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--- calibrating_the_global_trade_model [2020/03/01 07:45] – created matsz
+++ calibrating_the_global_trade_model [2020/03/01 07:46] – [tage IV: Initialization and test run] matsz
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 where SSQ is an artificial variable to be minimized, indices RMS, XXX, BAS and i indicate, respectively, regions, commodities, base year and activities (e.g., production, processing, imports etc.), and p_weight is a parameter of weights between 1 and 100 assigned to regions and activities. These weights are necessary to achieve plausible calibrated values and their specification is the outcome of a trial and error process, inspecting results from data calibration and retrying. They depend on the results of global database and trends generation. On the right hand-side of the equation v stands for a variable to be estimated and DATA – for base year data already adjusted at the data preparation and balancing stage. Hence with this equation squared sum over regions and commodities of differences between estimated and observed values (and or quantities), these differences being scaled by the observed data times the weight parameters, is minimized. Respectively, calibrated base year data fits the system of the market equations, given certain parameter values, and resembles the observed data as closely as possible. The activities implied under the I index include quantities of production, human consumption, feed, processing, processed to biofuels, import and export, producer, consumer and market prices, difference between market prices and import prices to reduce differences between physical and Armington aggregation, consolidated gap between producer and market prices, processing margin, trade flows and transport costs.
-The process of model solving is navigated with C:\...\CAPRI\gams\arm\data_fit.gms file. Its main function is to assure model solving by keeping the market balances closed and price system consistent. Because of the very large number of equations with the exact similar number of variables (36 thsds) that makes the system of equations square, as well as non-linear formulation of some of the equations, it is very likely that infeasibilities will occur during the model solving. To ensure the feasibility as far as possible, code elements such as widening of variable bounds, once they become binding, reducing non-smoothness of the functional forms and introduction of slack variables are introduced. More detailed information on this process can be found in a technical document by Wolfgang Britz and Heinz-Peter Witzke I//nfeasibilities in the market model of CAPRI – how they are dealt with// at [[https://www.capri-model.org/docs/infes.pdf]].
+The process of model solving is navigated with C:\...\CAPRI\gams\arm\data_fit.gms file. Its main function is to assure model solving by keeping the market balances closed and price system consistent. Because of the very large number of equations with the exact similar number of variables (36 thsds) that makes the system of equations square, as well as non-linear formulation of some of the equations, it is very likely that infeasibilities will occur during the model solving. To ensure the feasibility as far as possible, code elements such as widening of variable bounds, once they become binding, reducing non-smoothness of the functional forms and introduction of slack variables are introduced. More detailed information on this process can be found in a technical document by Wolfgang Britz and Heinz-Peter Witzke //Infeasibilities in the market model of CAPRI – how they are dealt with// at [[https://www.capri-model.org/docs/infes.pdf]].
 After solving the MODEL m_calMarketBas, the calibrated data are stored, new producer prices for agricultural outputs are set, sugar beet prices as a function of – sugar market price – sugar export price (pre-reform) or ethanol market price (post-reform) – processing yield (specific to CUR to calibrate to any set of projected beet prices) – levying model for A- and B- sugar (pre-reform) are calculated, share and shift parameters of CES-functions used in the Armington approach to determine import shares as a function of import prices are defined (file C:\...\CAPRI\gams\arm\cal_armington.gms). Furthermore, energy conversion factors for animal products are defined with MODEL m_fitFeedConv (in file C:\...\CAPRI\ gams\arm\feed_conv_decl.gms).
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 At the final stage, some of the starting values and bounds for the market model are set, and agricultural policy data are loaded, adjusted and extended to the simulation year. The policy data include single area payment scheme, set-aside regulations, differentiation between old and new MSs payments, special national envelopes, Nordic schemes, changes in administrative prices, rural development policy and other major CAP post-2014 instruments. Policy files used for the baseline are located in C:\...\CAPRI\ gams\scen\base_scenarios folder. Their loading into the baseline process is controlled by CAP_2014_2020.gms file. With the data mentioned, the outcome of calibration of the CAPRI market module can be tested. In particular, the market model is solved at "trend values" and, thus, the calibration outcome is checked for fitting to the square system of market model equations. This is controlled by C:\...\CAPRI\.gams\arm\prep_market.gms file.
+====Technical remarks====
+Note that the task "Baseline calibration of market model" deletes the sim_ini.gdx file, but does not create a new one at the end of the calibration process. The new sim_ini.gdx file will be only created at the first simulation run after the calibration. That is also the reason why a specific GUI option 'Kill simini file' is provided for the simulation tasks. The simini file can be deleted upon request at the beginning of any scenario run, forcing CAPRI to re-create it before the scenario shock is introduced.
+Technically, the calibration of the biofuel demand system and the Armington bilateral trade system is not directly linked to the BASELINE mode, but also executed every time when the simini file is missing (by create_sim_ini_gdx module).