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forecast_tool_captrd [2020/02/27 11:12] – [Step 3: Adding comprehensive sets of supports from AGLINK or other agencies] matszforecast_tool_captrd [2022/11/07 10:23] (current) – external edit 127.0.0.1
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 Furthermore, methodology was needed to avoid a break in the projections at the transition of medium run expert information (Aglink-COSIMO, up to 2020) and long run information (FAO/IFPRI for 2050). For this purpose a variable weighting scheme is introduced (in //‘gams\captrd\expert_support.gms’//) that gives an increasing weight to our “long run” sources (FAO/IFPRI) as the projection horizon approaches 2050. This tends to give projections that gradually approach the long run sources, for example as in the case of pork production in Hungary (taken from a baseline established in November 2011). Furthermore, methodology was needed to avoid a break in the projections at the transition of medium run expert information (Aglink-COSIMO, up to 2020) and long run information (FAO/IFPRI for 2050). For this purpose a variable weighting scheme is introduced (in //‘gams\captrd\expert_support.gms’//) that gives an increasing weight to our “long run” sources (FAO/IFPRI) as the projection horizon approaches 2050. This tends to give projections that gradually approach the long run sources, for example as in the case of pork production in Hungary (taken from a baseline established in November 2011).
 +
 +**Figure 11: Pork production in Hungary as an example for merging medium run and long run a priori information in the CAPRI baseline approach**
 +
 +{{::figure11.png?600|}} \\ Source: own elaboration
 +
 +The example has been chosen because historical trends (and Aglink-COSIMO projections) on the one hand and long run expectations differ markedly. This is not unusual because medium run forecasts often give a stronger weight to recent production trends, often indicating a stagnating or declining production in the EU, whereas the long run studies tend to focus on the global growth of food demand in the coming decades. The simple trends (filled triangles) would evidently give unreasonable, even negative forecasts after 2030. Already the imposition of constraints from relationships to other series would stabilise the projections and imply some recovery after 2030 (filled squares). The year 2020 supports from Aglink-COSIMO (not shown) produces some upward correction of the step 2 results for 2020, giving a final projection (filled circles) of about 375 ktons for pork production in Hungary. This is also the starting point for the specification of the long run support (empty circles) which is a weighted average of two components. The first is a linear interpolation to the external projection from FAO/IFPRI for 2050 (empty triangles).  The second is a nonlinear damped extrapolation of the medium run projection beyond 2020 (empty squares). Changing the weight for the first component (FAO/IFPRI support) with increasing projection horizon creates a long run target value (empty circles) that gives a smooth transition from the medium to the long run. As the final projections (filled circles) tend to follow these target values, they show a turning point in the future evolution of pork production in Hungary that ultimately reflects the consideration of increasing global demand underlying the FAO/IFPRI projections. 
 +
 +Evidently this approach is quite removed from economic modelling and it is not intended to be. Instead it tries to synthesize the existing projections from various agencies, each specialised in particular fields and time horizons, in a technically consistent and plausible manner. The specification of a constraint set and penalties of the objective function translates plausibility in an operational form. Technical consistency is imposed through the system of constraints active during the estimation.
 +
 +====Step 4: Breaking down results from Member State to regional and farm type level====
 +
 +Even if it would be preferable to add the regional dimension already during the estimation of the variables discussed above, the dimensionality of the problem renders such an approach infeasible. Instead, the step 3 projection results regarding activity levels and production quantities are taken as fixed and given, and are distributed to the regions minimizing deviation from regional supports. The aggregation conditions for this step (and correspondingly for the disaggregation of NUTS2 regions to farm types) are:
 +
 +  * Adding up of regional production to Member State production (//MSGROF_//)
 +  * Adding up of regional agricultural and non-agricultural areas to Member State areas (eqs. //MSLEVL_// and //MSLANDUSE_//)
 +  * Adding up of regional feed use by animal types to Member State values (//MSFEEDI_//).
 +
 +The results at Member State level are thus broken down to regional level, ensuring adding up of production, areas and feed use:
 +
 +\begin{equation}
 +X_{MS,i,t}^{GROF,Trend}=\sum_{r\in MS}X_{r,i,t}^{GROF,Trend}
 +\end{equation}
 +
 +\begin{equation}
 +X_{MS,"levl",t}^{j,Trend}=\sum_{r\in MS}X_{r,"levl",t}^{j,Trend}
 +\end{equation}
 +
 +\begin{equation}
 +X_{MS,"levl",t}^{j,Trend}\cdot \left(X_{MS,"feed",t}^{j,Trend}+10 \right)=\sum_{r\in MS}X_{r,"levl",t}^{j,Trend}\cdot \left(X_{r,"feed",t}^{j,Trend}+10 \right)
 +\end{equation}
 +
 +The addition of the “10” (kg/animal) considerably improves the scaling in case of very small quantities (say 1 gram per animal). This is an example of a technical detail that may be crucial for numerical stability but usually cannot be reported fully in this documentation. 
 +
 +In addition to the above aggregation conditions, the lower level (NUTS2 or farm type) models only require the following constraints (as the market variables are already determined at the MS level):
 +
 +  * Related to areas: area balance (Equation 57 FIXME), obligatory set aside (Equation 80 FIXME), aggregation to groups like cereals (0).
 +  * Related to yields: linkage of production, activity levels and yields (Equation 55 FIXME), stabilisation of straw yields (//STRA_//)
 +  * Related to animals: Nutrient balances (Equation 65 FIXME), local use of fodder (//EFED_//), definition of livestock density (//LU_//).
 +
 +In order to keep developments at regional and national level comparable, relative changes in activity levels are not allowed to deviate very far from the national development. These bounds are widened in cases of infeasibilities.
 +
 +Table below contains an example of the final output of the trends estimation task (C:\....CAPRI\STAR\star_2.4\output\results\baseline\results_BBYY.gdx), where BB stands for base year and YY for simulation year). Its main purpose is to provide with explanations on the variables of this output and, thus, a possibility to review the results in a step-by-step manner.
 +
 +**Table 24: Example of the final output of the trends estimation task and description of the variables**
 +^Product code^  Activity code  ^  Variables  ^  Years  ^^^^^^^^^  Explanations  ^
 +^  ^ ^ ^1984^…^2009^2010^2011^2012^2013^2014^2015^:::^
 +^SWHE^ SWHE^ BASM | | | | | | | | |    8337|Base year value from Build database workstep.|
 +^ ^ ^Penalty | | | | | | | | |  0.2|"squared root" difference between actual estimate and support value. The larger the value, the farer the estimate from support.|
 +^ ^ ^Lo | | | | | | | | |  8080| Lower estimation bound.|
 +^ ^ ^ DGAgri1 | | | 8876| 8385 |8046 |8109| 8632| 8996| 9167| Projection of Aglink-Cosimo for the EU15 aggregate scaled to fit the CAPRI database.((Aglink-Cosimo model produces projections not for each EU MS, but for the EU aggregates: EU, EU "old" MSs and EU "new" MSs. During the calibration process these values are first scaled to better fit the CAPRI database. These scaled values are then used for the calibration procedure.))|
 +^ ^ ^ TrustLevl | | | | | | | | |  3| Exogeneous value used for restricting min and max values of the support values. It is used in calculating lower and upper bounds (up and lo) of the projections.|
 +^ ^ ^ data | | | | | | | | | | |  
 +^ ^ ^ BAST | | | | | | | | |  8579| Simple average of the last 3 observation years available: 2012-2014.|
 +^ ^ ^ B2000 | | | | | | | | |    7988| |
 +^ ^ ^ support | | | | | | | | |  9167| Values estimated as linear combination of Step1 and BAST (BASM) with R2 as weight. They are replaced with expert support where applicable and then scaled. They are then stored as Support1. Support is then redefined based on the Aglink-Cosimo value.((The final version of the support value at MS level (if calibration to the projections of Aglink-Cosimo takes place), is calibration value derived from DgAgri1.))|
 +^ ^ ^ support1 | | | | | | | | |    8943| (expert) support value, before introduction of Aglink-Cosimo calibration values. |
 +^ ^ ^ step1 | | | | | | | | |  8918| 1) Result of estimation of unconstrined trends|
 +^ ^ ^ step2 | | | | | | | | |  8851|2) Results of solving the trend model with constraints at MS level and with support1|
 +^ ^ ^ step3 | | | | | | | | |  8949|3) First, it is defined as results of solving trend model with constraints at MS level and with support (defined with Aglink-Cosimo value). Then, it is redifined with the results from solving this trend model with additional constraints at NUTS2 level.  |
 +^ ^ ^ wVarErr | | | | | | | | |  259353| Error variance.
 +^ ^ ^ CoefVarErr | | | | | | | | | 0.1| |  
 +^ ^ ^ Extrap | | | | | | | | | |  |  
 +^ ^ ^ Longrun| | | | | | | 8553 | 8579 | 8633| |
 +^ ^ ^ Longrun1 | | | | | | | | | | |  
 +^ ^ ^ P_Data |6975| …| 9061| 8614| 8078 |8139| 8810| 8789|  | Historical data – output of Build database. The last observation year – 2014.|
 +^ ^ ^ series |6975| … |9061 |8614 |8078 |8139|8810 |8789 |  8949| Historical values (until 2014) and projected values (starting from 2015 with 5-year step, as defined in the GUI setting for the Trends projection task). The projected values are "copied" from Step 3.((Because the last observation year is 2014, values in 2015 are prjections.))((Values in 2020, 2025 and 2030 are projections as well but are not presented in this example.))|
 +^ ^ ^ up | | | | | | | | |      8978 |Upper estimation bound|  
 +Source: own compilation. Comments: SWHE in Product code column indicates soft wheat commodity. SWHE in Activity code indicates yield of soft wheat. The CAPRI model used for this example was calibrated to the projections of Aglink-Cosimo model. 
 +
  
forecast_tool_captrd.1582801938.txt.gz · Last modified: 2022/11/07 10:23 (external edit)

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