Time-specific average estimation of dynamic panel regressions
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Ba Chu
Abstract
This paper introduces an unbiased estimator based on least squares involving time-specific cross-sectional averages for a first-order panel autoregression with a strictly exogenous covariate. The proposed estimator is straightforward to implement as long as the variables of interest have sufficient time variation. The number of cross-sections (N) and the number of time periods (T) can be large, and there is no restriction on the growth rate of N relative to T. It is demonstrated via both theory and a simulation study that the estimator is asymptotically unbiased, and it can provide correct empirical coverage probabilities for the ‘true’ coefficients of the model for various combinations of N and T. An empirical application is also provided to confirm the feasibility of the proposed approach.
Funding source: Social Sciences and Humanities Research Council of Canada
Award Identifier / Grant number: 430-2016-00682
Funding source: Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
Award Identifier / Grant number: RGPIN-2020-04161
Acknowledgments
I would like to sincerely thank the editor (Prof. Bruce Mizrach) and two referees for their insightful comments that led to corrections, clarifications, and various improvements in the paper. The remaining errors are mine.
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Author contribution: The author has accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: I gratefully acknowledge funding from the Social Science and Humanities Research Council of Canada (MBF Grant 430-2016-00682) and the Natural Sciences and Engineering Research Council of Canada (Grant RGPIN-2020-04161).
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Conflict of interest statement: The author declares no conflicts of interest regarding this article.
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Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/snde-2019-0084).
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Artikel in diesem Heft
- Frontmatter
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- What does Google say about credit developments in Brazil?
- Forecasting transaction counts with integer-valued GARCH models
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Artikel in diesem Heft
- Frontmatter
- Research Articles
- What does Google say about credit developments in Brazil?
- Forecasting transaction counts with integer-valued GARCH models
- Asymmetries in the monetary policy reaction function: evidence from India
- A mixture autoregressive model based on Gaussian and Student’s t-distributions
- Time-specific average estimation of dynamic panel regressions
- Rescaled variance tests for seasonal stationarity
