# Text as Data: Real-time Measurement of Economic Welfare

@article{Nyman2020TextAD, title={Text as Data: Real-time Measurement of Economic Welfare}, author={Rickard Nyman and Paul Ormerod}, journal={arXiv: General Economics}, year={2020} }

Economists are showing increasing interest in the use of text as an input to economic research. Here, we analyse online text to construct a real time metric of welfare. For purposes of description, we call it the Feel Good Factor (FGF). The particular example used to illustrate the concept is confined to data from the London area, but the methodology is readily generalisable to other geographical areas. The FGF illustrates the use of online data to create a measure of welfare which is not based…

## References

SHOWING 1-10 OF 19 REFERENCES

Evolving Measurement for an Evolving Economy: Thoughts on 21st Century US Economic Statistics

- BusinessJournal of Economic Perspectives
- 2019

The system of federal economic statistics developed in the 20th century has served the country well, but the current methods for collecting and disseminating these data products are unsustainable.…

Is growth obsolete

- Economics
- 1973

A long decade ago economic growth was the reigning fashion of political economy. It was simultaneously the hottest subject of economic theory and research, a slogan eagerly claimed by politicians of…

Measuring Economic Policy Uncertainty

- Economics
- 2013

We develop a new index of economic policy uncertainty (EPU) based on newspaper coverage frequency. Several types of evidence – including human readings of 12,000 newspaper articles – indicate that…

Text As Data

- Computer Science
- 2017

An introduction to the use of text as an input to economic research is provided, the features that make text different from other forms of data are discussed, and a practical overview of relevant statistical methods is offered.

GloVe: Global Vectors for Word Representation

- Computer ScienceEMNLP
- 2014

A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.

Finding community structure in networks using the eigenvectors of matrices.

- Mathematics, MedicinePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2006

A modularity matrix plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations, and a spectral measure of bipartite structure in networks and a centrality measure that identifies vertices that occupy central positions within the communities to which they belong are proposed.

Finding and evaluating community structure in networks.

- Computer Science, PhysicsPhysical review. E, Statistical, nonlinear, and soft matter physics
- 2004

It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.

Do we need hundreds of classifiers to solve real world classification problems?

- Mathematics, Computer ScienceJ. Mach. Learn. Res.
- 2014

The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in theTop-20, respectively).

Analysis of a Random Forests Model

- Mathematics, Computer ScienceJ. Mach. Learn. Res.
- 2012

An in-depth analysis of a random forests model suggested by Breiman (2004), which is very close to the original algorithm, and shows in particular that the procedure is consistent and adapts to sparsity, in the sense that its rate of convergence depends only on the number of strong features and not on how many noise variables are present.

Bit by bit: social research in the digital age

- Mathematics, SociologyThe Journal of Mathematical Sociology
- 2019

In his 1963 book Informal Sociology, William Bruce Cameron wrote the often-misattributed quote “not everything that can be counted counts, and not everything that counts can be counted”. With this ...