Forecasting with panel data
Abstract
This paper gives a brief survey of forecasting with panel data. It begins with a simple error component regression model and surveys the best linear unbiased prediction under various assumptions of the disturbance term. This includes various ARMA models as well as spatial autoregressive models. The paper also surveys how these forecasts have been used in panel data applications, running horse races between heterogeneous and homogeneous panel data models using out‐of‐sample forecasts. Copyright © 2008 John Wiley & Sons, Ltd.
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Panel data give numerous data points, improving the efficiency of the econometric estimates by increasing degrees of freedom and reducing multicollinearity among study variables.
“Panel data also give the researcher numerous data points thus improving the efficiency of the econometric as the degrees of freedom are increased reducing multicollinearity amongst the study variables (Baltagi 2008; Hsiao 2014).”
From Financial inclusion, bank competition and economic growth in Africa (2021) · cites this paper as Baltagi 2008
Cites
Estimating long-run relationships from dynamic heterogeneous panels
Pesaran, M. H., & Smith, R. (1995) · Journal of Econometrics · doi:10.1016/0304-4076(94)01644-f · Cited by 5,479 · cited 9× · see citing works
“In fact, Robertson and Symons (1992) and Pesaran and Smith (1995) questioned the poolability of the data across heterogeneous units.”
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Inequality, ICT and financial access in Africa
Tchamyou, V. S., Erreygers, G., & Cassimon, D. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.11.004 · Cited by 653 · see citing works
The role of governance in mobile phones for inclusive human development in sub-Saharan Africa
Asongu, S. A., & Nwachukwu, J. C. (2016) · Technovation · doi:10.1016/j.technovation.2016.04.002 · Cited by 280 · see citing works
ICT, openness and CO2 emissions in Africa
Asongu, S. A. (2018) · Environmental Science and Pollution Research · doi:10.1007/s11356-018-1239-4 · Cited by 199 · see citing works
Chatti, W. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e08190 · Cited by 152 · see citing works
Financial inclusion, bank competition and economic growth in Africa
Chinoda, T., & Mashamba, T. (2021) · Journal of Economic and Financial Sciences · doi:10.4102/jef.v14i1.649 · Cited by 17 · see citing works
“Panel data also give the researcher numerous data points thus improving the efficiency of the econometric as the degrees of freedom are increased reducing multicollinearity amongst the study variables (Baltagi 2008; Hsiao 2014).”
Background
- Authors
- Baltagi, B. H.
- Affiliations
- University of Leicester (GB)
- Corresponding author
- Baltagi, Badi H.
- ORCID
- Baltagi, Badi H.
- Venue
- Journal of Forecasting
- Publisher
- Wiley
- Published
- 2008-01-21
- Volume
- 27
- Issue
- 2
- Pages
- 153-173
- ISSN
- 0277-6693 (ISSN-L)
- 1099-131X
- OpenAlex
- W3124649769
- Cited by
- 574
- References
- 1
- Citations by year
- 2026: 48 · 2025: 57 · 2024: 55 · 2023: 71 · 2022: 66 · 2021: 47 · 2020: 37 · 2019: 40 · 2018: 36 · 2017: 35 · 2016: 32 · 2015: 12 · 2014: 8 · 2013: 9 · 2012: 10
- FWCI
- 7.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Forecasting (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Leicester Research Archive (University of Leicester) (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- error components model, BLUP, ARMA models, spatial autoregressive model, out-of-sample forecasting
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