Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index

• URL: http://arxiv.org/abs/2111.00057v1
• Date: Fri, 29 Oct 2021 19:31:03 GMT
• Title: Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index
• Authors: Hairo U. Miranda Belmonte and Victor Mu\~niz-S\'anchez and Francisco Corona
• Abstract summary: Quantification of economic uncertainty is a key concept for the prediction of macro economic variables such as GDP. Economic policy uncertainty (EPU) index is the most used newspaper-based indicator to quantify the uncertainty. We propose a methodology to estimate the EPU index, which incorporates a fast and efficient method for topic modeling of digital news.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantification of economic uncertainty is a key concept for the prediction of macro economic variables such as gross domestic product (GDP), and it becomes particularly relevant on real-time or short-time predictions methodologies, such as nowcasting, where it is required a large amount of time series data, commonly with different structures and frequencies. Most of the data comes from the official agencies statistics and non-public institutions, however, relying our estimates in just the traditional data mentioned before, have some disadvantages. One of them is that economic uncertainty could not be represented or measured in a proper way based solely in financial or macroeconomic data, another one, is that they are susceptible to lack of information due to extraordinary events, such as the current COVID-19 pandemic. For these reasons, it is very common nowadays to use some non-traditional data from different sources, such as social networks or digital newspapers, in addition to the traditional data from official sources. The economic policy uncertainty (EPU) index, is the most used newspaper-based indicator to quantify the uncertainty, and is based on topic modeling of newspapers. In this paper, we propose a methodology to estimate the EPU index, which incorporates a fast and efficient method for topic modeling of digital news based on semantic clustering with word embeddings, allowing to update the index in real-time, which is a drawback with another proposals that use computationally intensive methods for topic modeling, such as Latent Dirichlet Allocation (LDA). We show that our proposal allow us to update the index and significantly reduces the time required for new document assignation into topics.

Related papers

• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• Fast Model Debias with Machine Unlearning [54.32026474971696]
  Deep neural networks might behave in a biased manner in many real-world scenarios. Existing debiasing methods suffer from high costs in bias labeling or model re-training. We propose a fast model debiasing framework (FMD) which offers an efficient approach to identify, evaluate and remove biases.
  arXiv  Detail & Related papers  (2023-10-19T08:10:57Z)
• Conservative Predictions on Noisy Financial Data [6.300716661852326]
  Price movements in financial markets are well known to be very noisy. Traditional rule-learning techniques would seek only high precision rules and refrain from making predictions where their antecedents did not apply. We apply a similar approach, where a model abstains from making a prediction on data points that it is uncertain on.
  arXiv  Detail & Related papers  (2023-10-18T09:14:19Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Macroeconomic Predictions using Payments Data and Machine Learning [0.0]
  This paper aims to demonstrate that non-traditional and timely data can provide policymakers with sophisticated models to accurately estimate key macroeconomic indicators in near real-time. We provide a set of econometric tools to mitigate overfitting and interpretability challenges in machine learning models to improve their effectiveness for policy use. Our models with payments data, nonlinear methods, and tailored cross-validation approaches help improve macroeconomic nowcasting accuracy up to 40% -- with higher gains during the COVID-19 period.
  arXiv  Detail & Related papers  (2022-09-02T11:12:10Z)
• Weak Supervision in Analysis of News: Application to Economic Policy Uncertainty [0.0]
  Our work focuses on studying the potential of textual data, in particular news pieces, for measuring economic policy uncertainty (EPU) Economic policy uncertainty is defined as the public's inability to predict the outcomes of their decisions under new policies and future economic fundamentals. Our work proposes a machine learning based solution involving weak supervision to classify news articles with regards to economic policy uncertainty.
  arXiv  Detail & Related papers  (2022-08-10T09:08:29Z)
• Benchmarking Econometric and Machine Learning Methodologies in Nowcasting [0.0]
  Nowcasting can play a key role in giving policymakers timelier insight to data published with a significant time lag. This paper examines the performance of 12 different methodologies in nowcasting US quarterly GDP growth. Performance was assessed on three different tumultuous periods in US economic history.
  arXiv  Detail & Related papers  (2022-05-06T15:51:31Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting [93.73198973454944]
  Deep learning models have led to significant performance improvements in many problems coming from different domains, including prediction problems of financial time-series data. The Temporal Attention-Augmented Bilinear network was recently proposed as an efficient and high-performing model for Limit Order Book time-series forecasting. In this paper, we propose a low-rank tensor approximation of the model to further reduce the number of trainable parameters and increase its speed.
  arXiv  Detail & Related papers  (2021-07-05T10:15:23Z)
• Aggregate Learning for Mixed Frequency Data [0.0]
  We propose a mixed-temporal aggregate learning model that predicts economic indicators for smaller areas in real-time. We find that the proposed model predicts (i) the regional heterogeneity of the labor market condition and (ii) the rapidly changing economic status. The model can be applied to various tasks, especially economic analysis.
  arXiv  Detail & Related papers  (2021-05-20T08:12:43Z)
• Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications [110.54266632357673]
  We present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals.
  arXiv  Detail & Related papers  (2020-05-09T01:32:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.