Thank you for tuning in to this audio only podcast presentation. This is week 138 of The Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for The Lindahl Letter is, “Prediction markets & Time-series analysis.”
We have been going down the door of digging into considering elections for a few weeks now. You knew this topic was going to show up. People love prediction markets. They are really a pooled reflection of sentiment about the likelihood of something occuring. Right now the scuttlebut of the internet is about LK-99, a potential, maybe debunked, maybe possible room temperature superconductor that people are predicting whether or not it will be replicated before 2025 [1]. You can read the 22 page preprint about LK-99 on ArXiv [2]. My favorite article about why this would be a big deal if it lands was from Dylan Matthews over at Vox [3]. Being able to advance the transmission power of electrical lines alone would make this a breakthrough.
That brief example being set aside, now people can really dial into the betting markets for elections where right now are not getting nearly the same level of attention as LK-99 which is probably accurate in terms of general scale of possible impact. You can pretty quickly get to all posts that the team over at 538 have tagged for “betting markets” and that is an interesting thing to scroll through [4]. Beyond that look you could start to dig into an article from The New York Times talking about forecasting what will happen to prediction markets in the future [5].
You know it was only a matter of time before we moved from popular culture coverage to the depths of Google Scholar [6].
Snowberg, E., Wolfers, J., & Zitzewitz, E. (2007). Partisan impacts on the economy: evidence from prediction markets and close elections. The Quarterly Journal of Economics, 122(2), 807-829. https://www.nber.org/system/files/working_papers/w12073/w12073.pdf
Arrow, K. J., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J. O., … & Zitzewitz, E. (2008). The promise of prediction markets. Science, 320(5878), 877-878. https://users.nber.org/~jwolfers/policy/StatementonPredictionMarkets.pdf
Berg, J. E., Nelson, F. D., & Rietz, T. A. (2008). Prediction market accuracy in the long run. International Journal of Forecasting, 24(2), 285-300. https://www.biz.uiowa.edu/faculty/trietz/papers/long%20run%20accuracy.pdf
Wolfers, J., & Zitzewitz, E. (2004). Prediction markets. Journal of economic perspectives, 18(2), 107-126. https://pubs.aeaweb.org/doi/pdf/10.1257/0895330041371321
Yeah, you could tell by the title that a little bit of content related to time-series analysis was coming your way. The papers being tracked within Google Scholar related election time series analysis were not highly cited and to my extreme disappointment are not openly shared as PDF documents [7]. For those of you who are regular readers you know that I try really hard to only share links to open access documents and resources that anybody can consume along their lifelong learning journey. Sharing links to paywalls and articles inside a gated academic community is not really productive for general learning.
Footnotes:
[2] https://arxiv.org/ftp/arxiv/papers/2307/2307.12008.pdf
[3] https://www.vox.com/future-perfect/23816753/superconductor-room-temperature-lk99-quantum-fusion
[4] https://fivethirtyeight.com/tag/betting-markets/
[5] https://www.nytimes.com/2022/11/04/business/election-prediction-markets-midterms.html
[6] https://scholar.google.com/scholar?hl=en&as_sdt=0%2C6&q=election+prediction+markets&btnG=
What’s next for The Lindahl Letter?
- Week 139: Machine learning election models
- Week 140: Proxy models for elections
- Week 141: Election expert opinions
- Week 142: Door-to-door canvassing
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