Wasserstein Index Generation Model: Automatic Generation of Time-series Index with Application to Economic Policy Uncertainty

Xie, Fangzhou

doi:10.2139/ssrn.3491416

Cited by 3 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, Azqueta-Gavaldon (2017) finds his index to be highly correlated to the original categorical EPU indices of Baker et al (2016). Xie (2020) proposed a novel method to construct sentiment indices with Wasserstein Index Generation model (WIG). WIG is an unsupervised machine learning method, so there is no need for human classifying of newspaper articles from The New York Times, as in Baker et al (2016).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Such reactions have prompted Baker et al ( 2016 ) to perform a large-scale human audit study of the articles entering the EPU coding, showing that the computer-based EPU is highly correlated to the human-audited one. Moreover, a separate branch of literature has emerged, focusing on the potentials of utilizing machine learning techniques to enhance the accuracy of the EPU index (Azqueta-Gavaldon, 2017 ; Tobback et al, 2016 ; Xie, 2020 ). And this is precisely the niche that we want to tackle with this study.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Economic Policy Uncertainty Index Meets Ensemble Learning

2021

View full text Add to dashboard Cite

We utilize a battery of ensemble learning techniques [ensemble linear regression (LM), random forest], as well as two gradient boosting techniques [Gradient Boosting Decision Tree and Extreme Gradient Boosting (XGBoost)] to scrutinize the possibilities of enhancing the predictive accuracy of Economic Policy Uncertainty (EPU) index. Applied to a data-rich environment of the Newsbank media database, our LM and XGBoost assessments mostly outperform the other two ensemble learning procedures, as well as the original EPU index. Our LM and XGBoost estimates bring EPU closer to the stylized facts of uncertainty than other uncertainty estimates. LM and XGBoost indicators are more countercyclical and have more pronounced leading properties. We find that EPU is more strongly correlated to financial volatility measures than to consumers' assessments of uncertainty. This corroborates that the media place a much higher weight on the financial sector than on the economic issues of consumers. Further on, we considerably widen the scope of search terms included in the calculation of EPU index. Using ensemble learning techniques on such a rich set of keywords, we mostly manage to outperform the standard EPU in terms of correlation with standard uncertainty proxies. We also find that the predictive accuracy of EPU index can be considerably increased using a more diversified set of uncertainty-related terms than the original EPU framework. Our estimates perform much better in a monthly setting (targeting the industrial production growth) than targeting quarterly GDP growth. This speaks in favor of uncertainty as a purely short-term phenomenon.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Economic Policy Uncertainty Index Meets Ensemble Learning

2021

View full text Add to dashboard Cite

show abstract

“…I also report Pearson's and Spearman's correlation test on four set of automatically generated EPU indices (one LDA-based EPU (Azqueta-Gavaldón, 2017), one WIG-based EPU (Xie, 2020), and two flavor of WIG given by wigpy package in this paper), against the original EPU 8 (Baker et al, 2016). In Table 2, the correlation statistics between EPU generated by WIGs and two other indices: VIX and Michigan Consumer Confidence Sentiment index.…”

Section: Application To Generating Economic Policy Uncertainty Index mentioning

confidence: 99%

“…Recently, the Wasserstein Index Generation model (Xie, 2020) was proposed to generate time-series sentiment indices automatically. There have been several methods (Azqueta-Gavaldón, 2017;Baker, Bloom, & Davis, 2016;Castelnuovo & Tran, 2017;Ghirelli, Pérez, & Urtasun, 2019) proposed to generate time series sentiment indices, but, to the best of my knowledge, WIG is the first automatic method to produce sentiment indices completely free of manual work.…”

Section: Introductionmentioning

confidence: 99%

Pruned Wasserstein Index Generation Model and wigpy Package

Xie

2020

CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics

Self Cite

View full text Add to dashboard Cite

Recent proposal of Wasserstein Index Generation model (WIG) has shown a new direction for automatically generating indices. However, it is challenging in practice to fit large datasets for two reasons. First, the Sinkhorn distance is notoriously expensive to compute and suffers from dimensionality severely. Second, it requires to compute a full N × N matrix to be fit into memory, where N is the dimension of vocabulary. When the dimensionality is too large, it is even impossible to compute at all. I hereby propose a Lasso-based shrinkage method to reduce dimensionality for the vocabulary as a pre-processing step prior to fittig the WIG model. After we get the word embedding from Word2Vec model, we could cluster these high-dimensional vectors by k-means clustering, and pick most frequent tokens within each cluster to form the “base vocabulary”. Non-base tokens are then regressed on the vectors of base token to get a transformation weight and we could thus represent the whole vocabulary by only the “base tokens”. This variant, called pruned WIG (pWIG), will enable us to shrink vocabulary dimension at will but could still achieve high accuracy. I also provide a wigpy module in Python to carry out computation in both flavor. Application to Economic Policy Uncertainty (EPU) index is showcased as comparison with existing methods of generating time-series sentiment indices.

show abstract

“…It is important to distinguish our study from studies of uncertainty that have used texts to construct measures of overall and topic-specific uncertainty (Baker et al, 2016;Larsen, 2017;Saltzman & Yung, 2018;Azqueta-Gavaldón, 2017;Xie, 2020, Husted et al, 2019. Their focus is measuring uncertainty and their text-based indices reflect both endogenous movements in uncertainty and uncertainty shocks.…”

mentioning

confidence: 99%

A Narrative Approach to Creating Instruments with Unstructured and Voluminous Text: An Application to Policy Uncertainty

Ryan

2020

SSRN Journal

View full text Add to dashboard Cite

We quantify the effects of policy uncertainty on the economy using a proxy structural vector autoregression (SVAR). Our instrument in the proxy SVAR is a set of exogenous uncertainty events constructed using a text-based narrative approach. Usually the narrative approach involves manually reading texts, which is difficult in our application as our text-the parliamentary record-is unstructured and lengthy. To deal with such circumstances, we develop a procedure using a natural language technique, latent Dirichlet analysis. Our procedure extends the possible application of the narrative identification approach. We find the effects of policy uncertainty are significant, and are underestimated using alternative identification methods.

show abstract

Wasserstein Index Generation Model: Automatic Generation of Time-series Index with Application to Economic Policy Uncertainty

Cited by 3 publications

References 0 publications

Economic Policy Uncertainty Index Meets Ensemble Learning

Economic Policy Uncertainty Index Meets Ensemble Learning

Pruned Wasserstein Index Generation Model and wigpy Package

A Narrative Approach to Creating Instruments with Unstructured and Voluminous Text: An Application to Policy Uncertainty

Contact Info

Product

Resources

About