Towards an Entrepreneurship Synthetic Indicator for Andalusia

1. Towards an Entrepreneurship Synthetic Indicator for Andalusia

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

3. 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 Justification

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

5. Selection of Partial Indicators Statistical treatment Filtering Aggregation Synthetic Index How?

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

7. Availability Data quality Significance Time series length Frequency Smoothed Operational Partial indicators selection criteria

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

9. Non-calendar effects, outliers,... Non-seasoned data Irregular components adjustment Previous treatment

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

11. Weighted sum NBER methodology Multivariate Analysis State-Space Alternative Methods of Aggregation

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

13. 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 Stock-Watson approach

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

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

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

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

19. The growth rate of the i-th macroeconomic variable, Δyi,t ,consists of two stochastic components: the common unobserved scalar “index” ΔCt and a idiosyncratic shock, ui,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. The Model (I)

20. The model is formulated as follows: The Model (II)

21. 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, Δyi,t , to the unobserved state vector which consists of ΔCt, ui,t and their lags. The state equation describes the evolution of the state vector. The Model (III)

22. The Model (IV) • Measurement equation:

23. State equation: The Model (V)

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

25. Results (I)

26. Results (II) Normalized data

27. Results (III) Correlations

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

29. 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 Conclusions

30. Thank you for your attention!