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Serial correlation in multiregional migration models
Journal of Regional Science
|November 1, 1990
Summary
This study on multiregional net migration highlights the importance of accounting for serial correlation in time-series models. Correcting for this statistical pattern significantly improves migration equation accuracy.
Area of Science:
- Demography
- Econometrics
- Regional Science
Background:
- Multiregional migration patterns are crucial for understanding population dynamics.
- Traditional time-series models may not fully capture the complexities of migration flows.
- Serial correlation in migration data can bias statistical estimates.
Purpose of the Study:
- To specify and estimate multiregional net-migration equations incorporating first-order serial correlation.
- To investigate the impact of the nonstochastic adding-up constraint on serial-correlation coefficients.
- To demonstrate the necessity of serial correlation correction in migration modeling.
Main Methods:
- Specification of multiregional net-migration equations.
- Estimation of time-series models with serial correlation adjustments.
- Application of the adding-up constraint to restrict serial-correlation parameters.
- Empirical analysis using Canadian provincial data (1962-1985).
Main Results:
- The adding-up constraint significantly restricts serial-correlation coefficients in multiregional migration systems.
- Serial correlation coefficients were found to be statistically significant.
- The inclusion of serial correlation correction improved the accuracy of migration estimates.
Conclusions:
- First-order serial correlation is a significant factor in multiregional net-migration time-series.
- Ignoring serial correlation can lead to inaccurate migration modeling.
- Future time-series analyses of multiregional migration should incorporate serial correlation corrections.
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