The unece organizes with the financial support of the World Bank

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

Otherwise, a risk of marginalisation of NSOs

Economic Crises – Conclusions

• 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

Turning Points Trend vs. Year-on-Year Rate Volume of Construction

Why Seasonally Adjust?

• Seasonal effects in raw data conceal the true underlying development

• Easier to interpret, reveals long-term development

• To aid in comparing economic 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

Why Original Data is Not Enough?

• Comparison with the same period of last year does not remove moving holidays

• 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

Seasonal Adjustment

• Seasonal adjustment is an analysis technique that:

• Estimates seasonal influences using procedures and filters
• Removes systematic and calendar-related influences

• Aims to eliminate seasonal and working day effects

• No seasonal and working day effects in a perfectly seasonally adjusted series

Interpretation of Seasonally Adjusted Data

• 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

Filter Based Methods

• 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

X-11 and X-11-ARIMA

• X-11

• 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

• 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

X-12-ARIMA

• http://www.census.gov/srd/www/x12a/

• Developed and maintained by the US Census Bureau

• Based on a set of linear filters (moving averages)

• User may define prior adjustments

• Fits a regARIMA 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

X-12-ARIMA

Model Based Methods

• 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

TRAMO/SEATS

• www.bde.es

• 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 for forecasting

• Powerful tool for detailed analyses of series

• Only proposes additive/log-additive decomposition

DEMETRA software

• http://circa.europa.eu/irc/dsis/eurosam/info/data/demetra.htm

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