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Using Demetra+ in daily work at NBP – SA in the time of crisis

Using Demetra+ in daily work at NBP – SA in the time of crisis. Sylwia Grudkowska, Department of Statistics, NBP. The study. Starting point:

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Using Demetra+ in daily work at NBP – SA in the time of crisis

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  1. Using Demetra+ in daily work at NBP – SA in the time of crisis Sylwia Grudkowska, Department of Statistics, NBP

  2. The study • Starting point: • When the drastic change in the time series’ mean takes place, the quality of SA results is often decreasing. Such disturbances are frequently observed e.g. during economic crises. In such cases the inclusion of the Ramp effects in the pre-processing part might help. • Problem: • Users often do not have expert knowledge about SA methods and software so it is hard for them to improve the model by themselves. • Aim: • To develop the semi-automatic algorithm for detecting Ramps that enhance the results from the Automatic Model Identification procedure of Tramo/Seats.

  3. When series is worth considering? • Key indicators of poor SA • Statistics on residuals: • Normality, • Randomness, • Linearity, • Independence, • Size of out of sample forecast error, • Residuals from Tramo without outlier correction.

  4. How to specify the position of the Ramp? • Stage 1 – the initial choice of Ramp effect • The presence of LS/TC (or the series of LS and TC), • The local maximum and minimum of the trend-cycle during its drastic change (if no LS/TC detected). • Stage 2 – the specification of Ramps • For the start of the Ramp effect: up to 2 periods earlier and later from the initial starting date, • For the end of the Ramp effect: up to 2 periods earlier and later from the initial ending date. • Stage 3 – the final choice • Quality check – residuals, • Significance of RegARIMA model’s parameters, • Choice based on BIC criterion.

  5. Example • Volume index of production 2010=100, Hungary - Electricity, gas, steam and air conditioning supply

  6. Stage 1 • Results – estimation span: [1998-1 : 2009-8]

  7. Stage 2 • Specifications considered: • [2009.02 – 2009.04] BIC = -6,1000 • [2009.03 – 2009.04] BIC = -6,1366 /skeweness present • [2009.03 – 2009.05] BIC = -6,1056 /skeweness present • [2009.04 – 2009.05] BIC = -6,0781 /RP not significant • [2009.04 – 2009.06] BIC = -6,1004 /skeweness present

  8. Stage 3 • Comparison of the results

  9. Potential problems • National calendars – difficulties with keeping up-to-date calendars, • Initial choice of the Ramp effect’s date in case of no LS/TC detected, • Differences between Demetra+ and TSW results in some cases.

  10. First findings • AMI works surprisingly well, • Areas where the significant improvement can be achieved: • Industrial production, • Consumption, • Business surveys, • …

  11. Questions to be answered during the study • What is the average size of an improvement in area of forecasts? • In which sectors of economy the improvement is the largest? • How many series need the Ramps’ treatment? • What is the average time for RegARIMA models (without Rams) to capture the change in the trend-cycle?

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