Economic Growth and Fluctuations in Germany: Lessons from 2006 Benchmark Revisions. Jennifer Chao, Ataman Ozyildirim, and Victor Zarnowitz

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1 Economic Growth and Fluctuations in Germany: Lessons from 2006 Benchmark Revisions Jennifer Chao, Ataman Ozyildirim, and Victor Zarnowitz The Conference Board March 2007 I. Introduction: Purposes and Problems For each country covered by The Conference Board s Global Indicators Program, aggregate economic activity is measured by a composite index of comprehensive coincident indicators (CEI). Its components are all seasonally adjusted monthly time series of national totals in physical units or deflated values for real income, real sales, employment, and industrial production. The quarterly real gross domestic product (GDP), taken from the national income and product accounts (NIPA), which for this purpose is linearly interpolated between the center months, provides additional evidence. The combination of CEI and the interpolated GDP is used to date the business cycle turning points for the country in question (here, Germany). In this article we will show graphs of the series used, with their cyclical turns and phases, and discuss the main problems encountered in the procedure. High-frequency time series must be employed here because business cycles and their phases are relatively short, usually not exceeding a few years. This necessitates much care in handling the shorter yet episodic seasonal and irregular changes. There is also a need to pay attention to the evolution of longer trends that are likely to be nonlinear and affected by significant changes in cyclical and shorter movements. Leading Indicators and composite indexes are constructed to predict the coincident indicators and indexes, hence come up later for analysis and discussion in next month s article. 1 1 Dealing with growth cycles requires trend estimation and elimination, hence is subsequent to, and much more complicated than, dealing with business cycles, which can make considerable advance without this tall requirement. 1

2 II. Measuring Current Economic Activity and Dating Business Cycles The most comprehensive and representative coincident indicators available from Germany monthly are (1) industrial production, (2) real retail sales, (3) real manufacturing sales, and (4) number of employed persons. The series cover West Germany through 1990, and unified Germany thereafter. They are shown in Charts 1 4 respectively. Retail sales and manufacturing sales are deflated by the source. The two deflated sales and industrial production are volume indexes, 2000=; employment is in thousands of persons. 2 Previously, industrial production and retail sales were smoothed by a three-month moving average to reduce the volatility of each series. Having examined the timing and effect of each series smoothed and unsmoothed on the coincident indicator, this benchmark replaces the smoothed series with the original series. Therefore, this paper shall refer to the unsmoothed industrial production and retail sales series. The four monthly coincident indicators are combined into a composite index of current economic activity or coincident economic index (CEI) shown in Chart 5. For many countries, including Germany, monthly data are scarce and need to be supplemented by quarterly real GDP data. Chart 5 also shows GDP interpolated linearly between center months of the quarters and a combination of CEI (or the coincident index COIN) with the so calculated monthly GDP. 2 Sources are the central bank and statistical central office (Deutsche Bundesbank, Statistisches Bundesamt), Thomson Financial, and TCB. All coincident components are seasonally adjusted by the source, except for manufacturing sales, which is adjusted using the Census X12 method. 2

3 The six shaded columns in Chart 5 are the cyclical declines in COIN+GDP selected by the Bry-Boschan turning-point identification program. 3 The 12-month decline in marked the first German recession after two decades of growth at high but sustainable rates from the low post-world War II level. All but one of the following declines were long (27-32 months) and substantial, yet the intervening rises in activity are represented by our comprehensive indexes were longer and larger yet, as would be expected. Only one of the declines, in , is relative small and short, but not unduly so (9 months). This explains why we have in the past questioned whether this episode qualifies as a recession. The answer is difficult as the case appears to be marginal. But a review of all available evidence and some expert opinions suggest that what happened in the mid-1990 s does pass as a mild recession. Industrial production, a highly cyclical but also volatile series reflecting mainly manufacturing, declined in each of the six recessions but also in a few extra occasions (e.g. in 1971 and 1998). (Chart 1) Its timing consisted of mostly short leads (with two longer ones of 5 and 9). (Table 1) Real retail sales, available only since 1994, had two long irregular declines, the first one overlapping the recession, and second nearly matching the one of (Chart 2). Real manufacturing sales declined in each of the six recessions, but also on four extra occasions, around the early and late 1970 s, around 1986, and around 1990 (Chart 3). The timing of this series is roughly coincident, with only very few long leads or lags. Because manufacturing sales is much more volatile than other components, it is smoothed with a 3 month moving average. This puts this component on more equal footing with the other components. Employment is very 3 See Bry, G., and C. Boschan, Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, NBER Technical Paper 1971, n. 20 3

4 smooth and cyclical, closely coincident at all but one peak (lagging in the 1980 recession), but sluggish in recoveries (Chart 4). The total monthly CEI index for Germany has a very good record of cyclical timing, conformity, and amplitude (Chart 5), while real GDP is just slightly behind in this regard. COIN declined in each of the six recessions, with turns close to each of the business cycle turning points covered, except one (+8 at the trough). These are large and clear movements even on the weakest case, that of 1995/96. Real GDP has a stronger upward trend and a weaker cyclical element than COIN, as would be expected (output grows faster than employment, services faster than goods, for example). Also, GDP often lags at peaks and leads at troughs, by lengthy intervals. Chart 6 compares the old and new coincident index for Germany. Although the new index is calculated from unsmoothed industrial production and retail sales series (as opposed to 3 month moving averages), the index shows little change. Timing at each recession is the same in all instances except the last recession ( ), where the difference in timing is only one month. 4 III. Germany s Leading Indicators and Composite Index: The 2006 Benchmark Review Six of the eight TCB leading indicators are found to have worked satisfactorily during the period covered, These 41 years include six business cycle recoveries and six recessions dated according to our monthly composite index of Germany s coincident indicators and the corresponding quarterly real GDP. This provides ample evidence on the cyclicality and timing of the TCB indicators for Germany, as documented in the attached exhibits. 4

5 Chart 7 shows that Consumer Confidence, a survey-based diffusion index, led at each of the four peaks covered since (The series starts in 1973, declining just before the 5/1973 recession, but too early in the data series to provide a reliable lead on this occasion.) At troughs, Consumer Confidence led three times but had two short lags. While not very volatile, this indicator had as many as four extra declines, but all were short. Chart 8 shows the Growth Rate of CPI for services. Its counterpart for U.S., when inverted, tends to lead. However, here for Germany, this series is a clear failure as a leader. The inverted growth rate for services prices proved to be inconsistent, with leads ranging from -1 to - 28 at peaks and -17 to an 8-month lag at troughs. It also proved to be excessively volatile, with several extra movements interrupting expansions, and missed the short recession. It must be excluded from Germany s leading index. Chart 9 presents business inventory investment (net change in inventories), which is known as a leading indicator, both important and difficult to handle. Here the timing is as expected: of the 12 observations (6 for peaks and 6 for troughs, ), there are only three relatively short lags (at the 5/1973 peak, the 7/1996 trough and the 8/2003 trough). But this is a highly volatile time series, with generally short and mostly small but numerous (8) extra declines occurring during expansions. Our tentative decision is to retain this indicator despite its deficiencies until a better substitute is found. There are strong conceptual arguments to include the information this indicator provides. New Orders received by Investment Goods Industries (Chart 10) is a familiar type of leading indicator, which would be expected to perform well, and does. This series goes back to 1963 and matches all turning points of the six business cycles covered. It leads by short or 4 These differences in the period may be partly due to data revisions as well as the difference in the use of smoothed vs. actual data. 5

6 intermediate intervals (-1 to -6) in all cases, except for one long lead (-15) at the 5/1966 peak and one short lag (+1) at the 5/1973 peak. There is a clear break between the component for West Germany before 1991 and the component for unified Germany thereafter. The volatility of the former series exceeds that of the latter, but both are moderate. The stock price index for Germany is available only since 1974 (Chart 11). It covers four recessions (peaks) and five recoveries (troughs), and shows intermediate or long leads (-5 to -17) at each of them. In comparative terms, based on considerable international evidence, it is only mildly volatile, i.e. its irregular component is small relative to its cyclical and trend components. Despite the short sample, this is a good indicator. Profit totals, rates and margins are core indicators, but timely and accurate measures are hard to get in most countries. Such data, as are available, tend to lead and help explain the leads of other variables. For Germany, it did well to use Gross Enterprise and Property Income (GEPATEF), which is shown in Chart 12 separately for (West Germany) and (unified). The first segment is very satisfactory: three leads at peaks (-3, -6, -10) and the three at troughs (-3, -8, -6). Although the break in the date prior to the unification allows no firm observation at the 2/1991 peak, the second segment suggests a decline in this indicator during the recession, suggests a lead of -11 at the trough, and does not preclude a very short lead at the peak, when taken together with the first segment. The leads at the recessions and revivals of 1995/96 are coincident, while those at the recessions and recoveries of 2001/03 very long and uncertain (-24, -36). Growth of GEPATEF in both and was very rapid, and the series has a lot of irregular movements, which however are relatively small and short. The West Germany series, previously measured in marks, was adjusted to euro denominations using the fixed exchange rate. 6

7 New Residential Construction Orders (GEHRC) is available only since 1972, but is otherwise a valuable leading indicator, as are construction contracts for both residential and nonresidential structures elsewhere in various countries. For West Germany, GEHRC leads at all business cycle peaks and troughs covered (in 1973 and 1980, and in 1975 and 1982, respectively) by intervals ranging from -6 to -16. However, for Germany , the series is a less reliable indicator. GEHRC rises steeply through the recession; drops sharply right before the - recession but rises again irregularly during this episode and its immediate aftermath; and then shows a long and mostly steep decline from mid-1996 through 2004, including the recession. (Chart 13) The timing consists of two leads in (-4, -5), one very long lead of dubious significance at the 5/2001 peak, and one lag (at the 8/2003 trough), which is long but uncertain (at the end of our data). For all the problems that are revealed, the series needs to be carefully watched but cannot be replaced or removed. The last of our leading index components for Germany is again familiar: the yield spread, i.e. the difference between long-term and short-term interest rates. Chart 14 shows at the bottom the difference, 10-year bond yields minus the three-month interest rate, and on top, the same spread successively cumulated month by month. The bottom curve, of course is stationary or trend-less; some but not all of its fluctuations cross the zero line. The top curve is much smoother and has a large upward trend created by the cumulation. The yield spread itself (GEYLD) leads at several but not all peaks and troughs covered, by variable but monthly long or even very long intervals (four of the eight observed leads exceed -12 months, three exceed -18). It has an excessive number of countercyclical extra movements (six very visible). Four of these declines are limited to the positive numbers only. But it is when the yield spread inverts, i.e. the long rate falls below the short rate, that a recession often follows; 7

8 not when the yield spread simply declines without inverting, which occurs frequently while and expansion continues. All this is well documented and understood. 5 The simple device of cumulation will simultaneously reduce the number of false signals from the yield spread as well as the length and variability of the resulting leads; in short, improving greatly the contribution of the yield spread to the composite leading index. The cumulated yield spread (GEYLD_CUM) shows much better cyclicality and timing than GEYLD, in addition to much greater smoothness and elimination of most extra turns. The leads of GEYLD range from -5 to -24; those of GEYLD_CUM range from -3 to -19 (but there is also one lag of +6 at the 7/1993 trough). There are valid theoretical reasons for the cumulation of the yield, and Chart 14 provides strong empirical reasons for it. But some counterarguments exist here as well. Three recessions, mainly those in 1966/97, 1995/96 and 2001/03, are missed by GEYLD_CUM altogether. The remaining three are matched with good leads, but there are also two small extra declines, in 1969/70 and 1989/90. But GEYLD performs substantially worse on all these occasions (see Table 2). The upward trending leading indicators are in general more helpful than those that have no trends, but the trend of GEYLD_CUM is not economically meaningful. 6 This, however, does not create a real problem for the composite leading index for Germany, which is adjusted to have a trend equal to that of the corresponding coincident (current activity) index. (This trend adjustment is now applied in the U.S. and will be incorporated into all TCB LEI s.) 5 See Victor Zarnowitz and Dara Lee, The New Treatment of the Yield Spread in the TCB Composite Index of Leading Indicators, Business Cycle Indicators, September An article by Gad Levanon of The Conference Board on the trend adjustment to the LEI is available at 8

9 The preceding detailed analysis of the eight principal leading indicators for Germany led us to two conclusions on how to improve the composite index based on these time series. First, we decided to eliminate the inverted growth rate of CPI for services because of the defects of its cyclical timing and conformity record and excessive volatility. Second, we decided to use the yield spread not in its original form but in cumulated form, so that the inversion (sign changes) of the spread produces the relevant signals of business cycle turns, not all the specific cycle turns (directional changes). Finally, an old and well known trend adjustment procedure was reintroduced as it was for the U.S. LEI. The new composite index presented in Chart 15 (GELEAD) embodies all these decisions. GELEAD is shown together with the corresponding composite index of coincident indicators (GECOIN), which has been derived and discussed in the first part of this report. The comparability of the two series is greatly enhanced by an adjustment designed to make the longer growth trend in GELEAD (as approximated roughly by its average monthly growth rate) equal to that of GECOIN. In effect, only the cyclical and irregular movements in GECOIN and GELEAD can differ; their trends over the sample period (mean growth over the periods covered) cannot. The trend adjustment in GELEAD was made separately for the periods and , i.e. before and after the unification of Germany. (Growth in the latter period was somewhat lower in the latter than in the former period and substantially lower in East than in West Germany.) The trend adjustment has some important advantages such as making the trend in the index concerned independent of the changes in the composition of that index. Hence, it is now adopted procedure in all the countries covered by the TCB indicator approach. 9

10 The results conveyed by Chart 15 are not surprising, being consistent with those obtained for the United States and elsewhere. The leading index shows much larger fluctuations than the coincident index; it is more cyclical and its greater sensitivity makes it at times more irregular (see its large extra movements in and 1986), but it is not excessively erratic. Moreover, GELEAD declined ahead of GECOIN before business cycle peaks and rose ahead of GECOIN before or at business cycle troughs with substantial consistency. But the leads were sometimes short, especially at troughs, and the declines were generally small and not well articulated in the short recession of 1995/96 (see chart). Chart 16 compares both new and old leading indexes. The old leading index is shown with the same trend adjustment applied to the new index for comparability. The cyclical movements in the new index clearly have much higher amplitudes. This is due to the cumulation of the yield spread. These higher amplitudes make the turning points in the index much easier to distinguish, and in fact, the new index has an improvement of one less extra cycle than the old index. However, the high amplitudes at the beginning of the new series mitigate the visual effect of the trend adjustment. We think the benefits of the changes to the LEI outweigh the negative graphical effects. Moreover, the out of sample forecasting exercises we performed to compare the predictive ability of the new and old indexes support this view, and we discuss those results in the next section. IV. Forecasting Performance of the Leading Index The out of sample forecasting exercises undertaken as part of the benchmark review provide evidence that the predictive ability of the leading index in real time is not hurt by the changes in composition and methodology. In fact, the forecast errors generated from regressions 10

11 that use the old and new LEI alternatively are not statistically different from one another. However, there is some evidence that there is a slight improvement in the new LEI s forecasting performance even though this improvement is not statistically significant. Table 3 shows the mean squared forecast errors of regressions used to forecast the growth in the coincident index (CEI) one month ahead or six months ahead (panel A and B in the table). We have looked at alternative growth rates of 3 to 12 months in the indexes (rows 1-4 and 5-8). The out of sample forecast errors of regressions that use the LEI (old and new in columns 2 and 3 respectively) are consistently equal or lower than those that use the CEI alone to forecast the growth rates of CEI (column 1). The gain from including the LEI in a regression to forecast the growth rate of CEI ranges from about 0.3 to about 3 percent in the case of one month ahead forecasts (columns 4 and 5 in panel A) and from about 4 to 13 percent in the case of 6 month ahead forecasts (columns 4 and 5 in panel B). 11

12 Chart 1. Germany Industrial Production, incl. Construction, S.A Germany Industrial Production (2000=) West Germany Industrial Production (1991=) Volume Index Source: Bundesbank Chart 2. Germany Retal Sales, S.A Germany Retail Sales Volume Index (2000=) Source: Datastream, Statistisches Bundesamt

13 120 Chart 3. Germany Manufacturing Sales, 3M moving avg., S.A. Germany Manufacturing Sales, 3MMA (1995=) Germany Manufacturing Sales, 3MMA (2000=) Volume Index Source: Bundesbank Chart 4. Germany Employed Persons, S.A. Germany Employed Persons Volume, thousands Source: Datastream, Statistisches Bundesamt 13

14 120 Chart 5. Germany Coincident and Real GDP Germany Coincident (COIN) (Left) Germany COIN + GDP (Left) Germany GDP (Right) Index (1990=) Billions of Euro Source: TCB, Datastream, Statistisches Bundesamt Chart 6. Germany Coincident Index (New and Old Index) Germany Coincident (New) Germany Coincident (Old) Index (1990=) Source: TCB 14

15 115 Chart 7. Germany Consumer Confidence, S.A. Germany Consumer Confidence 110 Index, Opinion Balance Source: Datastream, IFO Chart 8. Germany Growth Rate of CPI, Services, S.A. Germany CPI Services 6M Growth Rate (Inverted) 0 Annualized Growth (%) Source: Bundesbank 15

16 Chart 9. Inventory Changes, S.A. 16 Germany Inventory Changes, 3M moving avg. 12 Millions, 1999 Euros Source: Bundesbank Chart 10. New Orders in Investment Goods Industries, 3M moving avg., S.A Germany New Orders in Investment, 3MMA, (2000=) West Germany New Orders in Investment, 3MMA, (1995=) Volume Index Source: Bundesbank 16

17 Chart 11. Germany Stock Price Index 600 Germany Stock Prices 500 Index (1980=) Source: Bundesbank Chart 12. Germany Gross Enterprises and Properties Income Germany Gross Enterprises & Properties Income West Germany Gross Enterprises & Properties Income Millions of 1999 Euros Source: TCB, Bundesbank 17

18 Chart 13. Germany New Residential Construction Orders, 3M moving avg., S.A Germany New Residential Construction Orders, 3MMA West Germany New Residential Construction Orders, 3MMA Index (2000=) Source: Bundesbank Chart 14. Germany Yield Spread Germany Yield Spread, 10Y-3M (Left) Germany Cumulated Yield Spread (Right) Percent (10Y-3M) Percent (cumulated) Source: Bundesbank, TCB 18

19 Chart 15. Germany Leading and Coincident Index, Germany Leading (New, 7 Components) Germany Coincident (New) Index (1990=) Source: TCB Chart 16. Germany Leading Index (New and Old Index) Germany Leading (New, 7 Components) Germany Leading (Old, 8 Components, w/trend adj.) Index (1990=) Source: TCB 19

20 Table 1. Timing of Germany Coincident Indicators and Composite Index (CEI) GEMSA_MA3 GERS_MA3 GERS GEIP_MA3 GEIP GEMP GEGDP COIN COIN Turning Points for Industrial Industrial Old 1 New 2 Germany Manufacturing Retail Sales Retail Sales Production Production Employees GDP Coincident Index Coincident Index Business Cycle 3MMA 3MMA 3MMA (w/historical s) Timing at Business Cycle Peaks May-66 0 n.a. n.a May-73-2 n.a. n.a Mar-80 0 n.a. n.a Feb n.a. n.a May n.m May Extra Turns Missed Turns Mean Median St. Deviation Timing at Business Cycle Troughs May-67-4 n.a. n.a Oct n.a. n.a Nov-82-1 n.a. n.a Jul-93 0 n.a. n.a Feb n.m. 1 1 Aug Extra Turns Missed Turns Mean Median St. Deviation Combined Statistics Mean Median St. Deviation Old Coincident index consists of retail sales, industrial production series, and manufacturing sales smoothed using 3 month moving average, and employment (with historical revisions) 2 New Coincident index consists of retail sales and industrial production series unsmoothed, manufacturing sales smoothed using 3 month moving average, and employment 20

21 Table 2. Timing of Germany Leading Indicators and Composite Index (LEI) GECC GEICRSA_EXT GEOIR_MA3 GESP GEPATEF GEHCR_MA3 GEYLD_CUM GEYLD GEGSM6 LEAD LEAD LEAD Turning Points for New Orders Residential Yield Spread Yield Spread Growth Rate Current Current New Germany Consumer Inventory Investment Stock Gross Construction 10y - 3m 10y - 3m of CPI 6M Leading Index 1 Leading Index 2 Leading Index 3 Business Cycle Confidence Change Goods Prices Income New Orders (cumulative) (old) (inverted) 8 Components 8 Components 7 Components Timing at Business Cycle Peaks May-66 n.a n.a. -3 n.a. n.m. n.m. -14 n.m. n.m. -3 May-73 n.a. 6 1 n.a Mar Feb n.m May n.m. n.m. n.m May n.m Extra Turns Missed Turns Mean Median St. Deviation Timing at Business Cycle Troughs May-67 n.a n.a. -3 n.a. n.m Oct Nov Jul n.m Feb n.m. n.m. n.m Aug n.m Extra Turns Missed Turns Mean Median St. Deviation Combined Statistics Mean Median St. Deviation Current Leading composite index includes 8 components (calculated with yield spread and without trend adjustment) 2 Current Leading composite index includes 8 components (calculated with yield spread and trend adjustment) 3 New Leading composite index includes 7 components (excluding CPI growth rate for services), calculated with cumulated yield spread, and trend adjustment using two periods ( , ) 21

22 Table 3. Predicting Growth in the Coincident Index (CEI) with the Germany Leading Index (LEI) Initial Sample Period: 1965: :12 Out of sample forecast: 1980: :12 Root Mean Squared Forecast Errors Variable Transformation: PANEL A 1 Month Ahead Forecasts of Growth in CEI Percent Percent Forecasting with: improvement improvement CEI only Old LEI* New LEI with old LEI with new LEI (1) (2) (3) (4) (5) (1) 3 Month Growth Rate (2) 6 Month Growth Rate (3) 9 Month Growth Rate (4) 12 Month Growth Rate Variable Transformation: PANEL B 6 Month Ahead Forecasts of Growth in CEI Percent Percent Forecasting with: improvement improvement CEI only Old LEI* New LEI with old LEI with new LEI (1) (2) (3) (4) (5) (5) 3 Month Growth Rate (6) 6 Month Growth Rate (7) 9 Month Growth Rate (8) 12 Month Growth Rate * Old LEI is trend adjusted for comparability h + 5 Notes: The regression equation is: j CEIt = α j CEIt 1 + δ i j LEIt 1and i = h x = ln x ln x where x t = CEI t or LEI t ; h denotes the forecast horizon (h =1 or 6); j t [ ( ) ( )] t t j and j denotes log changes over j months when j = 3, 6, 9, or 12 months 22