinear least–squares estimation and therefore asymptotically unbiased and efficient. See Jan Kmenta, Elements of Econometrics (New York, 1971), pp. 442-45. The nonlinear estimation algorithm used in this paper was provided by Data Resources, Inc.
For the case in which freight costs are assumed to vary with the transit lag, see Section A of a set of Supplementary Notes, which is available upon request from the author, in care of the European Department, International Monetary Fund, Washington, D. C. 20431.
In a foregoing passage, it was argued that shipments exported and imported in the same month are subject to a lag of, roughly, 0.25 month on average, shipments exported and imported in successive months to an average lag of 1.25 months, and so on see the discussion on expressing the transport lag in units of time. This logic has been carried over to the computation of the exchange rate change series: ri* is a weighted average of the percentage changes over i and (1 + i) months, with weights of 0.75 and 0.25, respectively. While logical consistency is thus preserved, the empirical results are not noticeably affected by this a priori specification—relative to, say, a straightforward i–period percentage change.
For additional notes on data sources, see the Statistical Appendix.
These regional designations are the ones utilized in DOT and in IFS, through 1979, “more developed primary producing countries” being the combination of “other Europe,” Australia, New Zealand, and South Africa. Trade with the so–called Sino–Soviet area countries and with other countries that are nonreporters is omitted.
The disaggregation may be carried further. Paijit Habanananda has studied selected bilateral flows between individual countries of different regions; see “The Transit Lag in Trade Statistics,” Papers on International Financial Statistics (International Monetary Fund, January 22, 1979).
For a description of the method used in preparing the estimates of bias, see Section B of the Supplementary Notes mentioned in footnote 6.
Let s be the ratio of the specified end–year months’ (December plus November plus …) exports to annual exports, estimated by taking the arithmetic mean over the sample period. Then,
To the extent that the excluded shipments are correlated with the included ones—which is probably large in this case—the estimated timing asymmetry figures will not be biased, despite the upward bias in the regression estimate of b.
For regions’ exports to the “world,” the fourth–quarter flow was estimated by applying the relevant proportion calculated from IFS monthly data to the annual DOT figure.
A mixture of correct and incorrect signs, with instances of statistical significance being distributed fairly evenly between the two categories, is typical of the estimates of d based on annual data—irrespective of changes in, for example, sample length, explanatory variables, or geographic disaggregation. Additional regression results for equation (8), including the exchange rate effect, are presented in Section C of the Supplementary Notes mentioned in footnote 6.
It may be objected that, in general, standard errors of additional lag terms can be large because of collinearity, in which case application of the t–test will result in inappropriate exclusion of those terms and a downward–biased estimate of the average lag. In the present instance, however, the sign on the first excluded term is negative, and inclusion of additional terms does not result in a larger average lag. Moreover, bias from this source in the estimate of the lag measured in units of time does not imply bias in the estimated asymmetry values, since if the terms are collinear, the included terms capture the effects of the excluded ones. It has been pointed out to the author that the equation can be rewritten so that the lag terms are all first differences, a modification certain to lessen the degree of collinearity among the regressors. However, when the equation modified in this way was fitted, the estimates of the bi remained unaffected to two (in most cases, three) significant digits, even in the specification including six lag terms (corresponding to the last line of part (a) of Table 4). Thus, the problem of collinearity among terms of the form (Xt – Xt-i) is minimal in the present context.
Apart from the possibility of sampling error, this counterintuitive pattern may be due to customs verification procedures. Declarations containing irregular or implausible information may be set aside until further checking is possible, the shipments involved being counted as imports when this process has been completed.
Other things being equal, attribution of part of the value of imports to exchange rate change increases the remaining difference between exporter and importer records if dollar depreciation has been more common than dollar appreciation. This larger difference, in turn, results in generally smaller reduced form coefficients—a reduction in â, but an indeterminate effect on the
For a brief discussion of the econometric factors that account for differences in lag estimates from data of different frequencies, see Section E of the Supplementary Notes mentioned in footnote 6.
The simple correlation coefficient between monthly values of X and ΔX is even smaller, 0.21, but this is not the appropriate statistic for comparison, since the monthly version of the model contains three terms of the form Xt-Xt-i. The quoted statistic, 0.35, is the correlation coefficient, r, resulting from regressing X on all terms of the form Xt-Xt-i, just as the 0.97 for annual data could be calculated by regressing X on ΔX.
For example, see Smith (cited in footnote 1), pp. 223–24.
These averages naturally mask commodity differences; for example, for Japan, the range is from 2 days for electronic calculators (the median lag for calculators is zero days, as just over one half are shipped by air) to 52 days for steel plates and sheets.
Magee gives a figure of 31 days for the average entry lag for the Federal Republic of Germany (Table 4, p. 133), but this is clearly a typographical error, as none of the components of the mean approach this figure. The correct value, which is 15 days, can be computed from the horizontal sum in his table.
As discussed, the revision pattern observable in published time series indicates that estimation would be impaired by including data from 1976 and 1977 in the sample, but there is no evidence that earlier years are likely to be affected by outstanding revisions. The Fund’s Bureau of Statistics reports no change in its collecting or processing procedures dating from 1974 or 1975—such as the cutoff date for incorporating late revisions—that would explain the apparent tapering of the December factor.
See Magee, pp. 118–19, and Grassman (cited in footnote 4), passim.
The exchange rate variable was transformed by computing percentage changes. In the forecasting version, the value zero was used for all months through December 1970 because the U. S. effective rate has not been calculated for earlier periods in the IMF Data Fund.