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 (here the first few rows):
| model_varname | full_name | variable_code | database | dataset_id | var_col | freq | geo | unit | s_adj | nace_r2 | cpa2_1 | sector | direct | age | partner | finpos | p_adj | meat | citizen | wstatus | tra_oper | ipcc_sector | GEO | Seasonal adjustment | North American Industry Classification System (NAICS) | North American Product Classification System (NAPCS) | Prices | Type of fuel | Products and product groups | ref_area | commodity | unit_measure |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Supply | Total Supply | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Demand | Total Demand | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| GDPOutput | GDP Output Approach | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| GDPExpenditure | GDP Expenditure Approach | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| CO2Industry | Co2 Emissions Industry in Tonnes | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| TOTS | Total Supply | TOTS | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 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,
plot = FALSE
)
#> 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\RtmpGOqncW/eurostat/b9ab1dabbb1a27521cec3bdf0b9c2c23.rds
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64.91MB/sindexed 61.47MB in 1s, 64.94MB/sindexed 61.60MB in 1s, 64.98MB/sindexed 61.73MB in 1s, 65.01MB/sindexed 61.87MB in 1s, 65.05MB/sindexed 62.00MB in 1s, 65.10MB/sindexed 62.13MB in 1s, 65.14MB/sindexed 62.26MB in 1s, 65.18MB/sindexed 62.39MB in 1s, 65.22MB/sindexed 62.52MB in 1s, 65.26MB/sindexed 62.65MB in 1s, 65.30MB/sindexed 62.78MB in 1s, 65.34MB/sindexed 62.91MB in 1s, 65.38MB/sindexed 63.05MB in 1s, 65.41MB/sindexed 63.18MB in 1s, 65.45MB/sindexed 63.31MB in 1s, 65.49MB/sindexed 63.44MB in 1s, 65.52MB/sindexed 63.57MB in 1s, 65.56MB/sindexed 63.70MB in 1s, 65.61MB/sindexed 63.83MB in 1s, 65.63MB/sindexed 63.96MB in 1s, 65.67MB/sindexed 64.09MB in 1s, 65.71MB/sindexed 64.22MB in 1s, 65.75MB/sindexed 64.36MB in 1s, 65.80MB/sindexed 64.49MB in 1s, 65.81MB/sindexed 64.62MB in 1s, 65.80MB/sindexed 64.75MB in 1s, 65.82MB/sindexed 64.88MB in 1s, 65.84MB/sindexed 65.01MB in 1s, 65.87MB/sindexed 65.14MB in 1s, 65.91MB/sindexed 65.27MB in 1s, 65.94MB/sindexed 65.40MB in 1s, 65.99MB/sindexed 65.54MB in 1s, 66.03MB/sindexed 65.67MB in 1s, 66.07MB/sindexed 65.80MB in 1s, 66.10MB/sindexed 65.93MB in 1s, 66.15MB/sindexed 66.06MB in 1s, 66.18MB/sindexed 66.19MB in 1s, 66.23MB/sindexed 66.32MB in 1s, 66.26MB/sindexed 66.45MB in 1s, 66.30MB/sindexed 66.58MB in 1s, 66.33MB/sindexed 66.72MB in 1s, 66.37MB/sindexed 66.85MB in 1s, 66.41MB/sindexed 66.98MB in 1s, 66.44MB/sindexed 67.11MB in 1s, 66.48MB/sindexed 67.24MB in 1s, 66.52MB/sindexed 67.37MB in 1s, 66.54MB/sindexed 67.50MB in 1s, 66.56MB/sindexed 67.63MB in 1s, 66.58MB/sindexed 67.76MB in 1s, 66.61MB/sindexed 67.89MB in 1s, 66.65MB/sindexed 68.03MB in 1s, 66.67MB/sindexed 68.16MB in 1s, 66.69MB/sindexed 68.29MB in 1s, 66.73MB/sindexed 68.42MB in 1s, 66.74MB/sindexed 68.55MB in 1s, 66.79MB/sindexed 68.68MB in 1s, 66.82MB/sindexed 68.81MB in 1s, 66.86MB/sindexed 68.94MB in 1s, 66.90MB/sindexed 69.07MB in 1s, 66.90MB/sindexed 69.21MB in 1s, 66.94MB/sindexed 69.34MB in 1s, 66.98MB/sindexed 69.47MB in 1s, 66.97MB/sindexed 69.60MB in 1s, 67.02MB/sindexed 69.73MB in 1s, 67.05MB/sindexed 69.86MB in 1s, 66.68MB/sindexed 69.99MB in 1s, 66.68MB/sindexed 70.12MB in 1s, 66.65MB/sindexed 70.25MB in 1s, 66.62MB/sindexed 70.39MB in 1s, 66.64MB/sindexed 70.52MB in 1s, 66.65MB/sindexed 70.65MB in 1s, 66.64MB/sindexed 70.78MB in 1s, 66.63MB/sindexed 70.91MB in 1s, 66.53MB/sindexed 71.04MB in 1s, 66.47MB/sindexed 71.17MB in 1s, 66.46MB/sindexed 71.30MB in 1s, 66.42MB/sindexed 71.43MB in 1s, 66.44MB/sindexed 71.56MB in 1s, 66.45MB/sindexed 71.70MB in 1s, 66.48MB/sindexed 71.83MB in 1s, 66.48MB/sindexed 71.96MB in 1s, 66.50MB/sindexed 72.09MB in 1s, 66.45MB/sindexed 72.22MB in 1s, 66.46MB/sindexed 72.35MB in 1s, 66.46MB/sindexed 72.48MB in 1s, 66.48MB/sindexed 72.61MB in 1s, 66.48MB/sindexed 72.74MB in 1s, 66.48MB/sindexed 72.88MB in 1s, 66.45MB/sindexed 73.01MB in 1s, 66.43MB/sindexed 73.14MB in 1s, 66.42MB/sindexed 73.27MB in 1s, 66.39MB/sindexed 73.40MB in 1s, 66.36MB/sindexed 73.53MB in 1s, 66.34MB/sindexed 73.66MB in 1s, 66.30MB/sindexed 73.79MB in 1s, 66.27MB/sindexed 73.92MB in 1s, 66.26MB/sindexed 74.06MB in 1s, 66.23MB/sindexed 74.19MB in 1s, 66.22MB/sindexed 74.32MB in 1s, 66.22MB/sindexed 74.45MB in 1s, 66.23MB/sindexed 74.58MB in 1s, 66.24MB/sindexed 74.71MB in 1s, 66.24MB/sindexed 74.84MB in 1s, 66.22MB/sindexed 74.97MB in 1s, 66.15MB/sindexed 75.10MB in 1s, 66.12MB/sindexed 75.23MB in 1s, 66.04MB/sindexed 75.37MB in 1s, 66.00MB/sindexed 75.50MB in 1s, 65.98MB/sindexed 75.63MB in 1s, 65.93MB/sindexed 75.76MB in 1s, 65.91MB/sindexed 75.89MB in 1s, 65.87MB/sindexed 76.02MB in 1s, 65.82MB/sindexed 76.15MB in 1s, 65.74MB/sindexed 76.28MB in 1s, 65.67MB/sindexed 76.41MB in 1s, 65.62MB/sindexed 76.55MB in 1s, 65.58MB/sindexed 76.68MB in 1s, 65.57MB/sindexed 76.81MB in 1s, 65.57MB/sindexed 76.94MB in 1s, 65.57MB/sindexed 77.07MB in 1s, 65.57MB/sindexed 77.20MB in 1s, 65.58MB/sindexed 77.33MB in 1s, 65.59MB/sindexed 77.46MB in 1s, 65.62MB/sindexed 77.59MB in 1s, 65.66MB/sindexed 77.73MB in 1s, 65.68MB/sindexed 77.86MB in 1s, 65.70MB/sindexed 77.99MB in 1s, 65.73MB/sindexed 78.12MB in 1s, 65.76MB/sindexed 78.25MB in 1s, 65.80MB/sindexed 78.38MB in 1s, 65.81MB/sindexed 78.51MB in 1s, 65.85MB/sindexed 78.64MB in 1s, 65.87MB/sindexed 78.77MB in 1s, 65.88MB/sindexed 78.90MB in 1s, 65.91MB/sindexed 79.04MB in 1s, 65.93MB/sindexed 79.17MB in 1s, 65.97MB/sindexed 79.30MB in 1s, 66.00MB/sindexed 79.43MB in 1s, 66.03MB/sindexed 79.56MB in 1s, 66.06MB/sindexed 79.69MB in 1s, 66.09MB/sindexed 79.82MB in 1s, 66.12MB/sindexed 79.95MB in 1s, 66.15MB/sindexed 80.08MB in 1s, 66.18MB/sindexed 80.22MB in 1s, 66.22MB/sindexed 80.35MB in 1s, 66.24MB/sindexed 80.48MB in 1s, 66.25MB/sindexed 80.61MB in 1s, 66.27MB/sindexed 80.74MB in 1s, 66.30MB/sindexed 80.87MB in 1s, 66.33MB/sindexed 81.00MB in 1s, 66.35MB/sindexed 81.13MB in 1s, 66.37MB/sindexed 81.26MB in 1s, 66.40MB/sindexed 81.40MB in 1s, 66.43MB/sindexed 81.41MB in 1s, 66.40MB/s indexed 2.15GB in 1s, 2.15GB/s
#> Table namq_10_gdp cached at C:\Users\morit\AppData\Local\Temp\RtmpGOqncW/eurostat/7cfeb2317299a0462a20558b59fa7fd2.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\RtmpGOqncW/eurostat/786decf32a8dc0555b458405bc423a40.rds
#> indexed 0B in 0s, 0B/sindexed 10.09MB in 0s, 50.18MB/sindexed 10.22MB in 0s, 50.26MB/sindexed 10.35MB in 0s, 50.27MB/sindexed 10.49MB in 0s, 50.49MB/sindexed 10.62MB in 0s, 50.54MB/sindexed 10.75MB in 0s, 50.59MB/sindexed 10.88MB in 0s, 50.57MB/sindexed 11.01MB in 0s, 50.55MB/sindexed 11.14MB in 0s, 50.48MB/sindexed 11.27MB in 0s, 50.42MB/sindexed 11.40MB in 0s, 50.33MB/sindexed 11.53MB in 0s, 50.32MB/sindexed 11.67MB in 0s, 50.22MB/sindexed 11.80MB in 0s, 50.15MB/sindexed 11.93MB in 0s, 48.04MB/sindexed 12.06MB in 0s, 48.09MB/sindexed 12.19MB in 0s, 48.12MB/sindexed 12.32MB in 0s, 48.22MB/sindexed 12.45MB in 0s, 48.23MB/sindexed 12.58MB in 0s, 48.23MB/sindexed 12.71MB in 0s, 48.22MB/sindexed 12.84MB in 0s, 48.21MB/sindexed 12.98MB in 0s, 48.17MB/sindexed 13.11MB in 0s, 48.09MB/sindexed 13.24MB in 0s, 48.02MB/sindexed 13.37MB in 0s, 48.03MB/sindexed 13.50MB in 0s, 48.04MB/sindexed 13.63MB in 0s, 48.03MB/sindexed 13.76MB in 0s, 48.08MB/sindexed 13.89MB in 0s, 48.17MB/sindexed 14.02MB in 0s, 48.21MB/sindexed 14.16MB in 0s, 48.28MB/sindexed 14.29MB in 0s, 48.37MB/sindexed 14.42MB in 0s, 48.48MB/sindexed 14.55MB in 0s, 48.58MB/sindexed 14.68MB in 0s, 48.65MB/sindexed 14.81MB in 0s, 48.73MB/sindexed 14.94MB in 0s, 48.91MB/sindexed 15.07MB in 0s, 49.01MB/sindexed 15.19MB in 0s, 49.09MB/s indexed 2.15GB in 0s, 2.15GB/s
#> Table ei_lmlc_q cached at C:\Users\morit\AppData\Local\Temp\RtmpGOqncW/eurostat/5a2d893c55d2002394347b1740520c1f.rds
#> indexed 0B in 0s, 0B/sindexed 11.80MB in 0s, 58.26MB/sindexed 11.93MB in 0s, 58.21MB/sindexed 12.06MB in 0s, 58.05MB/sindexed 12.19MB in 0s, 58.02MB/sindexed 12.32MB in 0s, 58.02MB/sindexed 12.45MB in 0s, 57.97MB/sindexed 12.58MB in 0s, 57.96MB/sindexed 12.71MB in 0s, 57.97MB/sindexed 12.84MB in 0s, 57.91MB/sindexed 12.98MB in 0s, 58.00MB/sindexed 13.11MB in 0s, 58.01MB/sindexed 13.24MB in 0s, 57.97MB/sindexed 13.37MB in 0s, 57.89MB/sindexed 13.50MB in 0s, 55.30MB/sindexed 13.63MB in 0s, 55.18MB/sindexed 13.76MB in 0s, 55.18MB/sindexed 13.89MB in 0s, 55.13MB/sindexed 14.02MB in 0s, 55.02MB/sindexed 14.16MB in 0s, 54.90MB/sindexed 14.29MB in 0s, 54.88MB/sindexed 14.42MB in 0s, 54.87MB/sindexed 14.55MB in 0s, 54.70MB/sindexed 14.68MB in 0s, 54.17MB/sindexed 14.81MB in 0s, 53.40MB/sindexed 14.94MB in 0s, 52.82MB/sindexed 15.07MB in 0s, 52.32MB/sindexed 15.20MB in 0s, 51.90MB/sindexed 15.34MB in 0s, 51.71MB/sindexed 15.47MB in 0s, 51.76MB/sindexed 15.60MB in 0s, 51.78MB/sindexed 15.73MB in 0s, 51.73MB/sindexed 15.86MB in 0s, 51.74MB/sindexed 15.99MB in 0s, 51.86MB/sindexed 16.12MB in 0s, 51.89MB/sindexed 16.25MB in 0s, 52.01MB/sindexed 16.38MB in 0s, 52.12MB/sindexed 16.51MB in 0s, 52.27MB/sindexed 16.65MB in 0s, 52.32MB/sindexed 16.78MB in 0s, 52.34MB/sindexed 16.91MB in 0s, 52.44MB/sindexed 17.04MB in 0s, 52.57MB/sindexed 17.17MB in 0s, 52.69MB/sindexed 17.30MB in 0s, 52.76MB/sindexed 17.43MB in 0s, 52.88MB/sindexed 17.56MB in 0s, 53.01MB/sindexed 17.69MB in 0s, 53.10MB/sindexed 17.83MB in 0s, 53.19MB/sindexed 17.96MB in 0s, 53.19MB/sindexed 18.09MB in 0s, 53.30MB/sindexed 18.22MB in 0s, 53.42MB/sindexed 18.35MB in 0s, 53.53MB/sindexed 18.48MB in 0s, 53.62MB/sindexed 18.61MB in 0s, 53.73MB/sindexed 18.74MB in 0s, 53.77MB/sindexed 18.87MB in 0s, 53.75MB/sindexed 19.01MB in 0s, 53.81MB/sindexed 19.14MB in 0s, 53.85MB/sindexed 19.27MB in 0s, 53.90MB/sindexed 19.40MB in 0s, 53.96MB/sindexed 19.53MB in 0s, 54.00MB/sindexed 19.66MB in 0s, 54.06MB/sindexed 19.79MB in 0s, 54.05MB/sindexed 19.92MB in 0s, 54.02MB/sindexed 20.05MB in 0s, 54.03MB/sindexed 20.18MB in 0s, 54.00MB/sindexed 20.32MB in 0s, 53.98MB/sindexed 20.45MB in 0s, 53.97MB/sindexed 20.58MB in 0s, 53.98MB/sindexed 20.71MB in 0s, 53.99MB/sindexed 20.84MB in 0s, 53.95MB/sindexed 20.97MB in 0s, 53.89MB/sindexed 21.10MB in 0s, 53.87MB/sindexed 21.23MB in 0s, 53.81MB/sindexed 21.36MB in 0s, 53.68MB/sindexed 21.50MB in 0s, 53.71MB/sindexed 21.63MB in 0s, 53.68MB/sindexed 21.76MB in 0s, 53.68MB/sindexed 21.89MB in 0s, 53.67MB/sindexed 22.02MB in 0s, 53.72MB/sindexed 22.15MB in 0s, 53.79MB/sindexed 22.28MB in 0s, 53.72MB/sindexed 22.41MB in 0s, 53.83MB/sindexed 22.54MB in 0s, 53.89MB/sindexed 22.68MB in 0s, 53.98MB/sindexed 22.81MB in 0s, 54.02MB/sindexed 22.94MB in 0s, 54.14MB/sindexed 23.07MB in 0s, 54.22MB/sindexed 23.20MB in 0s, 54.31MB/sindexed 23.33MB in 0s, 54.41MB/sindexed 23.46MB in 0s, 54.49MB/sindexed 23.59MB in 0s, 54.57MB/sindexed 23.72MB in 0s, 54.67MB/sindexed 23.85MB in 0s, 54.75MB/sindexed 23.99MB in 0s, 54.83MB/sindexed 24.12MB in 0s, 54.91MB/sindexed 24.25MB in 0s, 55.01MB/sindexed 24.38MB in 0s, 55.09MB/sindexed 24.51MB in 0s, 55.10MB/sindexed 24.64MB in 0s, 55.18MB/sindexed 24.77MB in 0s, 55.26MB/sindexed 24.90MB in 0s, 55.33MB/sindexed 25.03MB in 0s, 55.41MB/sindexed 25.17MB in 0s, 55.53MB/sindexed 25.30MB in 0s, 55.63MB/sindexed 25.43MB in 0s, 53.97MB/sindexed 25.56MB in 0s, 53.99MB/sindexed 25.69MB in 0s, 54.03MB/sindexed 25.82MB in 0s, 54.11MB/sindexed 25.95MB in 0s, 54.20MB/sindexed 26.08MB in 0s, 54.26MB/sindexed 26.21MB in 0s, 54.31MB/sindexed 26.35MB in 0s, 54.37MB/sindexed 26.48MB in 0s, 54.39MB/sindexed 26.61MB in 0s, 54.43MB/sindexed 26.74MB in 0s, 54.47MB/sindexed 26.87MB in 0s, 54.49MB/sindexed 27.00MB in 0s, 54.52MB/sindexed 27.13MB in 0s, 54.59MB/sindexed 27.26MB in 0s, 54.63MB/sindexed 27.39MB in 1s, 54.68MB/sindexed 27.52MB in 1s, 54.71MB/sindexed 27.66MB in 1s, 54.76MB/sindexed 27.79MB in 1s, 54.80MB/sindexed 27.92MB in 1s, 54.85MB/sindexed 28.05MB in 1s, 54.91MB/sindexed 28.18MB in 1s, 54.96MB/sindexed 28.31MB in 1s, 55.01MB/sindexed 28.44MB in 1s, 55.09MB/sindexed 28.57MB in 1s, 55.16MB/sindexed 28.70MB in 1s, 55.24MB/sindexed 28.84MB in 1s, 55.32MB/sindexed 28.97MB in 1s, 55.40MB/sindexed 29.10MB in 1s, 55.45MB/sindexed 29.23MB in 1s, 55.52MB/sindexed 29.36MB in 1s, 55.61MB/sindexed 29.49MB in 1s, 55.67MB/sindexed 29.62MB in 1s, 55.72MB/sindexed 29.75MB in 1s, 55.73MB/sindexed 29.88MB in 1s, 55.78MB/sindexed 30.02MB in 1s, 55.82MB/sindexed 30.15MB in 1s, 55.88MB/sindexed 30.28MB in 1s, 55.93MB/sindexed 30.41MB in 1s, 55.98MB/sindexed 30.54MB in 1s, 56.02MB/sindexed 30.67MB in 1s, 56.08MB/sindexed 30.80MB in 1s, 56.13MB/sindexed 30.93MB in 1s, 56.18MB/sindexed 31.06MB in 1s, 56.23MB/sindexed 31.19MB in 1s, 56.28MB/sindexed 31.33MB in 1s, 56.32MB/sindexed 31.46MB in 1s, 56.38MB/sindexed 31.59MB in 1s, 56.44MB/sindexed 31.72MB in 1s, 56.47MB/sindexed 31.85MB in 1s, 56.51MB/sindexed 31.98MB in 1s, 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#> Table namq_10_a10 cached at C:\Users\morit\AppData\Local\Temp\RtmpGOqncW/eurostat/ae4f87b6b545fbad92dc2671c1ef1369.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
#> No Outliers or Step-Shifts detected in the marginal equations to test for Super Exogeneity in ln.GValueAddConstr.
#> Hence not possible to run the test.
#> Estimating GValueAddWholesaletrade = Export + LabCostService
#> Estimating FinConsExpHH =
#> No Outliers or Step-Shifts detected in the marginal equations to test for Super Exogeneity in ln.FinConsExpHH.
#> Hence not possible to run the test.
#> Constructing GDP = GValueAddGov + GValueAddAgri + GValueAddIndus + GValueAddConstr + GValueAddWholesaletrade + GValueAddInfocom + GValueAddFinance + GValueAddRealest + GValueAddResearch + GValueAddArts
#> Estimating GCapitalForm = FinConsExpGov + FinConsExpHH
#> Estimating Emissions = GDP + Export + GValueAddIndus
#> Estimating Import = FinConsExpHH + GCapitalForm
model_result
#> OSEM Model Output
#> -----------------------
#>
#> Estimation Options:
#> Sample: 2010-01-01 to 2023-07-01
#> Max AR Considered: 4
#> Estimation Option: ardl
#>
#> Relationships considered:
#> # A tibble: 9 × 3
#> Model `Dep. Var.` `Ind. Var`
#> 1 1 Import "FinConsExpHH + GCapitalForm"
#> 2 2 FinConsExpHH ""
#> 3 3 GCapitalForm "FinConsExpGov + FinConsExpHH"
#> 4 4 Emissions "GDP + Export + GValueAddIndus"
#> 5 5 GDP "GValueAddGov + GValueAddAgri + GValueAddIndus …
#> 6 6 GValueAddGov "FinConsExpGov"
#> 7 7 GValueAddManuf "Export + LabCostManuf"
#> 8 8 GValueAddConstr "LabCostConstr + BuildingPermits"
#> 9 9 GValueAddWholesaletrade "Export + LabCostService"
#>
#>
#> Relationships estimated in the order: 6,7,8,9,2,5,3,4,1
#>
#> Diagnostics:
#> # A tibble: 8 × 8
#> `Dependent Variable` AR ARCH `Super Exogeneity` IIS SIS n
#> <chr> <chr> <chr> <chr> <int> <int> <int>
#> 1 GValueAddGov 0.096* 0.557 "0.357" 10 2 55
#> 2 GValueAddManuf <0.001*** 0.016** "0.059*" 0 4 55
#> 3 GValueAddConstr 0.008*** 0.524 "" 7 3 55
#> 4 GValueAddWholesaletrade 0.616 0.588 "0.240" 0 5 52
#> 5 FinConsExpHH 0.675 0.920 "" 9 0 54
#> 6 GCapitalForm 0.691 0.247 "" 1 1 53
#> 7 Emissions 0.042** 0.364 "0.050**" 0 2 55
#> 8 Import 0.740 0.700 "<0.001***" 5 0 53
#> # ℹ 1 more variable: `Share of Indicators` <dbl>
The 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",
input = "inst/extdata/InputData.xlsx",
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 = 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)