Methodological work for modelling Data base for perennials at the regional level for major producing regions. Perennial sub-module in GAMS. Objectives Working paper 02 - 07 Sources identified. Almost all data compiled. Further operations needed Decision to be taken about the software to perform estimation and integration into GAMS
Methodological considerations • Problem • Approaches: Based on Time Series Analysis • Available statistical regional information on permanent crops is scarce • To capture the heterogenity of the production capacity • To capture the lagged decision making process • State Space Approach: Kalman Filter (KF1 and KF2) • Multinomial Logit Model (MLM)
Assesment and conclusions • The KF1 model • The KF2 model • . • The MLM improves the CAPRI approach by: Might be practical for the case of selected regions with available information, not for the whole system. Seems potentially feasible and innovative. However, still too many parameters to elicit. Estimation not sure to be ready and assessed during CAPSTRAT span • Extending the regional information • (CAPRI only used data for one triennial period). • Introducing economic variables at the RHS. • Making simulations possible..
Purpose: to obtain consistent estimated values for the shares of different crops in the total arable land. Shares are dependent on exogenous variables and error terms. Mathematical tools in order to get equations which are linear in parameters. From: To: Wit=(exp (fit+uit))/j(exp (fjt+ujt)) Log(Wit/Wt)=ai+jbijXjt+uit Log(Wit)=fit+uit-log(jexp (fjt+ujt)) • This method allows us to create a dynamic system by means of lagged, dependent variables. Log(Wit/Wt)≡Yit=ai+jbijXjt+dikYkt-1+uit Methodological remarks I: Multinomial Logit Model
Advantages: filling information gaps at regional level, separating estimation of the qualitatively different planting and removal decisions. State-space equations: y(k) = C x(k) + eyk x(k +1) = A x(k)+ B u(k)+ exk Methodological remarks II: State-space approach • Kalman filter: Given currents estimates of the state variables x^(k|k), the Kalman filter predicts the state value at the next period k+1, and then adjust the prediction with the measurement information.
Model Specifications: the MLM approach (I) First Stage: national level. Autorregresive models + Multinomial Logit Model Olives for oil Olives Table olives Table grapes Original data Vineyards Result: estimates of the shares of the 8 CAPSTRAT activities into the broader ones at the national level Table wines Apples,... Other wines Fruits Citrus Other fruits Second Stage: regional level Autorregresive models Olives Original data Vineyards Result: estimates of the broader activities at the regional level Fruits
Model Specifications: the MLM approach (II) Third Stage: combination of previous calculations First stage information: j and k CAPSTRAT activities at the national level and the annual rate of changes for the projection period = (1+ rj)/(1+ rk) Main assumption: the growing rate pattern observed at the national level inside each broad activity is “transferred” to the regional level projected regional ratio (j/k)= (initial ratio j/k) Second stage projections: broad activity= j+k Final result: estimates of the 8 perennial CAPSTRAT activities at the regional level, incorporating economic variables in the projections
Model Specifications: the State-Space approach (I) Comprehensive and detailed model (see WP 02-07) Young trees Original data: acreage of a perennial activity Productive trees Exogenous forecasting Young trees Projected acreage Productive trees STATE VARIABLES SYSTEM FORECASTS
Model Specifications: the State-Space approach (II) Allocation model: breakdown of a broad activity into more detailed ones Sub-activity 1 Original data: acreage of a broad activity Economic variables Sub-activity 2 Simulation: changes in the economic variables Exogenous forecasting Sub-activity 1 Economic variables Projected acreage Sub-activity 2 STATE VARIABLES SYSTEM FORECASTS