options(warn=-1) knitr::opts_chunk$set(echo = FALSE) library(knitr) library(pander) library(data.table) # File in vignettes/Documentation # examples <- readRDS('examples.rds') examples <- readRDS('C:/Users/caetano/Documents/Github/faoswsStock/vignettes/stockExamples.RDS') coefCerealsPulses <- fread('C:/Users/caetano/Documents/Github/faoswsStock/vignettes/coefficients_cereals_pulses.csv') coefSugar <- fread('C:/Users/caetano/Documents/Github/faoswsStock/vignettes/coef_sugar.csv')
The module uses two variables related to stocks: Stock Variation and Opening Stocks. The first one is stored in two different datasets: Updated SUA 2013 data and SUA Validated 2015.
The opening stocks variable is found in the dataset Aproduction.
The dataset Updated SUA 2013 is under the domain FAOSTAT 1, element code 71. This dataset contains validated data up to 2013 at FAO Code List (FCL). The data come from the old methodology and the values for Stock must be multiplied by -1 to be compliant with the new system.
geographicAreaFS
: country code FSmeasuredElementFS
: element code (Variation Intial Exstenc)measuredItemFS
: item codetimePointYears
: yearValue
: valueflagFaostat
: flag. pander(examples$updatedSUA2013, row.names = FALSE, caption = 'Subset of Updated SUA 2013 dataset', digits = 2, split.table = 100)
The dataset SUA Validated 2015 is in the domain SUA/FBS Domain. It contains the items already converted to cpc,countries to M49 country code and flags in the new system.
pander(examples$stockDataFrom2000, row.names = FALSE, caption = 'Subset of SUA Validated 2015 dataset', digits = 2, split.table = 100)
The data do not need to be multiplied by -1.
This dataset contains the information for Opening Stocks under the element code 5113.
pander(examples$openingStockData, row.names = FALSE, caption = 'Subset of Aproduction dataset (Opening Stocks)', digits = 2, split.table = 100)
The production data are found also in the dataset Aproduction in the domain Agriculture, element code 5510.
pander(examples$productionData, row.names = FALSE, caption = 'Subset of Agriculture dataset (Production)', digits = 2, split.table = 100)
The Total Trade Data are found in the domain Trade, elements 5610 and 5910.
pander(examples$totalTradeData, row.names = FALSE, caption = 'Subset of Total Trade dataset', digits = 2, split.table = 100)
It is a table in Datatables, domain Stock. This table comes from the World Bank. The four income groups we use are:
pander(examples$countryGroup, row.names = FALSE, caption = 'Subset of Country Group table', digits = 2, split.table = 100)
These coefficients come from a linear regression fitted using external data.
A statistical model was fitted for the two groups of commodities using data from AMIS: cereals and pulses.
More information about this model is found in the document Description Amis Analysis.
pander(coefCerealsPulses, caption = 'Coefficients of the model for cereals and pulses', digits = 2, split.table = 100)
Another statistical model was fitted for sugar using data from F.O. Lichts.
More information about this model is found in the document Description FO Lichts.
pander(coefSugar, caption = 'Coefficients of the model for sugar', digits = 2, split.table = 100)
The imputation process takes place after all above tables are pulled from the SWS. The process is straightforward and is split in five steps:
Estimate $ClosingStocks_{t}$ based on total availability for each country, commodity and year. In this step we apply the coefficients fitted in the models described above;
Compute $OpeningStocks_{t}$ as: $OpeningStocks_{t}$ = $ClosingStocks_{t-1}$;
Compute $Delta Stocks_{t}$ as: $Delta Stocks_{t}$ = $ClosingStocks_{t}$ - $OpeningStocks_{t}$;
Integrate official data for $Delta Stocks_{t}$ with the estimates from the step 3;
Recalculate the $ClosingStocks_{t}$, $OpeningStocks_{t}$ and $Delta Stocks_{t}$ estimates, remaining only the official values.
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