1. SERG Universidad de Huelva Towards an Entrepreneurship Synthetic Indicator for Andalusia

2. SERG Universidad de Huelva Schedule Justification How? Partial indicators selection Statistical treatment Aggregation Methodology Data Correlations The model Results Conclusions

3. SERG Universidad de Huelva Justification An Entrepreneurship Indicator System (as SICIEA) has a lot of “partial” indicators which approaches similar dimensions for a frequency. For this reason is necessary to obtain a composite or synthetic index In addition this kind of index are very useful for monitoring and forecasting In this context, we are working in the development of a entrepreneurship synthetic index from SICIEA data

4. SERG Universidad de Huelva Schedule Justification How? Partial indicators selection Statistical treatment Aggregation Methodology Data Correlations The model Results Conclusions

5. SERG Universidad de Huelva How? Selection of Partial Indicators Statistical treatment Filtering Aggregation Synthetic Index

6. SERG Universidad de Huelva Schedule Justification How? Partial indicators selection Statistical treatment Aggregation Methodology Data Correlations The model Results Conclusions

7. SERG Universidad de Huelva Partial indicators selection criteria 1.Availability 2.Data quality 3.Significance 4.Time series length 5.Frequency 6.Smoothed 7.Operational

8. SERG Universidad de Huelva Schedule Justification How? Partial indicators selection Statistical treatment Aggregation Methodology Data Correlations The model Results Conclusions

9. SERG Universidad de Huelva Previous treatment Non-calendar effects, outliers,... Non-seasoned data Irregular components adjustment

10. SERG Universidad de Huelva Schedule Justification How? Partial indicators selection Statistical treatment Aggregation Methodology Data Correlations The model Results Conclusions

11. SERG Universidad de Huelva Alternative Methods of Aggregation Weighted sum NBER methodology Multivariate Analysis State-Space

12. SERG Universidad de Huelva Schedule Justification How? Partial indicators selection Statistical treatment Aggregation Methodology Data Correlations The model Results Conclusions

13. SERG Universidad de Huelva Stock-Watson approach Suppose n partial indicators for a variable composed by two components: A common factor An idiosyncratic specific factor The common factor might be associated with co-movements with the main macroeconomic indicators Extracting this common factor we can estimate a synthetic index

14. SERG Universidad de Huelva Schedule Justification How? Partial indicators selection Statistical treatment Aggregation Methodology Data Correlations The model Results Conclusions

15. SERG Universidad de Huelva Exercise with Spanish Data Spanish labour series: 1.Private wage earners 2.Public wage earners 3.Employers 4.Own account workers Frequency: Quaterly Source: Instituto Nacional de Estadística (INE) From 1987:2 to 2004:4

16. SERG Universidad de Huelva Schedule Justification How? Partial indicators selection Statistical treatment Aggregation Methodology Data Correlations The model Results Conclusions

17. SERG Universidad de Huelva Contemporaneous correlations between growth rates of the variables PPBEO P 1.000 PB 0.0671.000 E 0.0970.0651.000 O -0.1210.118-0.2331.000

18. SERG Universidad de Huelva Schedule Justification How? Partial indicators selection Statistical treatment Aggregation Methodology Data Correlations The model Results Conclusions

19. SERG Universidad de Huelva The Model (I) The growth rate of the i-th macroeconomic variable, Δy i,t,consists of two stochastic components: the common unobserved scalar “index” ΔC t and a idiosyncratic shock, u i,t. Both, the unobserved index and the idiosyncratic shocks are modeled as having autoregressive stochastic process, AR(1). i denote the logarithm of a macroeconomic time-series labour variable, private and public wage earners, employers with and without workers.

20. SERG Universidad de Huelva The Model (II) The model is formulated as follows:

21. SERG Universidad de Huelva The Model (III) To estimate the model, we transform it into a state space form likelihood function. The state space form has both the state equation so that the Kalman filter can be used to evaluate it and the measurement equation. The measurement equation relates the observed variables, Δy i,t, to the unobserved state vector which consists of ΔC t, u i,t and their lags. The state equation describes the evolution of the state vector.

22. SERG Universidad de Huelva The Model (IV) Measurement equation:

23. SERG Universidad de Huelva The Model (V) State equation:

24. SERG Universidad de Huelva Schedule Justification How? Partial indicators selection Statistical treatment Aggregation Methodology Data Correlations The model Results Conclusions

25. SERG Universidad de Huelva Results (I)

26. SERG Universidad de Huelva Results (II) Normalized data

27. SERG Universidad de Huelva Results (III) Correlations Correlations between synthetic index and GDP GDP (t-4) GDP (t-3) GDP (t-2) GDP (t-1) GDP (t) GDP (t+1) GDP (t+2) GDP (t+3) GDP (t+4) GDP0,3770,4060,4310,4620,4880,514 0,522 0,5040,468

28. SERG Universidad de Huelva Schedule Justification How? Partial indicators selection Statistical treatment Aggregation Methodology Data Correlations The model Results Conclusions

29. SERG Universidad de Huelva Conclusions This work constructed a composite index using different labour series in Spain and demonstrated their usefulness in leading the business cycle (GDP growth) However: very preliminary version

30. SERG Universidad de Huelva Thank you for your attention!