“We’re rolling”
Loading...
Date
2021-03
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
In this paper, we present a new dynamic topic modeling method to build stable models and
consistent time series. We call this new method RollingLDA. It has the potential to overcome
several difficulties researchers, who use unsupervised probabilistic topic models, have
grappled with: namely the problem of arbitrary selection, which is aggravated when models
are to be updated with new sequences of data.
RollingLDA is derived by combining the LDAPrototype approach (Rieger, Jentsch and
Rahnenführer, 2020) with an implementation that uses preceding LDA results as an
initialization for subsequent quarters, while allowing topics to change over time. Squaring
dual-process theory, employed in Behavioral Economics (Kahneman, 2011), with the evolving
theory of Economic Narratives (Shiller, 2017), RollingLDA is applied to the measurement of
economic uncertainty.
The new version of our Uncertainty Perception Indicator (UPI), based on a newspaper corpus
of 2.8 million German newspaper articles, published between 1 January 2001 and 31
December 2020, proves indeed capable of detecting an uncertainty narrative. The narrative,
derived from the thorough quantitative-qualitative analysis of a key-topic of our model, can
be interpreted as collective memory of past uncertainty shocks, their causes and the societal
reactions to them. The uncertainty narrative can be seen as a collective intangible cultural
asset (Haskel and Westlake, 2017), accumulated in the past, informing the present and
potentially the future, as the story is being updated and partly overwritten by new
experiences. This concept opens up a fascinating new field for future research.
We would like to encourage researchers to use our data and are happy to share it on request.
Description
Table of contents
Keywords
uncertainty, narratives, latent dirichlet allocation, business cycles, Covid-19, text mining, computational methods, behavioral economics