EUROINDICATORS WORKING GROUP THE IMPACT OF THE SEASONAL ADJUSTMENT PROCESS OF BUSINESS TENDENCY SURVEYS ON TURNING POINTS DATING

Transcription

1 EUROINDICATORS WORKING GROUP 11 TH MEETING 4 & 5 DECEMBER 2008 EUROSTAT D1 DOC 239/08 THE IMPACT OF THE SEASONAL ADJUSTMENT PROCESS OF BUSINESS TENDENCY SURVEYS ON TURNING POINTS DATING ITEM 6.2 ON THE AGENDA OF THE MEETING OF THE WORKING GROUP ON EUROINDICATORS

2 The Impact of the Seasonal Adjustment Process of Business Tendency Surveys on the Datation of Turning Points. Paper presented at the 29 th CIRET Conference Santiago, Chile, October Dominique Ladiray (INSEE, France), Gian Luigi Mazzi (Eurostat, Luxembourg) 1 Introduction Business and consumer surveys are usually considered as one of the main earlier indicators of the economic activity. They are then extensively used to anticipate and estimate in real time most of macroeconomic indicators such us GDP, IPI etc. Moreover they can also give an accurate timely picture of the growth cycle situation. For this reason they also are a powerful instrument for modern business cycle analysis based not only on classical NBER approach but also on an integrated view where classical and growth cycles are simultaneously analyzed. In order to increase the usefulness of business and consumer surveys for business cycle analysis, these need to be adequately filtered to remove seasonal as well as noise components so that the growth cycle can be properly estimated. The DG Economic and Financial Affairs of the European Commission uses the Dainties program to seasonally adjust the series. This method, based on asymmetric filters, has often been criticized as it introduces phase shifts and therefore cannot detect turning points on time (see for example Hylleberg, 1986; Nilson and Brunet, 2003; Bengoechea, 2003). Anyway, a quite recent study (Franses et al, 2005) showed that Dainties performs quite well with Business and Consumer Surveys: In Section 8, we compare the performance of the three seasonal adjustment methods 1 on 300 series from the business and consumer surveys. A main conclusion from this section is that there are no marked differences in the performance of the three seasonal adjustment methods. Another conclusion is that the data are close to the "deterministic seasonality case", which implies that the Dainties method is very useful in this context. This paper analyses the impact of different seasonal adjustment methods on the chronology of turning points. 7 seasonal adjustment methods are used on the business tendency survey data: Tramo-Seats, Baysea, Stamp, Decomp, X-12-Arima version , X-12-Arima version 0.3 and Dainties. In each case, and for each survey and geographic level, an estimation of the growth cycle is obtained using dynamic factor models, a method used by DG Economic and Financial Affairs to compute the actual climate and sentiment indicators. The chronologies of turning points are then exhibited using a Bry-Boschan datation algorithm (1971) and compared. 1 Dainties, X12-ARIMA and TRAMO/SEATS 1

3 The results of these simulations confirm that the Dainties method tends to detect the turning points later than the other seasonal adjustment methods. 2 The methodology 2.1 The data Business and consumer survey raw data are nowadays freely and easily available from the DG Economic and Financial Affairs website. A quick exploratory analysis of the data shows that: Some series present lots of missing values; Some surveys which were quarterly are now run on a monthly basis. In most of the cases, a monthly series has been derived from the quarterly series simply replicating the same value for the 3 concerned months. In the following study, we used the following data subset: Data from the Industry, Services, Retail trade and Construction surveys; Monthly data up to March 2006; Series with 1 or less missing values. In this case, the estimation of the missing value was done with Tramo; Balance series In summary, the simulations done in this study are based on 1966 series from 4 monthly surveys, 25 countries and 2 European levels (Euro-Area EA and European Union EU). 2.2 The process Each series was submitted to the following treatment: Estimation of missing values with Tramo Seasonal adjustment with 7 methods: BAYSEA, DAINTIES, DECOMP, STAMP, TRAMO-SEATS, X12-ARIMA release , X12-ARIMA release 0.3 Construction of a synthetic indicator using a factor analysis for each geographical level and each survey Turning point datation with a Bry-Boschan algorithm (the one used in the Phase Average Trend method, without detrending). Comparison of the chronologies using the DAINTIES seasonally adjusted series as reference Remarks In this study, we do not check the real time detection of turning points and the performance of concurrent filters (Wildi, Schips, 2004; Wildi, 2005, 2006) To estimate the cycle, we use a static factor analysis as the results are very similar to more complex methods (Gayer, Genet, 2006) We do not perform any trading-day adjustment even if all methods except Dainties could easily handle it. 2

4 2.3 Presentation of the results Table 1 presents the chronologies of turning points obtained from each seasonal adjustment method, for the construction survey and the Euro-Area. It clearly appears that the results are the same for 1993 and 1994 but quite strange for 1996 for which a very large delay (10 months can be observed). To make the results more readable and more robust, some basic transformations are done. Table 1: Chronologies of turning points obtained for the construction survey and the Euro-Area. Survey Country Software TP1 TP2 TP3 TP4 TP5 TP6 Building EA DAINTIES DEC89 OCT93 OCT94 DEC96 APR00 JAN03 Building EA BAYSEA DEC89 OCT93 OCT94 DEC96 AUG00 DEC02 Building EA DECOMP MAR90 OCT93 OCT94 FEB96 APR00 DEC02 Building EA STAMP OCT89 OCT93 OCT94 FEB96 MAR00 DEC02 Building EA TSEATS DEC89 OCT93 OCT94 FEB96 AUG00 DEC02 Building EA X12V2 OCT89 OCT93 OCT94 FEB96 AUG00 DEC02 Building EA X12V3 OCT89 OCT93 OCT94 FEB96 AUG00 DEC02 In a first step, considering the DAINTIES series as the reference series, we compute the differences, expressed in number of months, between the different dates. This leads to table 2. A negative value means that the software detects the turning point before Dainties. Table 2: Delays (in number of months) between the various chronologies of turning points obtained for the construction survey and the Euro-Area. Survey Country Software TP1 TP2 TP3 TP4 TP5 TP6 Building EA BAYSEA Building EA DECOMP Building EA STAMP Building EA TSEATS Building EA X12V Building EA X12V In a second step, to make the results more robust, we compute the number of Leading (negative delay), Coincident (0 delay), and Lagged (positive delay) datations (see Table 3). 3

5 Table 3: Number of times the method leads, meets or lags the Dainties chronology. Results obtained for the construction survey and the Euro-Area. Survey Country Software Leading Coincident Lagged Building EA BAYSEA Building EA DECOMP Building EA STAMP Building EA TSEATS Building EA X12V Building EA X12V In a last step, we compute balances e.g. the difference between the percentage of times the indicators leads and the percentage of times the indicator lags the Dainties chronology (see Table 4). Table 4: Performances of the various methods, expressed as balances, compared to the Dainties chronology. Results obtained for the construction survey and the Euro-Area. Survey Country Software Balance Building EA BAYSEA 0.00 Building EA DECOMP 0.17 Building EA STAMP 0.67 Building EA TSEATS 0.17 Building EA X12V Building EA X12V Results The complete results are presented in tables 5 to 12: Tables 5 and 6 show the results for the European aggregates Tables 7 to 12 present the results for the various surveys and the various geographical levels. A principal component analysis has been performed to summarize the results. Figures 1 and 2 show the projections of the variables (softwares) and observations (countries and surveys) in the first factorial plan. Generally speaking (see Table 7) it clearly appears that Dainties tends to detect the turning points later than the other softwares. This conclusion is also true for each survey. On the average, BAYSEA appears to perform slightly better than the other softwares. The performances of DECOMP, STAMP, TSEATS and X12V3 vary according to the survey: DECOMP performs better on the Industry and Services series; STAMP performs poorly on Services series; TSEATS and X12V3 perform better on Building and Industry series. 4

6 Some interesting differences can be observed according to the country: Dainties does not perform well on European aggregates, especially for the European Union (see Table 6); It performs very poorly for some specific countries: Luxembourg, Sweden, Slovenia; And very well for some others: Austria, Belgium and Hungary. Figure 1: Representation of the variables (softwares) in the first principal plan. 1,00 DECOMP BAYSEA STAMP Axe 2-1,00 0,00 1,00 X12V2 TSEATS X12V3-1,00 Axe 1 5

7 Figure 2: Representation of the observations (countries and surveys) in the first principal plan. 1,50 1,00 IE SK NL RO 0,50 AT CZ BG LT LV MT Services FI DK CY Retail_R PT ESDE 0,00 All Industry EL EA FR EU LU -4,00-3,00-2,00-1,00 IT 0,00 1,00 2,00 3,00 4,00 BE Building UK SE -0,50 SI EE Axe 2 HU -1,00-1,50-2,00-2,50 PL -3,00-3,50 Axe 1 6

8 Table 5: Number of times the method leads, meets or lags the Dainties chronology. Results obtained for the various surveys and the European aggregates. Building Eurozone (EA) European union (EU) Softw Leading Coincident Lagged Leading Coincident Lagged BAYSEA DECOMP STAMP TSEATS X12V X12V Industry Eurozone (EA) European union (EU) Softw Leading Coincident Lagged Leading Coincident Lagged BAYSEA DECOMP STAMP TSEATS X12V X12V Retail trade Eurozone (EA) European union (EU) Softw Leading Coincident Lagged Leading Coincident Lagged BAYSEA DECOMP STAMP TSEATS X12V X12V Services Eurozone (EA) European union (EU) Softw Leading Coincident Lagged Leading Coincident Lagged BAYSEA DECOMP STAMP TSEATS X12V X12V

9 Table 6: Average performances of the various methods, expressed as balances, compared to the Dainties chronology. Results obtained for the various surveys and the European aggregates. Building Industry Retail Services All BAYSEA DECOMP STAMP TSEATS X12V2 X12V3 EA EU All EA EU All EA EU All EA EU All EA EU All Table 7: Average performances of the various methods, expressed as balances, according to the survey. BAYSEA DECOMP STAMP TSEATS X12V2 X12V3 Building Industry Retail Services All

10 Table 8: Average performances of the various methods, expressed as balances, compared to the Dainties chronology. Results obtained for the construction survey and the various geographical levels. Survey Building BAYSEA DECOMP STAMP TSEATS X12V2 X12V3 Country AT BE BG CY DE DK EA EL ES EU FI FR HU IE IT LT LU LV NL PL PT SE SI UK All

11 Table 9: Average performances of the various methods, expressed as balances, compared to the Dainties chronology. Results obtained for the industry survey and the various geographical levels. Industry BAYSEA DECOMP STAMP TSEATS X12V2 X12V3 Country AT BE BG CY CZ DE DK EA EL ES EU FI FR HU IE IT LT LU LV MT NL PL PT RO SE SI SK UK All

12 Table 10: Average performances of the various methods, expressed as balances, compared to the Dainties chronology. Results obtained for the retail trade survey and the various geographical levels. Retail BAYSEA DECOMP STAMP TSEATS X12V2 X12V3 Country AT BE BG CZ DE DK EA EL ES EU FI FR IE IT LT LV NL PT RO SE SI SK UK All

13 Table 11: Average performances of the various methods, expressed as balances, compared to the Dainties chronology. Results obtained for the services survey and the various geographical levels. Services BAYSEA DECOMP STAMP TSEATS X12V2 X12V3 Country AT BE BG CY CZ DE DK EA EE EL ES EU FI FR HU IE IT LT NL PT RO SE SI SK UK All

14 Table 12: Average performances of the various methods, expressed as balances, compared to the Dainties chronology. Results obtained for the various geographical levels. All surveys BAYSEA DECOMP STAMP TSEATS X12V2 X12V3 Country AT BE BG CY CZ DE DK EA EE EL ES EU FI FR HU IE IT LT LU LV MT NL PL PT RO SE SI SK UK All

15 4 References [1] Bengoechea, P. (2003), The Impact of the Seasonal Adjustment Methods in the Determination of Turning Points of Business and Consumer Surveys, working paper, DG ECFIN, European Commission. [2] Boschan C., Bry G. (1971). Programmed selection of cyclical turning points, in Cyclical Analysis of Time Series: Selected Procedures and Computer Programmes, NBER [3] Fischer, B., (1995), Decomposition of Time Series: Comparing Different Methods in Theory and Practice, Version 2.1. EUROSTAT, working paper [4] Franses, P. H., Paap, R., Fok, D. (2005), Performance of Seasonal Adjustment Procedures: Simulation and empirical results, DG ECFIN, final report of the study on Seasonal adjustment of business and consumer survey data" (Contract number ECFIN /SI ), European Commission. [5] Hendyplan (1997), Rapport technique du projet de développement en C de la méthode Dainties pour la Commission Européenne note interne, DG ECFIN. [6] Hylleberg, S. (1986), Seasonality in Regression, Academic Press, Orlando. [7] Matas-Mir, A., Osborn, D. (2004), Seasonal Adjustment and the Detection of Business Cycle Phases, working paper series, n 357, European Central Bank, Frankfurt. [8] Nilsson, R., Brunet, O. (2003), Detection of Phase Shift in the Determination of Turning Points in Business and Consumer Tendency Surveys after Seasonal Adjustment by 3 Different Methods, working paper, OECD Statistics Directorate. [9] OSCE (janvier 1979), Manuel U/12/3 : Ajustements saisonniers (Système DAINTIES), note OS/2/79 de l Office Statistique des Communautés Européennes. [10] Wildi, M. (2004) Signal extraction: efficient estimation, Unit-root tests and early detection of turning-points. Number 547. Lecture notes in economics and mathematical systems, Springer. [11] Wildi, M., Schips, B. (2005) Signal extraction: How (in)efficient are modelbased approaches? An empirical study based on Tramo/Seats and Census X-12- arima. Working paper 105, Institute of Business Cycle Research, ETH-Zurich. [12] Wildi, M. (2006) Real time signal extraction and the new kofkonjunkturbarometer. Working paper 115, Institute of Business Cycle Research, ETH-Zurich,