MASST – MAcroeconomic, Sectoral, Social and Territorial model Topics and problems

1. MASST – MAcroeconomic, Sectoral, Social and Territorial modelTopics and problems Andrea Caragliu – Politecnico di Milano

2. Aims of the project • The final goal of the project is forecasting future socio-economic trends for European regions over a period of 15 years from now. • However, currently my commitment is to the estimation stage.

3. Research steps • Drawing up of a sound theoretical model and definition of the appropriate econometric counterpart; • Estimation of the model; • Forecast of main relationships and definition of possible scenarios.

4. The MASST model - Logic scheme

5. Structure of the model where: Z = set of national demand variables K = set of regional structural variables T = set of regional territorial characteristics

6. The starting equation • I use the following decomposition of regional growth rates: where: yr = variation in the region’s GDP yn = variation in the nation’s GDP s = shift

7. Estimated equationsI – National component 1 – GDP variation where α = Parameters to be estimated ΔC = Consumption growth rate ΔI = Investment growth rate ΔG = Public expenditure growth rate ΔX = Exports growth rate ΔM = Imports growth rate

8. Estimated equationsI – National component 3 – Public expenditure growth rate 2 – Consumption growth rate Exogenous

9. Estimated equationsI – National component 4. Investment growth rate 5. Export growth rate

10. Estimated equationsII – Regional component s = f (human and economic resources; structual and sectoral characteristics; spatial spillover effects; integration processes; territorial features)

11. New territorial data

12. New socio-economic data

13. Spatial effects indicators

14. Traditional economic variables

15. Population growth rate

16. Database and indicators • The database is built for 27 Countries (all EU25 countries plus Bulgaria and Romania) and 259 regions (NUTS2). The national database is in panel form (1995-2002). • The database’s originality is due to: • The use of territorial and socio-economic data at NUTS2 level (so far inexistent), coming from other ESPON projects; • The use of other spillover indicators created for 259 regions; • Building up a database which is consistent with Eurostat and ESPON sources for which missing values were filled and consistency was checked.

17. Results of estimation of shift parameters

18. Open questions 1 - Econometrics • As I am estimating spatial spillover effects, most of the spatial autocorrelation should be already wiped out. Which kind of spcification test, in the shape of the Moran’s I, might I use in this case?

19. Open questions 1 - Econometrics • The spillover equation can be written as Therefore, I am already using income in the equation. Am I running into endogeneity of the regressors problem?

20. Open questions 1 - Econometrics • Regional shift effects do not automatically sum up to 0 (as we would wish for); instead, given the fact that the describing equation is filled with positive explanatory variables, they tend to be distorted towards positive values. Summing up to 0 is imposed in the estimation process; is there any alternative solution?

21. Open questions 2 - Economics • Calculated shift s, plotted for each year and each region, is characterized by high variane. That’s why its average over the period 1999-2002 is chosen. This choice should be econometrically correct, bu how do I motivate it from the theoretical point of view?

22. Open questions 2 - Economics • Again from the theoretic point of view, why is σ2s so high?

23. Open questions 2 - Economics • In the national equations subgroup, consumption growth rate was described by the following expression: It is in reduced form, which is a technique used in all the equations. Given its econometrically accetable use, how do I justify it from the economic perspective?