The goal of the {osem} Package is to implement and operationalise the Open Source Empirical Macro (OSEM) Model, developed by Moritz Schwarz, Jonas Kurle, Felix Pretis, and Andrew Martinez. This is an adaptation of the Norwegian Aggregate Model, developed by Gunnar Bardsen and Ragnar Nymoen.
Installation
You can install the development version of osem from GitHub with:
# install.packages("devtools")
devtools::install_github("moritzpschwarz/osem")Basic Workflow
This is an example which shows you how to run the model:
First we load the package:
Specify the model
The we calibrate the model specification and save this in a tibble. Here the column names and the structure of the specification table must follow the basic structure below.
spec <- dplyr::tibble(
type = c(
"n",
"n",
"n",
"n",
"d",
"n",
"n",
"n",
"n"
),
dependent = c(
"Import",
"FinConsExpHH",
"GCapitalForm",
"Emissions",
"GDP",
"GValueAddGov", # as in NAM, technical relationship
"GValueAddManuf", # more complicated in NAM, see 2.3.3 and 6.3.1
"GValueAddConstr" ,
"GValueAddWholesaletrade"
),
independent = c(
"FinConsExpHH + GCapitalForm",
"",
"FinConsExpGov + FinConsExpHH",
"GDP + Export + GValueAddIndus",
"GValueAddGov + GValueAddAgri + GValueAddIndus + GValueAddConstr + GValueAddWholesaletrade + GValueAddInfocom + GValueAddFinance + GValueAddRealest + GValueAddResearch + GValueAddArts",
"FinConsExpGov", # as in NAM, technical relationship
"Export + LabCostManuf", # NAM uses 'export market indicator' not exports - unclear what this is, NAM uses unit labour cost in NOR manufacturing relative to the foreign price level - here is just total labour cost
"LabCostConstr + BuildingPermits", # in NAM some form of YFP2J = 0.3JBOL + 0.2JF P N + 0.3JO + 0.3JOIL. Unclear what this is. Using Building Permits instead
"Export + LabCostService"
))To summarise this, we can print out the specification table:
| type | dependent | independent |
|---|---|---|
| n | Import | FinConsExpHH + GCapitalForm |
| n | FinConsExpHH | |
| n | GCapitalForm | FinConsExpGov + FinConsExpHH |
| n | Emissions | GDP + Export + GValueAddIndus |
| d | GDP | GValueAddGov + GValueAddAgri + GValueAddIndus + GValueAddConstr + GValueAddWholesaletrade + GValueAddInfocom + GValueAddFinance + GValueAddRealest + GValueAddResearch + GValueAddArts |
| n | GValueAddGov | FinConsExpGov |
| n | GValueAddManuf | Export + LabCostManuf |
| n | GValueAddConstr | LabCostConstr + BuildingPermits |
| n | GValueAddWholesaletrade | Export + LabCostService |
In order to run this model, we also need a dictionary that translates our model variables to EUROSTAT codes so that the download process can be automated. You can either pass a new dictionary to the model function, or you can use the built in dictionary osem::dict:
| model_varname | full_name | database | variable_code | dataset_id | var_col | freq | geo | unit | s_adj | nace_r2 | ipcc_sector | cpa2_1 | siec |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TOTS | Total Supply | NA | TOTS | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| GDP | Gross domestic product at market prices | eurostat | B1GQ | namq_10_gdp | na_item | q | AT | CLV05_MEUR | SCA | NA | NA | NA | NA |
| GValueAdd | Value added, gross | eurostat | B1G | namq_10_a10 | na_item | q | AT | CLV05_MEUR | SCA | TOTAL | NA | NA | NA |
| Export | Exports of goods and services | eurostat | P6 | namq_10_gdp | na_item | q | AT | CLV05_MEUR | SCA | NA | NA | NA | NA |
| Import | Imports of goods and services | eurostat | P7 | namq_10_gdp | na_item | q | AT | CLV05_MEUR | SCA | NA | NA | NA | NA |
| GCapitalForm | Gross capital formation | eurostat | P5G | namq_10_gdp | na_item | q | AT | CLV05_MEUR | SCA | NA | NA | NA | NA |
| FinConsExp | Final consumption expenditure | eurostat | P3 | namq_10_gdp | na_item | q | AT | CLV05_MEUR | SCA | NA | NA | NA | NA |
| FinConsExpGov | Final consumption expenditure of general government | eurostat | P3_S13 | namq_10_gdp | na_item | q | AT | CLV05_MEUR | SCA | NA | NA | NA | NA |
| FinConsExpHH | Household and NPISH final consumption expenditure | eurostat | P31_S14_S15 | namq_10_gdp | na_item | q | AT | CLV05_MEUR | SCA | NA | NA | NA | NA |
| StatDiscrep | Statistical discrepancy (expenditure approach) | eurostat | YA0 | namq_10_gdp | na_item | q | AT | CP_MEUR | SCA | NA | NA | NA | NA |
| Emissions | Greenhouse Gas Emissions (All NACE and HH) | eurostat | GHG | env_ac_aigg_q | airpol | q | AT | THS_T | SCA | TOTAL_HH | NA | NA | NA |
| LabCostManuf | Manufacturing Labour cost index - Total Labour Cost | eurostat | LM-LCI-TOT | ei_lmlc_q | indic | q | AT | I20 | SCA | C | NA | NA | NA |
| DomDemand | Domestic Demand | NA | DomDemand | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| GValueAddManuf | Value added, gross Manufacturing | eurostat | B1G | namq_10_a10 | na_item | q | AT | CLV05_MEUR | SCA | C | NA | NA | NA |
| GValueAddGov | Value added, gross Government | eurostat | B1G | namq_10_a10 | na_item | q | AT | CLV05_MEUR | SCA | O-Q | NA | NA | NA |
| LabCostConstr | Construction Labour cost index - Total Labour Cost | eurostat | LM-LCI-TOT | ei_lmlc_q | indic | q | AT | I20 | SCA | F | NA | NA | NA |
| BuildingPermits | Building permits - m^2 useful floor area | eurostat | BPRM_SQM | sts_cobp_q | indic_bt | q | AT | I15 | SCA | F_CC1 | NA | CPA_F41001_41002 | NA |
| LabCostService | Service Labour cost index - Total Labour Cost | eurostat | LM-LCI-TOT | ei_lmlc_q | indic | q | AT | I20 | SCA | G-N | NA | NA | NA |
| GValueAddConstr | Value added, gross Construction | eurostat | B1G | namq_10_a10 | na_item | q | AT | CLV05_MEUR | SCA | F | NA | NA | NA |
| GValueAddAgri | Value added, gross Agriculture | eurostat | B1G | namq_10_a10 | na_item | q | AT | CLV05_MEUR | SCA | A | NA | NA | NA |
| GValueAddIndus | Value added, gross Industry | eurostat | B1G | namq_10_a10 | na_item | q | AT | CLV05_MEUR | SCA | B-E | NA | NA | NA |
| GValueAddInfocom | Value added, gross Information and Communication | eurostat | B1G | namq_10_a10 | na_item | q | AT | CLV05_MEUR | SCA | J | NA | NA | NA |
| GValueAddFinance | Value added, gross Financial Services | eurostat | B1G | namq_10_a10 | na_item | q | AT | CLV05_MEUR | SCA | K | NA | NA | NA |
| GValueAddRealest | Value added, gross Real Estate | eurostat | B1G | namq_10_a10 | na_item | q | AT | CLV05_MEUR | SCA | L | NA | NA | NA |
| GValueAddResearch | Value added, gross Scientific and Professional Services | eurostat | B1G | namq_10_a10 | na_item | q | AT | CLV05_MEUR | SCA | M_N | NA | NA | NA |
| GValueAddArts | Value added, gross Arts and Entertainment | eurostat | B1G | namq_10_a10 | na_item | q | AT | CLV05_MEUR | SCA | R-U | NA | NA | NA |
| GValueAddWholesaletrade | Value added, gross Wholesale and retail trade and Tourism | eurostat | B1G | namq_10_a10 | na_item | q | AT | CLV05_MEUR | SCA | G-I | NA | NA | NA |
| HICP | Harmonised Index of Consumer Prices, all items, index 100 = 2015 | eurostat | CP00 | prc_hicp_midx | coicop | m | AT | I15 | NA | NA | NA | NA | NA |
| HICP_Energy | Harmonised Index of Consumer Prices, Energy, index 100 = 2015 | eurostat | NRG | prc_hicp_midx | coicop | m | AT | I15 | NA | NA | NA | NA | NA |
| HICP_Electricity | Harmonised Index of Consumer Prices, Electricity, index 100 = 2015 | eurostat | CP0451 | prc_hicp_midx | coicop | m | AT | I15 | NA | NA | NA | NA | NA |
| HICP_Gas | Harmonised Index of Consumer Prices, Gas, index 100 = 2015 | eurostat | CP0452 | prc_hicp_midx | coicop | m | AT | I15 | NA | NA | NA | NA | NA |
| HICP_Liquid_Fuels | Harmonised Index of Consumer Prices, Liquid Fuels, index 100 = 2015 | eurostat | CP0453 | prc_hicp_midx | coicop | m | AT | I15 | NA | NA | NA | NA | NA |
| HICP_Solid_Fuels | Harmonised Index of Consumer Prices, Solid Fuels, index 100 = 2015 | eurostat | CP0454 | prc_hicp_midx | coicop | m | AT | I15 | NA | NA | NA | NA | NA |
| HICP_Heat | Harmonised Index of Consumer Prices, Heat Energy, index 100 = 2015 | eurostat | CP0455 | prc_hicp_midx | coicop | m | AT | I15 | NA | NA | NA | NA | NA |
| HDD | Heating Degree Days | eurostat | HDD | nrg_chdd_m | indic_nrg | m | AT | NR | NA | NA | NA | NA | NA |
| CDD | Cooling Degree Days | eurostat | CDD | nrg_chdd_m | indic_nrg | m | AT | NR | NA | NA | NA | NA | NA |
| EmiCH4Livestock | Methane Emissions from Livestock | edgar | NA | https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/EDGAR/datasets/v80_FT2022_GHG/EDGAR_CH4_m_1970_2022.zip | NA | m | AT | NA | NA | NA | 3.A | NA | NA |
| EmiCO2Industry | Carbon Emissions from Industrial Processes and Product Use | edgar | NA | https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/EDGAR/datasets/v80_FT2022_GHG/IEA_EDGAR_CO2_m_1970_2022.zip | NA | m | AT | NA | NA | NA | 2 | NA | NA |
| EmiCO2Combustion | Carbon Emissions from Fuel Combustion Activities | edgar | NA | https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/EDGAR/datasets/v80_FT2022_GHG/IEA_EDGAR_CO2_m_1970_2022.zip | NA | m | AT | NA | NA | NA | 1.A | NA | NA |
| EmiN2OTotal | Nitrous Oxide Emissions Total | edgar | NA | https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/EDGAR/datasets/v80_FT2022_GHG/EDGAR_N2O_m_1970_2022.zip | NA | m | AT | NA | NA | NA | TOTAL | NA | NA |
Running the model
Now we are ready to run the model with the run_model() function:
model_result <- run_model(
specification = spec,
save_to_disk = "inst/extdata/InputData.xlsx",
primary_source = "download",
trend = TRUE,
saturation.tpval = 0.01
)
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#> Table namq_10_gdp cached at C:\Users\morit\AppData\Local\Temp\Rtmpo1GImx/eurostat/e76122ae8ad09435f09b9fba897779aa.rds
#> indexed 0B in 0s, 0B/sindexed 2.15GB in 0s, 2.15GB/s
#> Table env_ac_aigg_q cached at C:\Users\morit\AppData\Local\Temp\Rtmpo1GImx/eurostat/f9f0df28d832dc70fee1519c934adc15.rds
#> indexed 0B in 0s, 0B/sindexed 6.42MB in 0s, 16.27MB/s indexed 6.55MB in 1s, 8.19MB/sindexed 6.68MB in 1s, 8.32MB/sindexed 6.82MB in 1s, 8.42MB/sindexed 6.95MB in 1s, 8.54MB/sindexed 7.08MB in 1s, 8.67MB/sindexed 7.21MB in 1s, 8.79MB/sindexed 7.34MB in 1s, 8.92MB/sindexed 7.47MB in 1s, 9.04MB/sindexed 7.60MB in 1s, 9.17MB/sindexed 7.73MB in 1s, 9.29MB/sindexed 7.86MB in 1s, 9.41MB/sindexed 8.00MB in 1s, 9.53MB/sindexed 8.13MB in 1s, 9.65MB/sindexed 8.26MB in 1s, 9.77MB/sindexed 8.39MB in 1s, 9.89MB/sindexed 8.52MB in 1s, 10.01MB/sindexed 8.65MB in 1s, 10.13MB/sindexed 8.78MB in 1s, 10.24MB/sindexed 8.91MB in 1s, 10.36MB/sindexed 9.04MB in 1s, 10.47MB/sindexed 9.17MB in 1s, 10.59MB/sindexed 9.31MB in 1s, 10.69MB/sindexed 9.44MB in 1s, 10.79MB/sindexed 9.57MB in 1s, 10.90MB/sindexed 9.70MB in 1s, 11.01MB/sindexed 9.83MB in 1s, 11.12MB/sindexed 9.96MB in 1s, 11.22MB/sindexed 10.09MB in 1s, 11.31MB/sindexed 10.22MB in 1s, 11.42MB/sindexed 10.35MB in 1s, 11.52MB/sindexed 10.49MB in 1s, 11.60MB/sindexed 10.62MB in 1s, 11.68MB/sindexed 10.75MB in 1s, 11.77MB/sindexed 10.88MB in 1s, 11.85MB/sindexed 11.01MB in 1s, 11.81MB/sindexed 11.14MB in 1s, 11.89MB/sindexed 11.27MB in 1s, 11.98MB/sindexed 11.40MB in 1s, 12.07MB/sindexed 11.53MB in 1s, 12.16MB/sindexed 11.67MB in 1s, 12.26MB/sindexed 11.80MB in 1s, 12.17MB/sindexed 11.93MB in 1s, 12.26MB/sindexed 12.06MB in 1s, 12.35MB/sindexed 12.19MB in 1s, 12.44MB/sindexed 12.32MB in 1s, 12.52MB/sindexed 12.45MB in 1s, 12.58MB/sindexed 12.58MB in 1s, 12.67MB/sindexed 12.71MB in 1s, 12.76MB/s indexed 12.84MB in 2s, 7.82MB/sindexed 12.98MB in 2s, 7.87MB/sindexed 13.11MB in 2s, 7.93MB/sindexed 13.24MB in 2s, 7.78MB/sindexed 13.37MB in 2s, 7.80MB/sindexed 13.50MB in 2s, 7.84MB/sindexed 13.63MB in 2s, 7.88MB/sindexed 13.76MB in 2s, 7.87MB/sindexed 13.89MB in 2s, 7.93MB/sindexed 14.02MB in 2s, 7.98MB/sindexed 14.16MB in 2s, 8.03MB/sindexed 14.29MB in 2s, 8.08MB/sindexed 14.42MB in 2s, 8.14MB/sindexed 14.55MB in 2s, 8.20MB/sindexed 14.68MB in 2s, 8.25MB/sindexed 14.81MB in 2s, 8.31MB/sindexed 14.94MB in 2s, 8.37MB/sindexed 14.97MB in 2s, 8.35MB/s indexed 2.15GB in 2s, 2.15GB/s
#> Table ei_lmlc_q cached at C:\Users\morit\AppData\Local\Temp\Rtmpo1GImx/eurostat/258fb00990b016df7382eafb05dc1ba3.rds
#> indexed 0B in 0s, 0B/sindexed 25.03MB in 0s, 124.50MB/sindexed 25.17MB in 0s, 124.51MB/sindexed 25.30MB in 0s, 124.50MB/sindexed 25.43MB in 0s, 124.38MB/sindexed 25.56MB in 0s, 124.23MB/sindexed 25.69MB in 0s, 124.33MB/sindexed 25.82MB in 0s, 124.48MB/sindexed 25.95MB in 0s, 124.65MB/sindexed 26.08MB in 0s, 124.81MB/sindexed 26.21MB in 0s, 124.95MB/sindexed 26.35MB in 0s, 125.13MB/sindexed 26.48MB in 0s, 125.31MB/sindexed 26.61MB in 0s, 125.34MB/sindexed 26.74MB in 0s, 125.47MB/sindexed 26.87MB in 0s, 125.63MB/sindexed 27.00MB in 0s, 125.79MB/sindexed 27.13MB in 0s, 125.95MB/sindexed 27.26MB in 0s, 126.09MB/sindexed 27.39MB in 0s, 126.25MB/sindexed 27.52MB in 0s, 126.40MB/sindexed 27.66MB in 0s, 126.46MB/sindexed 27.79MB in 0s, 126.46MB/sindexed 27.92MB in 0s, 126.49MB/sindexed 28.05MB in 0s, 126.56MB/sindexed 28.18MB in 0s, 126.61MB/sindexed 28.31MB in 0s, 126.64MB/sindexed 28.44MB in 0s, 126.65MB/sindexed 28.57MB in 0s, 126.74MB/sindexed 28.70MB in 0s, 126.71MB/sindexed 28.84MB in 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#> Table namq_10_a10 cached at C:\Users\morit\AppData\Local\Temp\Rtmpo1GImx/eurostat/37ff1e8a43300ebf543f2324f2b54596.rds
#> indexed 0B in 0s, 0B/sindexed 2.15GB in 0s, 2.15GB/s
#> Table sts_cobp_q cached at C:\Users\morit\AppData\Local\Temp\Rtmpo1GImx/eurostat/9e930697659f7bcb8ef80083c6d3b693.rds
#> Warning in load_or_download_variables(specification = module_order, dictionary
#> = dictionary, : Unbalanced panel, will lose more than 20\% of data when making
#> balanced
#>
#> --- Estimation begins ---
#> Estimating GValueAddGov = FinConsExpGov
#> Estimating GValueAddManuf = Export + LabCostManuf
#> Estimating GValueAddConstr = LabCostConstr + BuildingPermits
#> Estimating GValueAddWholesaletrade = Export + LabCostService
#> Estimating FinConsExpHH =
#> Constructing GDP = GValueAddGov + GValueAddAgri + GValueAddIndus + GValueAddConstr + GValueAddWholesaletrade + GValueAddInfocom + GValueAddFinance + GValueAddRealest + GValueAddResearch + GValueAddArts
#> Estimating GCapitalForm = FinConsExpGov + FinConsExpHH
#> Estimating Emissions = GDP + Export + GValueAddIndus
#> Estimating Import = FinConsExpHH + GCapitalFormThe first time that we run this, all data will be downloaded and saved in the folder data/use/InputData.xlsx.
The next time that we run the same model, we can save some time and just load the data from our earlier run:
model_result <- run_model(
specification = spec,
primary_source = "local",
inputdata_directory = "inst/extdata",
trend = TRUE,
saturation.tpval = 0.01
)Forecasting the model
Now that we have run the model, we can forecast the model (here using an AR process for the exogenous values and for 10 time periods):
model_forecast <- forecast_model(model_result, n.ahead = 10, exog_fill_method = "AR", plot.forecast = FALSE)
#> No exogenous values provided. Model will forecast the exogenous values with an AR4 process (incl. Q dummies, IIS and SIS w 't.pval = 0.001').
#> Alternative is exog_fill_method = 'last'.Once we are done, we can plot the forecast:
plot(model_forecast, order.as.run = TRUE)