arXiv 21 Oct 2024 · Econometrics · 3 citations (OpenAlex)
arXiv:2410.16112 · PDF · DOI · OpenAlex · Extracted main text
This paper identifies an important bias - termed dynamic bias - in fixed effects panel estimators that arises when dynamic feedback is ignored in the estimating equation. Dynamic feedback occurs if past outcomes impact current outcomes, a feature of many settings ranging from economic growth to agricultural and labor markets. When estimating equations omit past outcomes, dynamic bias can lead to significantly inaccurate treatment effect estimates, even with randomly assigned treatments. This dynamic bias in simulations is larger than Nickell bias. I show that dynamic bias stems from the estimation of fixed effects, as their estimation generates confounding in the data. To recover consistent treatment effects, I develop a flexible estimator that provides fixed-T bias correction. I apply this approach to study the impact of temperature shocks on GDP, a canonical example where economic theory points to an important feedback from past to future outcomes. Accounting for dynamic bias lowers the estimated effects of higher yearly temperatures on GDP growth by 10% and GDP levels by 120%.
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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 | Melissa Dell, Benjamin F Jones, and Benjamin A Olken (2012) Temperature shocks and economic growth: Evidence from the last half century | 0.964 | 19 | 4 | 89% |
| 2 | Manuel Arellano and Stephen Bond (1991) Some tests of specification for panel data: Monte carlo evidence and an application to employment equations | 0.928 | 4 | 3 | 100% |
| 3 | Francis Annan and Wolfram Schlenker (2015) Federal crop insurance and the disincentive to adapt to extreme heat | 0.874 | 6 | 4 | 67% |
| 4 | Jörg Breitung, Sebastian Kripfganz, and Kazuhiko Hayakawa (2022) Bias-corrected method of moments estimators for dynamic panel data models | 0.874 | 5 | 2 | 100% |
| 5 | Robert M Solow (1956) A contribution to the theory of economic growth | 0.843 | 4 | 4 | 75% |
| 6 | Marshall Burke, Solomon M Hsiang, and Edward Miguel (2015) Global non-linear effect of temperature on economic production | 0.843 | 3 | 3 | 100% |
| 7 | Arturas Juodis et al (2015) Iterative bias correction procedures revisited: A small scale monte carlo study | 0.737 | 3 | 2 | 100% |
| 8 | Jan F Kiviet (1995) On bias, inconsistency, and efficiency of various estimators in dynamic panel data models | 0.693 | 5 | 1 | 100% |
| 9 | Juliano Assuncão, Robert McMillan, Joshua Murphy, and Eduardo Souza-… (2023) Optimal environmental targeting in the amazon rainforest | 0.644 | 2 | 2 | 100% |
| 10 | Olivier J Blanchard and Lawrence H Summers (1988) Beyond the natural rate hypothesis | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 66 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Forecasted Treatment Effects with Short Panels | 0.405 | 1 | 1 |
| 2 | Heterogeneous Treatment Effects via Linear Dynamic Panel Data Models | 0.405 | 1 | 1 |
| 3 | Back to Feedback Dynamics and Heterogeneity in Panel Data | 0.405 | 1 | 1 |