- This project presents a new approach to building category-specific Economic Policy Uncertainty (EPU) indices by extracting keywords from texts via the genetic algorithm.
- Knowing that the EPU indices vary with different sets of EPU terms, we let the EPU terms vary to fit a specific target variable to obtain the final optimized EPU index.
- We proceed in three steps:
- First, with initial EPU terms, we use a pre-trained word embedding space to extend the set of candidate keywords.
- A word embedding space maps words into high dimensional vectors. Words with similar semantic meanings are close to each other in the space. We use the pre-trained embedding space from Pennington et al. with GloVe Technique.
- Second, using the genetic algorithm, we search for the best subset of candidate keywords that matches the dynamics of a pre-determined target variable.
- Last, following the same steps proposed by Baker et al. (2016), we build the new category-specific EPU index.
- First, with initial EPU terms, we use a pre-trained word embedding space to extend the set of candidate keywords.
june911 / epu_geneticalgo Goto Github PK
View Code? Open in Web Editor NEWDeveloping Category-specific Economic Policy Uncertainty Indices through Genetic Algorithm
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent