sou · GQB3118d4 · 2003-01-01 00:00:00 — Preliminary fig- ures of Government Consumption were overestimated and indi- cated the wrong direction in almost half of the observations. Revi- sions seemed to be independent of the growth rates, in accordance with our findings, see Table 5.1. The study ABS (1998) is a bold effort to assess the quality of Balance of Payments data. It contains an intuitive classification ac- cording to reliability of the components of that balance. It is based on a survey among ABS statisticians producing the data. A similar classification of National Accounts data followed in ABS (2000). One of the variables regarded as most reliable (by the statisticians themselves) is Private Consumption, while the group of least reli- able variables are some service variables as well as Central Govern- ment Consumption; a problematic variable also in the Swedish ac- counts, as will be shown in the next chapters. Here, some aspects of reliability of annual National Accounts data are assessed. The most important results can be assembled in a ta- ble where the Dutch figures are compared to the . Table 2.2.1 Bias (mean) and mean absolute value of revisions m(ABS) in 1986–1995 and –1998. m(ABS) The dispersion as measured by m(ABS) is uniformly smaller for Swedish than for Dutch revisions, but for bias the picture is not so clear. GDP revisions have less bias in the , although for some of its components the Dutch revisions have larger bias than the Swedish ones. Government consumption again stands out as problematic. The difference in dispersion in favor of Swedish data can at least in part be explained by the fact that for the Dutch data the final figures come from year t + 3 and hence could be expected to be larger than Swedish revisions at time t + 2. Two recent studies have been made of U.K. Barklem (2000) covers quarterly National Accounts figures of income, ex- penditure and production components, both in current and in con- stant prices. Additionally revisions of some other economic indica- tors are studied. The report also analyses the first sum of four quarters (our definition of a preliminary annual figure), which is compared to a three year later outcome. The revisions are calculated in a different way than used in our study so we refrain from nu- merical comparisons and just report the main findings. No bias is found in annual current price GDP, but price GDP is un- derestimated in preliminary figures. Preliminary values of many variables were found to be underestimating growth in expansion periods. The mean absolute value (m(ABS)) of some revisions had declined considerably during the past decade. For GDP the results are corroborated in Chapter 5. Symons (2001) investigates only annual figures of GDP in con- stant prices and its major components, both expenditures and in- comes. The preliminary estimate is the first figure based on annual data issued in t + 1 and the benchmark is the revised figure from t + 2. Note that this is not the same data our study is analyzing, so here, too, we refrain from numerical comparisons. The main results were mostly the same as in Barklem (2000) above: 1.Preliminary figures were on the average too low, i.e. posi- tively biased revisions. 2.The means of the component revisions were larger than the GDP revision, which suggests that some revisions tended to cancel across components. 3.The bias has been falling during the last three decades. 4.Revisions correlate positively with the business cycle. These studies make references to earlier studies of U.K. revisions, indicating that the Office of National Statistics has repeatedly ana- lyzed its revisions. No other countrys revisions of National Accounts have been ana- lyzed as much as those of the U.S.A. In fact such studies belong to the publication scheme in the BEA13 series Survey of Current Busi- ness. The latest is Fixler and Grimm (2002)14, which is said to be the 14th revision study of U.S. National Accounts data, and updates Grimm and Parker (1998). Jaszi (1965) covered the period 1942– 1962. (1995) discusses five of the earlier studies, which will not be commented on in this section. There are at least three differences between the US and the Swedish data: (1)Most US studies are made using only quarterly data, Fix- ler and Grimm (2002) being a notable exception, where annual revisions are also analyzed. The quarterly figures, too, were decomposed into current and price figures. To keep the study to more modest proportions we decomposed only the annual data. (2)The quarterly US data are seasonally adjusted quarterly growth rates that are normalized to annual growth rates. This automatically introduces a substantial source of revi- sion: the updating of the seasonal estimates. (3)A revision is calculated as the difference between the pre- liminary figure and the best available estimate. Point (2) merits some discussion. The Swedish figures are just pure annual growth rates. The reason to the US practice is that it is thought that the quarterly seasonally adjusted differences lead an- nual growth rates and thus provide earlier information. This may hold for reasonably well-behaved time series, not too much con- taminated by random noise, if the seasonal and trend components are estimated by maximum likelihood methods15, but not generally. But since the most accurate latest estimate is seasonally adjusted 13Bureau of Economic Analysis. 14Moulton et al. (2001) contains a closer study of the most recent revisions. 15Ad hoc type methods such as X-11 have been used in all countries until recently. in the U.S. case and the raw figure in the Swedish case, the un- known true value being unknown, there is no easy way of com- paring which technique would provide e.g. better turning point sig- nals. The third point was discussed in Section 1.3. Note that the dif- in calculation make comparisons difficult by inflating the U.S. revisions in comparison with the corresponding Swedish data. Despite of these differences in the way of calculating revisions, we present the U.S. and Swedish figures from our study in the table below. Table 2.2.2 Bias (mean) and mean absolute value of revisions in the U.S.A 1983–1997 and –2000. m(ABS) -1.2*** 7.7*** *We have changed the sign so as to comply with our definition. **Contains investment. ***Contains only private investment Source: Fixler and Grimm (2002), Tables 1 and 3 and Table 4.1 of this report. are of a different magnitude for Central Government Consump- tion, Exports and Imports, and the dispersions, measured as the mean of absolute values (m(ABS)), are uniformly much larger for the U.S. than for the Swedish revisions. The difference can be at- tributed to the points 1–3 above and to definitional bench mark revisions, as well as to the transition from fixed base year deflators to chain in the US. Looking closer at the biases (the mean of the revisions), we see that preliminary figures are too high for Investments in both countries16. Exports and Imports revision bi- ases are about the same in , but not even of the same sign in 16 The reason to this in the case of Swedish data can be found in Section 4.3. The bias in GDP is twice that of the Swedish GDP, but note that for annual data in Chapter 5, US bias is smaller than the Swedish bias, so probably much, maybe all, of the differences can be explained by the reasons mentioned above. The differences in magnitudes of m(ABS) between US and Swe- den in Table 2.2.2 are so large that we think that the difference in method of calculation must play a major role here, especially the fact that the very latest revised figure is the benchmark for the pre- liminary figure in US, so it is better just to compare the relative magnitudes. Note that although the US Central Government revi- sion bias was quite small the dispersion is substantial. The disper- sion is large also for . The same applies to the cor- responding Swedish revisions but on a smaller scale and the third largest dispersion is the same in both countries: revisions of Im- ports. The revisions of annual figures display much the same patterns and are not shown here. Some academic writers have taken an interest in revisions of U.S. National Accounts, the best-known and first being . In the 2nd edition of his book (1963) he devotes two sections, pp. 261–275 to studying revisions in the U.S.A and the U.K. He claims that revisions are so large in the U.S. quarterly GNI that the business cycle fits entirely into the uncertainty interval. Invest- ments, although presumably a good indicator of future production, are found to be of use as preliminary figures. Many of Morgensterns basic critical statements on economic statistics are still relevant today; see Kenessay (1997). Zellner (1958) remarks that directional errors often occur in turning points. Stekler (1967 and 1987) argues that preliminary data contain meaningful information; contrary to Morgensterns statement, and that the early data present an approximation to the true pattern of economic movements. But he also emphasizes that there are substantial errors in the GDP components. The quality of the underlying data seem to have improved very since Morgensterns studies, according to and Tortora (1995). Another book, entirely on errors in provisional estimates, is (1969). This is an advanced study, considering how early it is. The author presents evidence of preliminary figures having similar features as extrapolations. Much later there were some studies on how errors in preliminary figures affect econometric models, two of them being Holden and Peel (1982) and Stekler (1987). There have also been attempts at modeling revisions, see et al. (1981) and Howrey (1984). A researcher who during more recent years has written exten- sively on revisions and their rationality is K.D. He has studied if revisions are co-integrated, with only one common sto- chastic trend. data (2002) finds that there are two trends, and this makes the revision process irrational. data only one trend seems to prevail. Also, the last revision is ex- ogenous to the rest, thus representing the common trend, and is in this respect to be regarded as the best estimate, see et al. (2002). The list of studies on revisions could be made much longer. We refer to some other studies elsewhere in the text. 3Revisions of Swedish Annual Accounts The most popular macroeconomic figure is the annual real change in GDP, since the public interest focuses on growth and productiv- ity. This is why we have chosen to start the analysis from data in real terms. Subsequently we try to see what further information can be gained from current price and implicit deflator revisions In order to facilitate comparisons of the three types of revisions of each variable, their histograms are juxtaposed on the same page at the end of this chapter. Note that in most cases the compilation of the accounts proceeds from current prices to real figures, using available deflators. We study 14 aggregate variables of the annual expenditure ac- counts. Chapter 4 on quarterly figures provides a more detailed picture of the results in this chapter and the conclusions from the study of Swedish revisions are compared with some international results in Chapter 5. 3.1.1Frequency distributions of revisions The Diagrams 3.1.1–3.3.13 show histograms of the revisions in Private Consumption, Government Consumption (Central and Lo- cal Government separately), Investments, Inventories, Exports and Imports (goods and services separately) and . Revisions in GDP are displayed in Diagrams 3.1.14, 3.2.14 and 3.2.13. This is a set of variables that have very different weights in the National Accounts. Many are two components of an aggregate. In particular all variables are components of the aggregate, GDP. Ac- cording to the law of large numbers one should expect larger varia- tion in the smaller aggregates than in larger ones. And indeed, this can be seen from the diagrams. A typical distribution of a large aggregate is that of Private Con- sumption, see Diagram 3.1.1. It has small dispersion, positive bias, a tendency to be skew, and an outlier. Smaller aggregates like Ex- ports and Imports of Services are scattered across a large interval on the horizontal axis. They had to be provided with different bro- ken scales (marked in bold) to accommodate all the data points. Government Consumption, both Central and Local are also prob- lematic, the former with two, the latter with one observation out- side the horizontal scale. Revisions of Investments can be criticised for being more or less uniformly distributed in the interval –1.1 and 2.7 percentage units. If this behaviour is allowed to continue it means that a preliminary figure of growth in fixed Investments could with the same probability be either –or . The former might signal a recession, while a figure as high as or more has actually been recorded in less than half of the years studied. There is a distinct bias in the revisions of Exports (Diagram 3.1.7) and the distribution has thick tails, but the dispersion is moderate. The relatively good accuracy is due to Exports of Goods. The revisions of Imports are distributed not much differently from Exports, but the tails are even longer and thicker. A tendency to- wards bimodality (two tops) can be seen in all foreign trade revi- sions. Imports of Goods have a tendency towards bimodality that imposes itself on total Imports. Preliminary figures of both Ex- ports and Imports of Services are to be considered extremely unre- liable, historically, and their revisions could potentially have a con- siderable effect on GDP. However, the revisions of these two vari- ables are positively correlated and hence they tend to cancel in and in GDP, and may therefore go unnoticed. In Section 3.1.3 we present evidence that the revisions of these variables have declined in the 1990s. Starting from means and medians note that most, but not all vari- ables have at least a moderate positive bias. In two cases the revi- sion bias is extreme: Central Government Consumption and In- vestments. One possible explanation is that there may be an ele- ment of substituting planned for realised consumption, defence- spending being a major contributor to inaccuracy of Central Gov- ernment preliminary figures. There may also be widespread imper- fection in the accounting systems. Local Government seems to have a better control of data, but note the large bias and skew dis- tribution. Statistics has been aware of these shortcomings for a long time and has tried to cope with the problems. 17 When reading Table 3.1.1 refer to Section 1.3 for a detailed explanation of the measures presented. Confirming what was observed from the histograms, Private Consumption, being the largest aggregate, has the smallest revi- sions, while the largest discrepancies between final and preliminary figures can be found in the variables Central Government Con- sumption and foreign trade in services. Skewness is reflected in the difference between mean and median. Are booms associated with underestimating the growth in pre- liminary figures and do we see overestimation in busts? Four vari- ables seem to be correlated in such a way with the business cycle: Central and Local Government Consumption, Imports of Services and GDP. According to a Students t test the correlations are in these cases statistically significant, but because of suspected non- normality, these test results should not be taken literally. What is the reason to high correlation between revision and growth? What first comes to mind is that preliminary figures can only partially be based on real data. In order to be able to publish aggregate figures the statistician has to use approximations such as interpolation and extrapolation. The former means that the missing figure is substituted for a class average; while in the latter case a trend or the latest observed value is chosen. A class average misses accelerations and decelerations if the missing values represent vari- ables that are most sensitive to cyclical fluctuations, while substi- tutes like a trend or the previous observation are predetermined to miss a turn, up or down. The two last columns of Table 3.1.3 provide a palpable illustra- tion of the correlation between revision and cycle. In the shaded entries preliminary GDP figures have been overestimating the out- come exactly where the final outcome indicates strong decline in growth. The bold figures in the revision column are associated with strong acceleration in production. As was the case with the other characteristics, the relationship between revision and growth can also be seen in the histograms as two tops (bimodality) in GDP revisions in Diagram 3.1.14. The left hand top is associated with retarding and the right hand one with accelerating production. The tendency toward bimodality is even more distinct in current price data, see diagram 3.2.14. Analyzing distributions of GDP forecast errors in , Chumacero (2001) finds bimodality that can be ex- plained by underestimations in upturns and overestimations in downturns, corroborating the similarity between forecasts and pre- liminary figures, already observed by (1969). The Sign row in Table 3.1.1 indicates that the preliminary figure for GDP has had another sign than the final one just once, in 1982, see Table A1 in the Appendix. The preliminary figure was –and the final ,. The inaccuracy of the preliminary figures in (measured as share of GDP) has resulted in different signs of change in preliminary and final figures in two cases. In 1988 a growth was signaled, while the final figure registered a de- cline and vice versa happened in 1991, but according to Table 3.1.2 revisions of the shakiest parts of the balance, Imports and Exports of Services, have declined over time. According to what has been said above it could easily happen that preliminary figures indicate acceleration but final figures show that there was a deceleration and vice versa. The row (Ac/Dc) in Table 3.1.1 shows that for GDP this has never happened during the two decades investigated. The last row of Table 3.1.1 presents a measure of the Impact on the total, i.e. GDP, of revisions of the components. The impact is calculated as the product of the share in GDP (see top of table) and the mean of absolute revisions, m(ABS). If e.g. the absolute value of revisions of the shakiest preliminary estimates of GDP components (Investments, Exports, Exports of Services, Imports of Services) could be halved, the m(ABS) of GDP could be reduced by more than a quarter (from 0.4 to less than 0.3). In other words, this row tells us where in the accounts the largest benefit in terms of smaller GDP revisions can be achieved. Here we do not calculate the cost of implementing such improvements so that this is not a cost-benefit analysis, only a benefit-analysis. We return to this matter in 3.1.4. Relative bias (rMean), i.e. the ratio between the bias in the revi- sions of a variable and the mean of the variable being revised, is shown in the first row of the shaded area in Table 3.1.2. For GDP the figure 0.25 means that the growth rate indicated by a prelimi- nary figure is on average too low, so that e.g. a preliminary figure showing that GDP increased by will on average change to after the final revision. Note that (1974) reports rMean = for GDP revisions 1959–1968. For Swedish data the correlation with the cycle could change this ex- pectation in times of strong acceleration or deceleration of the business cycle. The biases in the foreign trade variables were small in Table 3.1.1 and the relative biases (rM) are negligible in Table 3.1.2. This is be- cause of the strong growth trends in the reference variables. The problem with these variables can be located to the spread of revi- sions of Exports and Imports of Services, see Table 3.1.1. For Central Government Consumption the bias is more than one and a half time larger than the average growth rate in that vari- able (rMean = 1.7)! The standard deviation and m(ABS) of this variable are more or less equal to those of the revisions. This means that the standard deviation of revisions is as large as if one would use average growth as a preliminary figure, ignoring the published preliminary figures. Relating revision dispersion characteristics to those of the vari- able being revised can be ambiguous, because one doesnt know if high volatility in final figures reflects high volatility in the phe- nomenon recorded in the statistics or if it is simply a more general sign of poor statistics. 3.1.3Have revisions decreased? In Table 3.1.2 we have calculated the average absolute revisions (m(ABS)) and the same as relative entities (rm(ABS)), separately for the 1980s and the 1990s. Note that the only variables for which the revisions have decreased considerably, both in absolute terms and in relation to the variable being revised, are the two problem variables: Exports and Imports of Services. The opposite has oc- curred for Government Consumption. Both m(ABS) and rm(ABS) of Central Government Consumption was four times larger in the 1990s than in the 1980s and these measured doubled for Local Government Consumption. In the 1980s Central Government Consumption primary data were so shaky that Statistics often used models to correct obviously erroneous figures. In the beginning of the 1990s an effort was made to improve the data. At the same time the model-based adjustment procedure was aban- doned. These changes do not seem to have been a success. Note that Exports and Imports of Goods had much larger m(ABS) values in the 1990s than in the 1980s, while the relative measures remained unchanged. Whether this is a result of the ma- jor change in data collection from total statistics to sampling that was introduced when joined the EU in 1995, is hard to say. These tendencies and changes between the 1980s and 1990s can be studied in detail in Table 3.1.3 that shows revisions over time. Extreme values have been marked in bold figures. For Central Government Consumption the figures dominate the 1990s while for Exports and Imports of Services, bold figures are mostly to be found in the 1980s. Note that there are runs of bold figures. may be a result of the correlation with the business cycle, which often has spells of acceleration and deceleration. Revisions are generally positively biased and their frequency distri- butions are thick-tailed and skew. Preliminary figures of Exports and Imports of Services are very shaky as measured by m(ABS), but their dispersion decreased in the 1990s. Central Government Consumption was both biased and unreliable and these unsatisfac- tory features worsened considerably in the 1990s. The preliminary GDP figures delivered more or less the same message about the business cycle as the final ones. But the almost uniform distribu- tion of Investment revisions could be deceptive to the business cy- cle analyst. Morgenstern (1963, p. 272), writing about U.S. data from the 1940s and the 1950s, expressed the same finding when stating that the preliminary figures showing the direction of the economy was what he called firm statistics, but other important preliminary information too often was weak: Corporate profits and gross private domestic investment on the other hand would be very interesting for estimating, say, future activity on the stock market, but they are definitely weak series and therefore of use when needed. The revisions of many variables, among them GDP, correlate positively with the cycle, i.e. underestimate upturns and overesti- mate downturns. The largest gains in reliability are achieved by im- proving the preliminary statistics on Investments, Exports and Im- ports of Services. Diagram 3.1.15 shows m(ABS) of revisions vs.