sou · GQB3118d4 · 2003-01-01 00:00:00 — share of GDP of Private Consumption (PC), Exports (EXP) Exports of Goods (EXG), Imports (IMP), Imports of Goods (IMG), Government Consumption (GC), Local Government Consumption (LGC), Investments (INV), Central Government Consumption (CGC), Exports of Services (EXS) and Imports of Services (IMS). The horizontal distance to the origin shows how large the revisions are in each variable. Some people would be more interested in a certain variables reliability as a preliminary figure and less so in its overall effect on GDP. Then it is easy to pick out Exports and Imports of Services as those in most urgent need of amended recording proce- dures. On the other hand, variables with the greatest distance to the origin should most urgently be looked over for possibilities to achieve better preliminary GDP figures. This is a graphical descrip- tion of the impact figures in Table 3.1.1, but here the GDP share and m(ABS) can be studied together. Private Consumption is here used as a standard for the indifference curve GDP Share x m(ABS) = 0.2 = 0.5 x 0.4 shown in Diagram 3.1.15, where 0.5 is the share of Private Con- sumption in GDP, and 0.4 its m(ABS), see Table 3.1.1. This deter- mines the position of the indifference curve. The points to the right of this line are variables whose revisions have greater impact than Private Consumption on GDP revisions. We see that Imports and Exports of Services are now joined by Exports and Imports (total) and by Investments, but Government Consumption and its both components lie underneath the indifference curve, together with Exports and Imports of Goods. The same message is of course delivered by comparing the impact factors in Table 3.1.1 to that of Private Consumption. Diagram 3.1.16 is the same comparison, but with data from the 1990s. There is a remarkable change: In Diagram 3.1.15 six of 11 variables were below the Private Consumption isoquant; in Dia- gram 3.1.16 only four! Variables that were before below the iso- quant but now are above it are: Exports and Imports of Goods, Government Consumption, and both its components. Only In- vestments have moved from above to below. This diagram modifies what was said above if the trends during the 1990s have continued since then. If one had to choose, the main target of improvement should be Government Consumption and Exports and Imports of Goods. In this section and the next we want to find out if knowledge about the revisions in prices can be deepened by comparing them to the revisions of current price data and of the implicit defla- tor revisions. 3.2.1Comments on diagrams and tables The diagrams here deliver more or less the same message as that of price data. The main difference is that some of the vari- ables, notably Exports and Imports of Goods and seem to be more concentrated than for data in prices. There is also more distinct bimodality in price GDP revi- sions in Diagram 3.2.14, as mentioned in Section 3.1.2. price figures are obtained by dividing current price data by the corresponding price on the most disaggregated level. , one would expect that the need to revise the data in prices would be greater than for its two components. In practice, price data are rarely revised; an important price data set, CPI, is in fact never revised. However, the weights in the price in- dices used as deflators (not the series presented here) may change. Another important source of changes emanates from the harmoni- zation of expenditure and production estimates. This is a compli- cated procedure where, on the production side, input-output (I/O) tables are used, and these may and do change during the harmoni- zation procedure. Furthermore, using crude aggregate price esti- mates for preliminary data introduces an implicit revision when more detailed prices become known in the final accounts. All this could materialize both as larger bias and greater disper- sion in price than in current price data. A hint of this is obtained from ocular inspection of the histograms, and Table 3.2.1 confirms the hypothesis. With the exception of , both measures of bias (Mean and Median) and the mean absolute value of revisions (m(ABS)) of current price revisions are smaller than or equal to the same for price figures18. However, the pure dispersion measures (S and Range) are more or less the same as for prices, except for Net Ex- ports, for which current price revisions have remarkably smaller 18 Fixler and Grimm (2002) also find larger revisions in than in current price U.S. dispersion than for prices. The decomposition between volume and price seems to be shaky in the preliminary figures of this variable. The correlation between revisions and growth are smaller for data in current prices; only two variables, Central Government Consumption and GDP have large coefficients. In prices two more variables had revisions that were strongly correlated with growth, see Table 3.1.1. This shows that under- and overestima- tions occurring in expansions and contractions, respectively, are also due to weight changes between preliminary and final deflation procedures. This is when much is happening in the market and hence price index weights can be expected to change rapidly. Also, quality changes can first go undetected, overestimating price in- creases and underestimating the volume. Imports, and especially Imports of Services, emerge as the vari- able with the proportionally greatest impact on GDP revisions. Since negative growth figures are not common in current price data it is not surprising that there are only two cases of different signs in preliminary and final current price data, as opposed to 27 in Ta- ble 3.1.1. Less expected is the fact that there are only 16 misses in acceleration/deceleration; there were 40 in price revisions. In other words, price data that at the time of the release of prelimi- nary figures poorly match the internal structure are an important source of this kind of error in preliminary data in prices, having an impact on data in turning points. This is where the dif- between two sources of error versus one source is most evident. Table 3.2.2 resembles Table 3.1.2 and requires no further com- ment. Also, the black and bold figures in Table 3.2.3 are by and large distributed as in Table 3.1.3. Negative revisions of GDP (last two columns) occurred in phases where growth was slowing down (except for 1993) and positive revisions coincided with the econ- omy picking up, except in five cases. The relationship with the business cycle again shows up less in current price than in price data. 3.3.1Comments on diagrams and tables19 Given the time series on revisions in and current prices, deflator changes due to revisions20 can be calculated implicitly. Since we are dealing with growth rates the revision in a growth fig- ure in current price minus the revision in price is an ap- proximation to the implicit change in the deflator. Denoting final figures by capital letters and preliminary figures by lower case let- ters a revision in current price can be decomposed in the following way: (Q q) + (P Q p q) where Y is a value in current price, P is the implicit deflator and Q is the value in price and dots signify pct. In Sec- tion 3.1.1 we studied the second term to the right, and in the previ- ous section the term on the left hand side. Here we would like to investigate the implicit deflator (P& p& ) , but we have to accept that the last term is included in our measure. However, this term can be assumed to be small, and we ignore it here. Recall that the deflators used when calculating price data are from the CPI, PPI, etc. statistics and hence are rarely re- vised. The changes to be found in the deflators have at least two origins: 1.Price have a base year weight distribution that be- comes obsolete when becoming distant in time. 2.Harmonizing the Expenditure and Production Accounts re- quires revisions of both current and price data and this leads to implicit changes in the deflators, see the previ- ous section. The diagrams 3.3.1–3.3.12 of implicit deflator changes look differ- ent from the two sets of diagrams discussed in the previous sec- tions. They are more concentrated; if there is bias it is mostly nega- tive – price are generally overestimated. Especially Central Government Consumption has a large negative bias and the bias in Exports of Goods is of the same magnitude as in current prices, 19Inventories and are not studied in this section. 20The difference between final and preliminary values of implicit deflators are here called changes, not revisions. To some extent the negative bias can be explained by fixed base years in the price indexes, overestimating relative price decrease. Generally, there has been a remarkable slow-down in in- flation during the period studied, see the last column of TableA3. Another case where old weights lead to initial overestimation of price occurs when prices rise and the price elasticity is less than zero. Then buyers substitute more expensive goods for less expen- sive items. This is not detected in an index using fixed weights. Point 2 above is less easy to tackle. Without detailed knowledge of how the harmonization of accounts, mentioned in the previous section, it is hard to say what this could explain of the implicit changes of deflators. In there is no official set of rules or models to consolidate the two different accounts. Except for Imports of Goods, m(ABS) is smaller for implicit de- flator changes than for the expenditure revisions expressed in cur- rent prices in Table 3.2.1. The same applies for the other spread measures (S and R), again except for Imports of Goods (S=0.7, R=2.5), for which these measures are much larger than for the same figures in current prices (S=0.4, R=1.5). In fact they are of the same magnitude as for price data (S=0.8, R=2.6) in Table 3.1.1. There is no obvious explanation to this. The correla- tion with the final implicit deflators is for no variable statistically significant. The Impact figures are smaller for deflator changes than for con- stant and current price data revisions. If improvement in prelimi- nary deflation procedures are contemplated the largest payoff is obtained from import deflators, also see the comments on Imports in the previous section. From Table 3.3.2 we see that the implicit GDP deflator changes, as measured by m(ABS), have decreased slightly, but not in relative terms (rm(ABS)) in the 1990s as compared to the 1980s. From the two previous sections one expects Central Government implicit deflator changes to increase substantially, and indeed this is true. A new feature is that m(ABS) for Investments increases almost three- fold and rm(ABS) is five times larger in the 1990s than in the 1980s. The bold figures in Table 3.3.3 are distributed more or less as in the analogous tables of the two previous sections. It would be important to know why implicit deflator changes have increased for so many variables. Preliminary and final deflators in the two last columns of Table A3 show that during the 1980s inflation was overestimated in seven out of ten years. Inflation was also underestimated. primarily in years of high inflation. Overestimation of inflation reoccurs for all the four first years of the low inflation period that started in the end of 1992. Technically, the difference between final and preliminary defla- tors reflect: 1.Inconsistencies and shortcomings in underlying data sources, 2.The necessity to harmonize production and expenditures, and 3.Differences in compilation procedures between (prelimi- nary) quarterly and (final) annual accounts. Since price are rarely revised this means that the results of this section provide a more genuine picture of shortcomings in the underlying data and compilation procedures. But the effects of in- dividual error sources are difficult to trace. detailed docu- mentation would be necessary, and indeed useful. 3.4Discussion of the results We saw in Section 3.3 that implicit price changes were substantial and increasing and that their biases mostly were negative. The overestimation of price increases may, at least partly, be explained by the fact that in the period 1980–1998 the base year of the price was changed only every five years. Up to five years old goods baskets could be expected to lead to an overestimation of price increases and an underestimation of volumes during a period of declining inflation. How strong this effect has been is difficult to say. Variables that had large revisions in prices had so also in current prices and had large changes in deflators. One may con- clude that problem variables are problematic both in the volume and in the price components, an example being Exports and Im- ports of Services. The background to this is the following. Before 1987 most of the statistics of foreign trade in services was pro- duced by Statistics , based on a survey. In 1987 it was de- cided that the Riksbank should use its database with some modifi- cations for producing the National Accounts, although some ser- vice data were still collected by Statistics . This change hap- pened almost at the same time as a general revision was carried out in the National Accounts, implemented in 1989. This is probably the reason to the very large revisions in current price foreign trade in services in the second half of the 1980s. The new system then led to smaller revisions. Government Consumptions appears as the variable most in need of improvement, especially the preliminary statistics on Central Government Consumption. The overall increase in rm(ABS) has also to be given serious consideration. 4Revisions of Swedish Quarterly Accounts in In the international literature on revisions quarterly observations are the most common observation unit. it will be possible to contrast our results both against those from the 1970s and 1980s in (1992) and against some international figures. In Section 1.3 (Table 1.2) we described how quarterly revision data were calculated. Recall that our data are neither seasonally nor working day adjusted. (1992) emphasizes the deficiencies in quarterly source data. He finds that they often contain obvious errors that have to be adjusted judgmentally. Note that this does not necessarily imply larger revisions of quarterly than of annual preliminary figures, the simple reason being that a preliminary annual figure is the sum of four quarterly figures, so that annual revisions will be smaller only because of cancellation when adding quarters. But this also means that higher accuracy in both sets of preliminary figures can only be achieved by improving the quarterly source data. From Chapter 3 we may expect that the source data on Private Consumption are the most reliable and that source data on Gov- ernment Consumption are the least accurate. This is the case. There are also other similarities between the results. 4.2Frequency distributions of revisions Except for , Diagrams 4.1–4.13 are analogous to Dia- grams 3.1.1–3.1.14 and 3.2.1–3.2.14, but note that the scale of the horizontal axis has been almost doubled so as to accommodate the much more widely dispersed quarterly revisions. Still, Exports and Imports of Services needed even wider scales. All features found in annual data can also be discerned from the histograms on quarterly data: bias, skewness (maybe slightly less so than for annual data), thick tails and some tendency to bimodality. Although the data are increased fourfold there is a tendency of convergence to- ward the normal distribution. Again, preliminary figures on Ex- ports and Imports of Services, Central Government Consumption and Investments have the largest revisions. The biases are both smaller and larger in Table 4.1 as compared to annual data in Table 3.1.1. The bias in preliminary quarterly GDP figures is smaller than that in annual data, according to both bias measures, mean 0.2 vs. 0.4 and median 0.2 vs. the bias in quarterly data is substantially larger. The bias of investments revisions has changed from 0.8 in annual to –0.4 in quarterly data! Table 4.4 can explain this. There is a large negative bias in the second quarter (-2,2), for which statistics have to be released so early that investment survey data for that quarter are not yet available. Instead, first quarter data on expectations for the next quarter are used as an estimate. Investment expectations appear to be overoptimistic, as plans often are. Since only one revi- sion is studied here we cannot confirm the hypothesis that revi- sions of quarterly data have also been inconsistent, but (1992) finds such evidence. Less serious skewness turns up as a smaller difference between mean and median. All the other characteristics reflect larger revi- sions of quarterly than of annual data, both in and in cur- rent prices. The correlation with the business cycle is of the same order of magnitude as for annual data. This was expected, as dis- cussed in Chapter 3. Volatile quarterly figures more often have different signs for preliminary and final data. From pure numbers one would expect four times more different signs as compared to annual figures. It is slightly surprising that different signs occur only twice with quar- terly GDP data, i.e. in the first two quarters of 1981, when the economy was turning into a contraction. It must have been confus- ing for economic forecasters to specify the turning point from the statistics at that time. Indeed, both nomic Research () and grossly mis- judged the business cycle 1981–1982, see and Barot (2000). During the next 17 years different signs did not turn up again. The signs of preliminary figures for Exports and Imports of Goods are equally reliable in quarterly and in annual figures. The unreliability of preliminary figures on Central Government Con- sumption is even more obvious in quarterly than in annual data. There are 26 wrong signs, despite the fact that the series contains less observations than the GDP series. As an example, the direction of the preliminary figure was not the same as that of the final figure during the entire period 1987, 3rd quarter to 1988 4th The preliminary statistics on foreign trade with services were expected to be bad, and indeed, Exports of Services have 20 wrong signs, yet of the observations were missing. However, the different signs mostly occur in the 1980s. On the other hand, Private Consumption, the hitherto rather reliable variable has dif- ferent signs seven times, four of which occur in the critical business cycle 1990–1994, see Table 4.3! It is tempting to go 44 years back in time and quote Zellner (1958) who writes about revisions of US GDP data 1947–1955: This good showing, however, must be tempered by the fact that a sizable proportion of the errors in direction of change occurred in the neighborhood of cyclical turning points. Later writers, study- ing more recent data come to more or less the same conclusion, namely that preliminary US data give a satisfactory description of the business cycle, but will occasionally transmit a false signal close to a turning point, see Stekler (1967), (1995) and Grimm and Parker (1998) who find that preliminary GDP data often over- state a decline before a trough and understate growth in recoveries. By and large U.S. quarterly data provide a correct picture of the turning points, and the business cycle more generally. Acceleration/deceleration (Ac/Dc in Table 4.1) of GDP was ne- ver different in preliminary and final annual figures (Table 3.1.1). The situation is quite different in quarterly data: 14 misses21. The most critical cases occurred in the first two quarters of 1981 (see above), in 1991, 4th quarter and in 1992, 1st quarter, when the length and depth of the recession were underestimated by many analysts, and at the start of the mini-recession in 1996. Exports and Imports, except for services have a remarkably good record as showing the same acceleration/deceleration in prelimi- nary and final figures. Again, Exports and Imports of Services per- formed badly in the 1980s. We conclude that for turning point prediction the impact figures in Table 4.1 may be slightly misleading, showing the greatest bene- fits from improvements in foreign trade variables, especially ser- vices, because preliminary and final figures for Exports and Im- ports of services have come closer to each other in the 1990s. In- stead Government Consumption and Private Consumption be- come the most important objects for improving the quality of pre- liminary statistics. Also, see Diagrams 3.1.15 and 3.1.16. 21 This is 1/5 of the observations. Fixler and Grimm (2002) report ¼ misses, but recall that their final figures are the latest available data. Following the histograms of this chapter we have added a Dia- gram 4.14, which displays how preliminary and final GDP growth rates saw the crisis in the beginning of the 1990s. The figures have been picked from Table A4b in the Appendix. We see that the main features and the change from minus to plus are similar in the two time series, but both oscillations are understated in the preliminary figures. Also, note the dip in the preliminary figure for 1994, 3rd quarter, which has disappeared from the final figure. 4.4Have revisions decreased? Comparing Table 4.2 to Table 3.1.2 one finds more or less the same tendencies. The mean absolute revisions (m(ABS)) have more than doubled in the 1990s for all Government Consumption items, whe- reas total export and import revisions have slightly decreased, due to much smaller revisions of services. , m(ABS) was either the same or larger in the 90s than in the 80s (foreign trade data collection changed in 1995). But the reduction in revisions of services more than compensated for this in the total. GDP has had slightly smaller m(ABS) in the 90s. In the annual figures no decrease was found. The reduction in m(ABS) of foreign trade revisions is also re- flected in the relative measure, rm(ABS). For GDP m(ABS) and rm(ABS) have both decreased. Total and Local Government Con- sumption revisions have increased considerably in relative terms. Final figures of Central Government Consumption have become as much more volatile as the revisions in the 90s, so that rm(ABS) stays the same. This may be a sign of lower quality in the final fig- ures. Also, recall the quotation from (1995) in Section 1.1, concerning better/worse preliminary vs. Note that the relative standard deviation rS is 1.0 for Central Government Con- sumption and Exports of Services. This means that using the arithmetic average of final growth rates as a preliminary figure would have resulted in revisions of the same magnitude as when using published preliminary figures! The relative bias (rMean) of GDP is the same for quarterly and annual data (0.2). The relative dispersion rm(ABS) has decreased for many variables in the 1990s, including GDP. But it has in- creased for Private Consumption and has doubled for Government Consumption. Table 4.0 Bias and m(ABS) in GDP revisions in the 1970 and the 1980s (1992), Table 5.1 compared to this study m(ABS) m(ABS) m(ABS) Rel m(ABS) m(ABS) m(ABS) *)The reason for the discrepancy between and our estimates of bias is un- clear. 1980 is missing from , but this should not change the bias estimate much. In Table 4.0 we combine our results with those in (1990). There seems to have been an overall decrease in GDP revision bias in the 1980s as compared to the 1970s, and again between the 1980s and the 1990s (according to our figure). Grimm and Parker (1998) find a substantial reduction over time in revision bias in U.S. quar- terly data, both in current price and in real terms. 4.5Discussion of the results Table 4.3 clearly shows the difference between annual and quarterly revisions. In Tables 3.1.3 and 4.3 revisions that in absolute value are greater than or equal to unity are marked in bold.