Content

1. Content • Customized Optimization Criteria • Forecasting • Real-time signal extraction

2. Forecasting A Practical Example: ESI A Simulated Example: one- vs multi-step NN3-Competition: Customized Criteria

3. The Economic Sentiment Indicator

4. ESI (EFN Report)

5. Comments • Forecasting-model: • Integrated process • Forecasting intervals spread out rapidly • Point-forecasts do not converge to the mean • Series is bounded: integration=misspecification • The 40%-Interval (and a fortiori higher confidence intervals) contains both trend directions • Impossible to infer the occurrence of a turning-point • Forecasts are uninformative

6. An Artificial Example(Dynamics Close to Business Surveys) Model-Misspecification Multi-Step Ahead Forecasting

7. Artificial Time Series (close to KOF-Economic Barometer)

8. Forecasting-Model and Diagnostics

9. Problems • In applications TRAMO and/or X-12-ARIMA often identify airline-models • Misspecification cannot be detected • One-step ahead forecasts are good • σ=1.16 (true innovations are N(0,1) ) • What about multi-step ahead performances?

10. Multi-step ahead Forecasts0 months after TP1 of cycle

11. Multi-step ahead Forecasts6 months after TP1 of cycle

12. Multi-step ahead Forecasts1 year after TP1 of cycle

13. Multi-step ahead Forecasts 20 months after TP1 and 0 months after TP2

14. Multi-step ahead Forecasts3 months after TP 2

15. Multi-step ahead Forecasts6 months after TP 2

16. Comments • One-step ahead forecasts are good • σ=1.16 • Poor multi-step ahead performance • Huge delays

17. Multi-step ahead 95% Interval-Forecasts: 6 months after TP2

18. Multi-step ahead 50% Interval-Forecasts: 6 months after TP2

19. Comments • Forecast intervals spread out much too rapidly • It is impossible to assert the occurrence of TP’s • even 50%-intervals are completely uninformative

20. NN3 Customized Criterion

21. Receive updates: • www.neural-forecasting-competition.com

22. Competitors • Theta-model (winner of M3) • Forecast-Pro (best commercial package M3) • Autobox (ARIMA-based high-performer) • X-12-ARIMA • Latest neural networks designs • …

23. NN3 Results Results on the Complete Dataset of 111 Time Series This represents the actual benchmark of the NN3 competition, as the reduced dataset of 11 series is included in the 111. Congratulations to all of you that were able to forecast this many time series automatically! Please find the results for the top 50% of submissions released below by name and description. All other participants must contact the competition organisers via email to agree the disclosure of their name and method with their rank.

24. NN3 Results Results on the Complete Dataset of 111 Time Series This represents the actual benchmark of the NN3 competition, as the reduced dataset of 11 series is included in the 111. Congratulations to all of you that were able to forecast this many time series automatically! Please find the results for the top 50% of submissions released below by name and description. All other participants must contact the competition organisers via email to agree the disclosure of their name and method with their rank.

25. Summary • Well-designed (customized) optimization criterion performs best • Prototypical package • NN3-series were the first series passed through the code • No experience, limited time • 2 weeks for code implementation and computations • We expect substantial `fine-tuning’-potential

26. A Methodological Essay for the ESI • Forecast the series by focusing on TP’s • Traditional ARIMA-based approaches perform worst in TP’s • Real-time signal extraction • Relevant customized criteria • Real-time TP-filter

27. Real-Time Signalextraction Research Purpose, Signal and Real-Time Problem MBA DFA Example: Economic Sentiment Indicator

28. Research Purpose, Signal and Real-Time Problem

29. Research Purpose • Anticipate TP’s (GDP,ESI,…) • Data: selected KOF-Business-surveys • Questionnaire - and hence data - are informative about growth-rate (not level) • Data is contaminated by noise and seasonal components • Signal: • Eliminate noise and seasonal components

30. Business-cycle frequencies (cutoff 1.5 y.) • Research purpose: anticipate TP’s of GDP • Signal: • Trend = output of the transfer function on the right hand side. • Signal definition matches research purpose

31. Symmetric MA- and Real-Time Filters

32. MBA’s

33. Traditional Approach (TRAMO/SEATS, X-12-ARIMA) • Identify a time series model for the DGP • Forecast the future • One- and multi-step forecasts • Apply the symmetric filter to the extended time series

34. Problem 1:Model-Misspecification • Business survey data: bounded time series

35. Problem 2: multi-step forecasting performance • Models perform well with respect to short-term (one-step) forecasting • Poor multi-step ahead performance • Performance particularly poor in TP’s • Mean-reversion is not accounted for by misspecified integrated processes • TP’s cannot be accounted for explicitly by traditional MBA’s

36. DFA Customized Criteria

37. DFA: new `Customized’ Optimization Criteria • Level criterion: • TP criterion : • Book, chapters 3-5

38. DFA TP-criterion • Speed and reliability of the real-time filter can be accounted for explicitly • λ: speed • W(ω): reliability (smoothness) • Improved performance specifically in TP‘s • User preferences (risk-aversion) can be accounted for explicitly • Both criteria match exactly the structure of the relevant estimation problems • Efficiency

39. ESI DFA versus Dainties

40. 1996:2001 DFA is both fast and reliable

41. Towards the current boundaryRecall ESI-Forecasts (EFN Report)

42. Summary • Very effective detection of TP’s • Very fast (delay zero) • Very reliable (no false alarms between consecutive TP’s) • TP-filter can neatly improve over traditional ARIMA-based forecasts

43. Amplitude and time delay (DFA)

44. Response by the EC • The approach of the European Commission to adjusting the business and consumer surveys (BCS) has always been seasonal adjustment, not trend/cycle extraction, i.e. smoothing. • The irregular component carries information on respondents‘ perception of economically relevant special events such as strikes, elections or strong exchange rate or commodity price movements, we believe that retaining the component is important in interpreting the data. • To my knowledge, the seasonal adjustment approach is also the dominant, if not exclusive, approach used by the national survey institutes…

45. Response by the EC • Of course, the real-time trend/cycle extraction approach of your DFA filter is very relevant in its own right for the identification of the cycle and its turning points. Therefore, your work is very interesting and stimulating. • The calculation and publication of such smooth indicators representing the trend/cycle component is not among our priorities for the nearer future, we would still be interested in learning more about the possible advantageous or complementary features of your DFA approach with respect to our traditional approach of processing the BCS data.

46. Non-Response by contributors to EFN-Report • The relevant forecasting issues ere addressed several times to all contributors • IGIER, CEPII, IWH, EUI, AQR, IFL, DAE, • Coordinator of report • Book, NN3, articles • No response! • Typical monopolistic behavior • Lack incentives and/or time to scrutinize methods and/or to improve performances

47. Available Material • Theory: • latest book “Real-Time Signal Extraction: Beyond Maximum Likelihood Principles” • `Open source’ pdf-vintages: www.idp.ch • On-going project • Software: • R-package signalextraction • CRAN-depository: cran.r-project.org • Questions/remarks: marc.wildi@zhaw.ch