bfbc8cf0368d3811325bb33d36adf8ea.ppt

- Количество слайдов: 21

UNECE Workshop on Short-Term Statistics (STS) and Seasonal Adjustment 14 – 17 March 2011, Astana, Kazakhstan Why Seasonally Adjust and How? Approaches X-12 -ARIMA, TRAMO/SEATS, X-13 A/S and Tools Anu Peltola Economic Statistics Section, UNECE

Overview v v v What and why Basic concepts Methods Software Recommendations Useful references March 2011 UNECE Statistical Division Slide 2

A Coyote Moment Did We Notice the Turning Point? March 2011 UNECE Statistical Division Slide 3

Economic Crises – Statistics v Did we give any warnings? A responsibility for the statistical offices? A new task? • Important to all users of statistics • w v Not only to politicians, but also to enterpreneurs and citizens Statistical offices often have monopoly to analyze detailed data sets We should not forecast, but draw attention to statistics • Identify changes early, leading indicators, develop more flash estimates -> quality vs. timeliness • v Otherwise, a risk of marginalisation of NSOs March 2011 UNECE Statistical Division Slide 4

Economic Crises – Conclusions v Some limits of official statistics were highlighted by the critics: lack of comparability among countries • need for more timely key indicators • need for statistical indicators in areas of particular importance for the financial and economic crisis • Source: Status Report on Information Requirements in EMU March 2011 UNECE Statistical Division Slide 5

Turning Points Trend vs. Year-on-Year Rate Volume of Construction March 2011 UNECE Statistical Division Slide 6

Why Seasonally Adjust? v Seasonal effects in raw data conceal the true underlying development • v To aid in comparing economic development • v v Easier to interpret, reveals long-term development Including comparison of countries or economic activities To aid economists in short-term forecasting To allow series to be compared from one month to the next • Faster and easier detection of economic cycles March 2011 UNECE Statistical Division Slide 7

Why Original Data is Not Enough? v Comparison with the same period of last year does not remove moving holidays • v v v If Easter falls in March (usually April) the level of activity can vary greatly for that month Comparison ignores trading day effects, e. g. different amount of different weekdays Contains the influence of the irregular component Delay in identification of turning points March 2011 UNECE Statistical Division Slide 8

Seasonal Adjustment v Seasonal adjustment is an analysis technique that: Estimates seasonal influences using procedures and filters • Removes systematic and calendar-related influences • v Aims to eliminate seasonal and working day effects • No seasonal and working day effects in a perfectly seasonally adjusted series March 2011 UNECE Statistical Division Slide 9

Interpretation of Seasonally Adjusted Data v In a seasonally adjusted world: Temperature is exactly the same during both summer and winter • There are no holidays • People work every day of the week with the same intensity • Source: Bundesbank March 2011 UNECE Statistical Division Slide 10

Filter Based Methods v v X-11, X-11 -ARIMA, X-12 -ARIMA (STL, SABL, SEASABS) Based on the “ratio to moving average” described in 1931 by Fredrick R. Macaulay (US) Estimate time series components (trend and seasonal factors) by application of a set of filters (moving averages) to the original series Filter removes or reduces the strength of business and seasonal cycles and noise from the input data March 2011 UNECE Statistical Division Slide 11

X-11 and X-11 -ARIMA X-11 v v Developed by the US Census Bureau Began operation in the US in 1965 Integrated into software such as SAS and STATISTICA Uses filters to seasonally adjust data X-11 -ARIMA v v v Developed by Statistics Canada in 1980 ARIMA modelling reduces revisions in the seasonally adjusted series and the effect of the end-point problem No user-defined regressors, not robust against outliers March 2011 UNECE Statistical Division Slide 12

X-12 -ARIMA http: //www. census. gov/srd/www/x 12 a/ v v v v Developed and maintained by the US Census Bureau Based on a set of linear filters (moving averages) User may define prior adjustments Fits a reg. ARIMA model to the series in order to detect and adjust for outliers and other distorting effects Diagnostics of the quality and stability of the adjustments Ability to process many series at once Pseudo-additive and multiplicative decomposition X-12 -Graph generates graphical diagnostics March 2011 UNECE Statistical Division Slide 13

X-12 -ARIMA Source: David Findley and Deutsche Bundesbank March 2011 UNECE Statistical Division Slide 14

Model Based Methods v v v TRAMO/SEATS, STAMP, ”X-13 -ARIMA/SEATS” Stipulate a model for the data (V. Gómes and A. Maravall) Models separately the trend, seasonal and irregular components of the time series Components may be modelled directly or modelling by decomposing other components from the original series Tailor the filter weights based on the nature of the series March 2011 UNECE Statistical Division Slide 15

TRAMO/SEATS www. bde. es v v v By Victor Gómez & Agustin Maravall, Bank of Spain Both for in-depth analysis of a few series or for routine applications to a large number of series TRAMO preadjusts, SEATS adjusts Fully model-based method forecasting Powerful tool for detailed analyses of series Only proposes additive/log-additive decomposition TRAMO = Time Series Regression with ARIMA Noise, Missing Observations and Outliers SEATS = Signal Extraction in ARIMA Time Series March 2011 UNECE Statistical Division Slide 16

DEMETRA software http: //circa. europa. eu/irc/dsis/eurosam/info/data/demetra. htm v v v By EUROSTAT with Jens Dossé, Servais Hoffmann, Pierre Kelsen, Christophe Planas, Raoul Depoutot Includes both X-12 -ARIMA and TRAMO/SEATS Modern time series techniques to large-scale sets of time series To ease the access of non-specialists Automated procedure and a detailed analysis of single time series Recommended by Eurostat March 2011 UNECE Statistical Division Slide 17

X-12 -ARIMA vs. TRAMO/SEATS Source: Central Bank of Turkey (2002): Seasonal Adjustment in Economic Time Series. March 2011 UNECE Statistical Division Slide 18

Demetra+ software v Users can choose: Tramo-Seats model-based adjustments • X-12 -ARIMA • v v One interface Aims to improve comparability of the two methods Uses a common set of diagnostics and of presentation tools Necmettin Alpay Koçak is a member of the testing group March 2011 UNECE Statistical Division Slide 19

Common Guidelines 1. Use tools and software supported widely • • • 2. 3. 4. 5. Demetra+ will be supported by Eurostat Methodological guidelines will be available Results will be more comparable Use your national calendars Dedicate enough human resources to SA Define a SA strategy Aim at a clear message to the users • • March 2011 Consider which series serve the purpose of the indicator Document all relevant choices and events UNECE Statistical Division Slide 20

Useful references v v v v Eurostat is preparing a Handbook on Seasonal Adjustment ESS Guidelines on Seasonal Adjustment http: //epp. eurostat. ec. europa. eu/cache/ITY_OFFPUB/KS-RA-09 -006/EN/KSRA-09 -006 -EN. PDF Central Bank of the Republic of Turkey (2002). Seasonal Adjustment in Economic Time Series. http: //www. tcmb. gov. tr/yeni/evds/yayin/kitaplar/seasonality. doc Hungarian Central Statistical Office (2007). Seasonal Adjustment Methods and Practices. www. ksh. hu/hosa US Census Bureau. The X-12 -ARIMA Seasonal Adjustment Program. http: //www. census. gov/srd/www/x 12 a/ Bank of Spain. Statistics and Econometrics Software. http: //www. bde. es/servicio/software/econome. htm Australian Bureau of Statistics (2005). Information Paper, An Introduction Course on Time Series Analysis – Electronic Delivery. 1346. 0. 55. 001. http: //www. abs. gov. au/ausstats/[email protected] NSF/papersbycatalogue/7 A 71 E 7935 D 23 BB 17 CA 2570 B 1002 A 31 DB? Open. Document March 2011 UNECE Statistical Division Slide 21