Objectives discipline

This model takes in inputs historical data and simulated data for capital and output and computes the mean squared errors (MSE).

$$Mean\_squared\_error = \frac{1}{N} \sum_{i=1}^{N}(x_i-y_i)^2$$ with $x_i$ the observed value and $y_i$ the predicted value.

Inputs

• Economics df ($economics_df$): a dataframe with simulated total capital and output in T$
• The production df ($production_df$): the dataframe with output and net output in T$ for each sector in $sector_list$.
• The capital df ($production_df$): the dataframe with capital and usable capital stock in T$ for each sector in $sector_list$.
• Sector list ($sector_list$): the list of sector coming from the macroeconomics model
• Historical gdp ($historical_gdp$): the historical data for the output in T$
• Historical capital ($historical_capital$): the historical data for the capital in T$

Outputs

• $error_pib_total$ : the MSE for the total output
• $error_cap_total$: the MSE for the total capital
• $sectors_cap_errors$: a dictionary with the MSE for each sector capital
• $sectors_gdp_errors$: a dictionary with the MSE for each sector output
• year start and year end