Adding exogenous forecasts to scenarios in IFs: Difference between revisions
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The table does not include the variable GDP at MER. Note, that the model uses exogenous forecasts of GDP per capita at PPP and exogenous forecasts of population and a conversion ratio (PPPCONV) to compute a forecast of GDP at MER which would be similar to an exogenous forecast. | The table does not include the variable GDP at MER. Note, that the model uses exogenous forecasts of GDP per capita at PPP and exogenous forecasts of population and a conversion ratio (PPPCONV) to compute a forecast of GDP at MER which would be similar to an exogenous forecast. | ||
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Migration rate (net) as a percent of the population | Migration rate (net) as a percent of the population | ||
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=Running exogenous forecasts in IFs: Step 1(Changing access files) = | =Running exogenous forecasts in IFs: Step 1(Changing access files) = |
Revision as of 16:37, 18 April 2018
Introduction
In addition to constructing scenarios, users can also override model forecasts in a scenario with exogenous forecasts for specific variables. For overriding model forecasts, the user would have to make changes to specific access files and make use of two controllable parameters (All these changes are described in detail below). At this time, the main use of exogenous series in the model is for the work related to the Shared Socioeconomic Pathways (SSPs).
IFs can read in exogenous series for a select number of variables that are normally forecast endogenously in the model. The table below describes all of the variables that can be overridden with exogenous values. When activated, the exogenous forecasts will override calculations within the model. Note that this includes, overriding any values in the first year of the model run and if the exogenous series does not have data for a particular country for a particular year, the endogenously calculated variable is used.
The table does not include the variable GDP at MER. Note, that the model uses exogenous forecasts of GDP per capita at PPP and exogenous forecasts of population and a conversion ratio (PPPCONV) to compute a forecast of GDP at MER which would be similar to an exogenous forecast.
Variables that can be overridden with exogenous forecasts |
Description |
POP |
Population |
BIRTHS |
Total number of births |
DEATHS |
Total number of deaths |
TFR |
Total fertility rate |
POPURBAN |
Urban Population |
EDYRSAG15 |
Education years obtained by population older than 15 years (Male, Female and Total) |
EDYRSAG25 |
Education years obtained by population older than 25 years (Male, Female and Total) |
GDPPCP |
GDP per capita at PPP |
Migrater |
Migration rate (net) as a percent of the population |
Running exogenous forecasts in IFs: Step 1(Changing access files)
Running exogenous forecasts in IFs is a twostep process. First, the user must find the table IFsexogenousVars in the IFs.mdb file in the IFs\Data folder. It will look like the following, In this table,
• ExogenousModelId indicates whether the variable named in VarName is to be overwritten, i.e. if the exogenous series is to be activated. Any value of 1 or above means override. A user can assign a number that corresponds to a particular scenario, so that multiple scenarios can be run together. For example, if the user wants to run one scenario where TFR and POPURBAN are overridden and wants to run another scenario where only GDP is overridden, then the ExogenousModelId will be “1” for TFR and POPURBAN and “2” for GDPPCP. All other variables that a user does not want overridden should be set to 0. • VarName is the variable to be overridden by an exogenous forecast • Dim2 and Dim3 indicate the values for the second and third dimensions of the variable, if applicable. • TableName is the name of the table in IFsHistSeries where the exogenous forecast is stored • Adjustment can have the following values: 1) %ToAbs, this one means the user wants to convert a given table that is in % to absolute values, 2) AbsTo%, means the user wants to convert a given table that is in absolute values into %, or 3) Empty • AdjustmentTable specifies what variable or table to compute the % or absolute values respectively, so it could be for example: 1) SeriesPopulation, if the variable to adjust is in % of population and we want to go to Absolute numbers, or 2) SeriesGDP, if the variable is in Billion $ and we want to convert it to % of GDP.