Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2601.10352 |
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Inhaltsangabe:
- Many economically relevant variables (risk, confidence, uncertainty) are latent and therefore not directly observable, which creates identification challenges in applied regressions. This text formalizes how omitting latent factors generates omitted-variable bias and discusses when including a proxy variable can mitigate it. We distinguish the case of a perfect proxy, which can eliminate the bias, from the more realistic case of an imperfect proxy, where residual bias remains and the estimated effect is attenuated. We propose a practical evaluation protocol based on four properties: relevance, conditional sufficiency, exogeneity, and stability. As an illustration, we use micromobility data from Arlington together with the U.S. Geopolitical Risk Index, estimating cointegration and a bivariate VEC model to interpret local activity as a high-frequency signal of the latent component of geopolitical tension.