What are the Benefits of High-Frequency Data for Fixed Effects Panel Models. Ghanem, D. & Smith, A. Journal of the Association of Environmental and Resource Economists, 8(2):199-234, 2021.
Paper abstract bibtex High-frequency panel data sets, where outcomes and regressors are observed at a daily or hourly frequency, are increasingly available in environmental and resource economics. To understand the potential gains from these richer datasets, this paper compares fixed effects estimators using high-frequency data with those using temporally aggregated data. We provide a set of conditions under which both estimators are consistent for the same parameter. Three departures from these conditions are: (1) response heterogeneity at the high-frequency dimension, (2) differential response to high- and low-frequency variation in the regressor, (3) nonlinearities in the relationship between the high-frequency outcome and regressor. Under these alternative conditions, the two estimators converge to different probability limits. In general, we recommend that empirical researchers think carefully about the features of the ``true'' high-frequency outcome equation to understand the effects of high-frequency data and temporal aggregation. We illustrate our results using an application to the energy-temperature relationship.
@article{ghanem2021which,
title={What are the Benefits of High-Frequency Data for Fixed Effects Panel Models},
author={Ghanem, Dalia and Smith, Aaron},
journal={Journal of the Association of Environmental and Resource Economists},
volume={8},
number={2},
pages={199-234},
year={2021},
keywords={econometrics},
abstract={High-frequency panel data sets, where outcomes and regressors are observed at a daily or hourly frequency, are increasingly available in environmental and resource economics. To understand the potential gains from these richer datasets, this paper compares fixed effects estimators using high-frequency data with those using temporally aggregated data. We provide a set of conditions under which both estimators are consistent for the same parameter. Three departures from these conditions are: (1) response heterogeneity at the high-frequency dimension, (2) differential response to high- and low-frequency variation in the regressor, (3) nonlinearities in the relationship between the high-frequency outcome and regressor. Under these alternative conditions, the two estimators converge to different probability limits. In general, we recommend that empirical researchers think carefully about the features of the ``true'' high-frequency outcome equation to understand the effects of high-frequency data and temporal aggregation. We illustrate our results using an application to the energy-temperature relationship.},
addendum={\textbf{Winner of Outstanding Publication in JAERE, 2021, AERE}},
url={https://files.asmith.ucdavis.edu/2021_JAERE_GS_highfreq.pdf}
}
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