The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Which panel data estimator should I use?
Abstract
This study employs Monte Carlo experiments to evaluate the performances of a number of common panel data estimators when serial correlation and cross-sectional dependence are both present. It focuses on fixed effects models with less than 100 cross-sectional units and between 10 and 25 time periods (such as are commonly employed in empirical growth studies). Estimator performance is compared on two dimensions: (i) root mean square error and (ii) accuracy of estimated confidence intervals. An innovation of our study is that our simulated panel data sets are designed to look like ‘real-world’ panel data. We find large differences in the performances of the respective estimators. Further, estimators that perform well on efficiency grounds may perform poorly when estimating confidence intervals and vice versa. Our experimental results form the basis for a set of estimator recommendations. These are applied to ‘out of sample’ simulated panel data sets and found to perform well.
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Cites
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Environmental, social and governance transparency and firm value
Yu, E. P., Guo, C. Q., & Luu, B. V. (2018) · Business Strategy and the Environment · doi:10.1002/bse.2047 · Cited by 613
Drivers and barriers in the transition from a linear economy to a circular economy
Neves, S. A., & Marques, A. C. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.130865 · Cited by 359
Khan, M. T. I., Yaseen, M. R., & Ali, Q. (2019) · Energy Economics · doi:10.1016/j.eneco.2019.07.018 · Cited by 256
Impact of climate change on cereal production: evidence from lower-middle-income countries
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Metadata
- Authors
- Reed, W. R., & Ye, H.
- Venue
- Applied Economics
- Publisher
- Informa UK Limited
- Published
- 2009-07-16
- Volume
- 43
- Issue
- 8
- Pages
- 985-1000
- ISSN
- 0003-6846 (ISSN-L)
- 1466-4283
- OpenAlex
- W2063207649
- Cited by
- 440
- References
- 14
- FWCI
- 11.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- doi.org/10.1080/policies-text-data-mining (tdm)
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
