The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Fully modified OLS for heterogeneous cointegrated panels
Abstract
This chapter uses fully modified OLS principles to develop new methods for estimating and testing hypotheses for cointegrating vectors in dynamic panels in a manner that is consistent with the degree of cross sectional heterogeneity that has been permitted in recent panel unit root and panel cointegration studies. The asymptotic properties of various estimators are compared based on pooling along the ‘within’ and ‘between’ dimensions of the panel. By using Monte Carlo simulations to study the small sample properties, the group mean estimator is shown to behave well even in relatively small samples under a variety of scenarios.
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Citations
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 2×
“Furthermore, as Pesaran & Smith (1995) argue in the context of OLS regressions, when the true slope coef ficients are heterogeneous, group mean estimators provide consistent point estimates of the sample mean of the heterogeneous cointegrating vectors, while pooled within dimension estimators do not.”
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INFRASTRUCTURE, LONG‐RUN ECONOMIC GROWTH AND CAUSALITY TESTS FOR COINTEGRATED PANELS
CANNING, D., & PEDRONI, P. (2008) · The Manchester School · doi:10.1111/j.1467-9957.2008.01073.x · Cited by 421
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Arouri, M. E. H., & Rault, C. (2012) · International Journal of Finance & Economics · doi:10.1002/ijfe.443 · Cited by 375
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Metadata
- Authors
- Pedroni, P.
- Venue
- Advances in Econometrics
- Publisher
- Emerald (MCB UP )
- Published
- 2004-05-19
- Pages
- 93-130
- ISSN
- 0731-9053 (ISSN-L)
- OpenAlex
- W1598969128
- Cited by
- 2,619
- References
- 1
- FWCI
- 125.49
- Type
- book chapter
- Language
- en
- Open access
- closed
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- Pedroni, P. (2004). Fully modified OLS for heterogeneous cointegrated panels. Advances in Econometrics. https://doi.org/10.1016/s0731-9053(00)15004-2
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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.
