CAPRI
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capri:concept:refrun [2011/04/11 11:37]
131.220.212.14
capri:concept:refrun [2014/06/25 11:17] (current)
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       from global to regional scale for 8-10 year time horizon, at the current legislation.       from global to regional scale for 8-10 year time horizon, at the current legislation.
  
-      It integrates agricultural market projections from other insitutions ​as FAPRI, FAO and DG-AGRI.+      It integrates agricultural market projections from other institutions ​as OECD, FAPRI, FAO and DG-AGRI.
  
-      Unique for CAPRI are regional resolution below the national level for EU27 at the level of+      Unique for CAPRI is the regional resolution below the national level for EU27 at the level of
  
-      NUTS 2 regions, and the Bayesian methodology applied.</​P>​+      NUTS 2 regions ​and farm types inside NUTS 2 region, and the Bayesian methodology applied.</​P>​
  
  
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- +      ​<P> 
- +      ​In opposite to many other reference run approaches, CAPRI employs a Bayesian estimation
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-      ​<P>In opposite to many other reference run approaches, CAPRI employs a Bayesian estimation+
  
       framework to define a mutually consistent set of projection values for activity levels,       framework to define a mutually consistent set of projection values for activity levels,
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       framework which maximizes the joint posterior density for given a priori distribution       framework which maximizes the joint posterior density for given a priori distribution
  
-      of the different elements.</​P>​+      of the different elements. The a priori distribution is wherever possible derived to from exogenous forecasts or expert information.</P>
  
  
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       <​P>​The a priori distributions are derived from two different sources. For all elements,       <​P>​The a priori distributions are derived from two different sources. For all elements,
  
-      long terms are projected. Using "no change"​ as the zero hypothesis, a weighted average+      long term trends ​are projected. Using "no change"​ as the zero hypothesis, a weighted average
  
       between the base year value and the trend estimate is calculated, using R squared       between the base year value and the trend estimate is calculated, using R squared
  
-      as weight for the trend estimate.</​P>​ +      as weight for the trend estimate. The
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-      <​P>​The resulting independent projections are then corrected +
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-      by the relative effect of implementing already decided upon changes in agricultural policies in the base +
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-      year - so-called "​policy shifts"​. These effects are derived from a simulation with the full modelling system. The+
  
       resulting estimates along with the estimation error of the trend deliver a priori distributions       resulting estimates along with the estimation error of the trend deliver a priori distributions
  
       for all items in the estimation framework.</​P>​       for all items in the estimation framework.</​P>​
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-     <​H3>​Quality control</​H3>​+     <​H3>​Quality control ​(validation)</H3>
  
-     <​P>​Due to the NUTS 2 resolution ​with its 250 regions, ​and the high number ​of activities, outputs ​and inputs ​covered by the CAPRI modelling+     <​P>​The reference run can easily comprise several million data points, due to the detailed geographical ​resolution and to the rich set of agricultural ​activities and food commodities ​covered by the CAPRI modelling 
 +system. In order to ease the validation, results of the reference run are accessible via the Graphical User Interface, where several reporting tables aggregate over activities, commodities and countries.
  
-     system, the references comprises several Mio numbersTherefore, aggregates over activities (single cereals aggregated to all cereals)+     With the help of the GUIanalysts can compare systematically ​the subsequent steps of the baseline procedure.</P>
  
-     and products, and over countries to EU15, EU10 etc. are defined and reported in tables accessible via the Graphical user interface. 
  
-     Those tables compare systematically the different inputs (trend estimate, policy shift, estimation results from the different steps).</​P>​ 
  
 +     <​P>​Nevertheless,​ checking results for plausibility remains a challenge. An interesting comparison of the methdology between the reference
  
 +     run exercise for the OECD/FAO AGLINK-COSIMO model with an estimated input of 20 person months and the CAPRI reference run
  
-     <P>Nevertheless,​ checking results for plausibility remains a challengeAn interested comparison of the methdology between the reference+     with an estimated inputs of about 2 person months is reported by <A HREF=http://​purl.umn.edu/​44120>​Adenauer 2008</A>.</P>
  
-     run exercise for the OECD/FAO AGLINK-COSIMA model with an estimated input of 20 person months and the CAPRI reference run 
  
-     with an estimated inputs of about 2 person months ​is reported by <HREF=http://purl.umn.edu/44120>Adenauer 2008</A>.</P>+ <​H3>​Calibration to the EC mid-term market outlook</​H3>​ 
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 + The CAPRI baseline ​is regularly calibrated to the mid-term commodity outlook of the European Commission. More on the technical and institutional aspects can be found in the following JRC reports: <​br>​ 
 + 
 + <​I>​<HREF="https://ec.europa.eu/jrc/​en/​publication/​calibrating-capri-and-esim-models"​ class="​intext"​>Calibrating CAPRI to the mid-term commodity outlook of the EC</a></I
  
 +  <br>
 +  ​
 + <​I><​a HREF="​http://​ipts.jrc.ec.europa.eu/​publications/​pub.cfm?​id=4879"​ class="​intext">​Background information on the baseline construction process</​a></​I> ​
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