Scaling Political Positions from Text: Assumptions, Methods and Pitfalls

Egerod, Benjamin C. K.; Klemmensen, Robert

doi:10.4135/9781526486387.n30

Cited by 8 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The documents are on average more than 26,000 words long. This figure is almost 15 greater than the 1,800 words threshold recommended by Egerod and Klemmensen (2020) for an effective use of scaling methods.…”

Section: Methodsmentioning

confidence: 67%

“…We ran four separate analyses for each of the missions in which German troops were involved to reduce linguistic heterogeneity in the documents. Wordfish performances worsen as the quantity of unique words in the texts becomes excessively large (Egerod and Klemmensen 2020;Hjorth et al 2015). In each of the analyses, we set the parameters to produce a dimension in which higher scores of both discriminating words and final positions correspond to support for the intervention in Afghanistan and vice versa.…”

Section: Empirical Analysismentioning

confidence: 99%

See 1 more Smart Citation

Ideological Talk, Strategic Vote: German Parties’ Positions on the Military Intervention in Afghanistan in Parliament

2022

View full text Add to dashboard Cite

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

show abstract

Section: Methodsmentioning

confidence: 67%

Section: Empirical Analysismentioning

confidence: 99%

Ideological Talk, Strategic Vote: German Parties’ Positions on the Military Intervention in Afghanistan in Parliament

2022

View full text Add to dashboard Cite

show abstract

“…One shortfall of statistical-based models such as Wordscores and Wordfish is the impact that decreasing document length could have on lowering performance (Egerod & Klemmensen, 2019). This is addressed in the methodological approach by selecting random samples of comparably sized articles, which in the case of For illustrative purposes, the banking crisis spectrum of no-crisis to crisis is overlaid onto the left-wing to right-wing political axes.…”

Section: Textual Datamentioning

confidence: 99%

Can Text-Based Statistical Models Reveal Impending Banking Crises?

du Plessis

2024

Comput Econ

View full text Add to dashboard Cite

This paper introduces statistical models Wordscores and Wordfish to study and predict banking crises. While Wordscores is akin to supervised learning, Wordfish is analogous to unsupervised learning. Both methods estimate the position of banking distress on a tranquil-to-crisis spectrum. Findings suggest that the two statistical methods signal banking crisis up to two-years in advance, with robust results from AUROC, Granger causality and VAR impulse responses. Both methods outperform random forests in predicting crises using textual data. The Wordscores index highlights increased usage of banking sector nomenclature two years preceding a crisis, and Granger causes a crisis series with one and two lag lengths. Results from the Wordfish technique, a statistical model with Poisson distribution, show the index spikes before and during the Global Financial Crisis, when a large share of the countries in the world encountered banking crises. This paper contributes to literature on text-based models of banking crises by bolstering the preemptive policy responses available to policy makers. Given their early warning signals, both Wordscores and Wordfish can be considered a part of the toolset to monitor the stability and resilience of the banking sector.

show abstract