arXiv 29 Jun 2026 · Econometrics
arXiv:2606.30040 · PDF · DOI · OpenAlex · Extracted main text
Macroeconomic expectations are usually observed through point forecasts or through asset prices whose mapping into beliefs is model-dependent. This paper uses prediction-market prices to recover high-frequency distributions of short-run macroeconomic beliefs. We construct a panel of Kalshi-implied distributions for CPI and core CPI releases by converting adjacent threshold contracts into probability mass over inflation outcomes. The data reveal market-implied means, uncertainty, and upper-tail probabilities from 30 days to one hour before each release. The market-implied mean contains meaningful forecast information, especially for headline CPI, but the main signal is distributional. Lagged Reuters Poll surprises do not predict systematic deviations of Kalshi means from the current Reuters consensus. By contrast, large lagged surprises are associated with higher implied uncertainty, and positive lagged surprises raise the probability assigned to fixed high-inflation outcomes. In the baseline specification with variable-by-horizon fixed effects, a 0.1 percentage point positive lagged surprise raises the probability of monthly inflation above 0.3 percent by about 4.7 percentage points, even after controlling for the current consensus forecast. In release-level validation tests, Kalshi upper-tail probabilities also predict the realization of high-inflation states, including episodes in which the market-implied mean remains close to the Reuters consensus. The evidence suggests that prediction markets can provide real-time information about inflation risk that is missed by point forecasts.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Burgi, C., Deng, W., Whelan, K (2026) Makers and takers: The economics of the Kalshi prediction market | 0.843 | 4 | 4 | 75% |
| 2 | Akey, P., Gregoire, V., Harvie, N., Martineau, C (2026) Who wins and who loses in prediction markets? Evidence from Polymarket | 0.737 | 3 | 3 | 67% |
| 3 | Dubach, P.D (2026) The anatomy of a decentralized prediction market: Microstructure evidence from the Polymarket order book | 0.737 | 3 | 3 | 67% |
| 4 | Andrade, P., Crump, R.K., Eusepi, S., Moench, E (2016) Fundamental disagreement | 0.644 | 2 | 2 | 100% |
| 5 | Croushore, D (1993) Introducing: The Survey of Professional Forecasters | 0.644 | 2 | 2 | 100% |
| 6 | Engelberg, J., Manski, C.F., Williams, J (2009) Comparing the point predictions and subjective probability distributions of professional forecasters | 0.644 | 2 | 2 | 100% |
| 7 | Manski, C.F (2006) Interpreting the predictions of prediction markets | 0.644 | 2 | 2 | 100% |
| 8 | Wolfers, J., Zitzewitz, E (2004) Prediction markets | 0.644 | 2 | 2 | 100% |
| 9 | Wolfers, J., Zitzewitz, E (2006) Interpreting prediction market prices as probabilities | 0.644 | 2 | 2 | 100% |
| 10 | Diercks, A.M., Katz, J.D., Wright, J.H (2026) Kalshi and the rise of macro markets | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 40 scored citations.