# WIG Model

    library(rwig) |> suppressPackageStartupMessages()

The Wasserstein Index Generation (WIG) model leverages the WDL model for topic modeling and generates time series sentiment index, given that the docs are associated with timestamps. This was used to automatically reconstruct the Economic Policy Uncertain (EPU) index.

    # create a small dataset
    wigdf <- data.frame(
      ref_date = as.Date(c("2012-01-01", "2012-02-01")),
      docs = c("this is a sentence", "this is another sentence")
    )

    wigfit <- wig(wigdf, ref_date, docs, specs = wig_specs(
      wdl_control = list(num_topics = 2),
      word2vec_control = list(min_count = 1)
    ))
    #> Preprocessing the data...
    #> Running tokenizer on the sentences...
    #> Running Word2Vec for the embeddings and distance matrix...
    #> `method` is automatically switched to "log"
    #> Running WDL in CUDA mode...
    #> This might take a while depending on the problem size...
    #> Initializing WDL model with 3 vocabs, 2 docs, and 2 topics...
    #> Training WDL model with 2 epochs, 1 batches
    #> Epoch 1 of 2, batch 1 of 1
    #>   batch time: 0.00 sec
    #> Epoch 2 of 2, batch 1 of 1
    #>   batch time: 0.00 sec
    #> Inference on the dataset
    #>   Inference: 2 of 2 docs done
    wigfit
    #> WIG model from 2012-01-01 to 2012-02-01
    #> WDL model topics:
    #>
    #> Topic 1:
    #> sentenc    </s>   anoth
    #>    0.44    0.41    0.15

## See Also

See also `vignette("wdl-model")`, `vignette("specs")`.

## References

Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024

Xie, F. (2020). Wasserstein index generation model: Automatic generation of time-series index with application to economic policy uncertainty. Economics Letters, 186, 108874. https://doi.org/10.1016/j.econlet.2019.108874