osem

A novel Open-Source Empircial Macro (OSEM) Model to study climate policies and the wider macro-economy that can easily be applied to a large set of countries

https://github.com/moritzpschwarz/osem

Repository

A novel Open-Source Empircial Macro (OSEM) Model to study climate policies and the wider macro-economy that can easily be applied to a large set of countries

README.Rmd

---
output: github_document
always_allow_html: true
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  fig.width = 7,
  fig.height = 5,
  dev = "png",
  dpi = 600
)
```

# osem - Open Source Empirical Macro Model


[![R-CMD-check](https://github.com/moritzpschwarz/osem/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/moritzpschwarz/osem/actions/workflows/R-CMD-check.yaml)
[![Codecov test coverage](https://codecov.io/gh/moritzpschwarz/osem/branch/main/graph/badge.svg)](https://app.codecov.io/gh/moritzpschwarz/osem?branch=main)


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](https://normetrics.no/nam/), developed by Gunnar Bardsen and Ragnar Nymoen. 

## Installation

You can install the development version of {osem} from [GitHub](https://github.com/) with:

``` r
# 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:

```{r, include=FALSE}
library(tidyverse)
```


```{r loading}
library(osem)
```

### 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.

```{r set-up}
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: 

```{r, include=FALSE}
library(kableExtra, verbose = FALSE)
```



```{r, results='asis', echo=FALSE}
kable(spec) %>% 
  kable_styling() %>% 
  kable_paper()
```


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):


```{r, results='asis', echo=FALSE}
osem::dict %>% 
  head() %>% 
  kable() %>% 
  kable_styling() %>% 
  kable_paper()
```



























### Running the model

Now we are ready to run the model with the `run_model()` function:

```{r}
model_result <- run_model(
  specification = spec,
  save_to_disk = "inst/extdata/InputData.xlsx",
  primary_source = "download",
  trend = TRUE,
  saturation.tpval = 0.01,
  plot = FALSE
)
```

```{r}
model_result
```


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: 
```{r, eval=FALSE}
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):

```{r}
model_forecast <- forecast_model(model_result, n.ahead = 10, exog_fill_method = "AR", plot = FALSE)
```

Once we are done, we can plot the forecast: 

```{r}
plot(model_forecast, order.as.run = TRUE)
```

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