I intend to construct a sanctions index using a combination of variables, including inflation rate, interest rate, crude oil production, and GDP. For this purpose, I will apply entropy and fuzzy logic (with quantitative data). Specifically, I will assign a weight to each variable based on the extent to which it is affected by sanctions, and then use the following formula to create the sanctions index:
SA=(INF×W(INF))+(INT×W(INT))+(OIL×W(OIL))+(GDP×W(GDP))
In this formula, what values between zero and one should I assign to each variable, considering the extent of their susceptibility to sanctions in an economy dependent on oil revenues?
Using the fuzzy min-max scaling method, I normalized the data (exchange rate, interest rate, crude oil production, and GDP). I then calculated the average of each variable and determined the weights. Considering that the data pertains to an oil-dependent economy, I adjusted the determined weights by multiplying them with subjective weights based on my judgment. Subsequently, I constructed the sanctions index using the specified formula, converting it into a quantitative variable.
The weights I determined based on my judgment:
Inflation rate:0.20 Interest rate:0.08 Crude oil production:0.37 GDP:0.35
The final weight of each variable is as follows:
Inflation rate:0.22 Interest rate:0.11 Crude oil production: 0.45 GDP:0.23
From an economic perspective, have these weights been appropriately assigned, considering the impact of sanctions on each component?
To construct a sanctions index combining inflation rate, interest rate, crude oil production, and GDP, follow these steps:
This index will reflect the combined influence of these factors on the economic impact of sanctions
Dear Chuck A Arize I am very grateful for your guidance on this matter
To construct a sanctions index using inflation rate (INF), interest rate (INT), crude oil production (OIL), and GDP, you need to assign weights to each variable based on their susceptibility to sanctions, especially in an oil-dependent economy. The formula \( SA = (INF \times W(INF)) + (INT \times W(INT)) + (OIL \times W(OIL)) + (GDP \times W(GDP)) \) allows you to combine these variables, with weights (W) between 0 and 1 representing the extent to which each is impacted by sanctions. In oil-dependent economies, crude oil production (OIL) is usually the most affected, so W(OIL) should have a higher value, possibly between 0.7 and 0.9. Inflation (INF) and GDP might receive moderate to high weights (0.5 to 0.7 for INF and 0.5 to 0.8 for GDP), reflecting their indirect effects, while interest rates (INT) could have a moderate weight (0.3 to 0.5). You can apply entropy weighting to objectively determine weights based on variability in the data, and fuzzy logic to handle uncertainties, assigning weights based on how each variable is affected by sanctions in your specific context.
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