ICAR Research Data Repository for Knowledge Management
Replication For: Equation Balance in Time Series Analysis: Lessons Learned and Lessons Needed
Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)
View Archive Info| Field | Value | |
| Title |
Replication For: Equation Balance in Time Series Analysis: Lessons Learned and Lessons Needed
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| Identifier |
https://doi.org/10.7910/DVN/IITPH8
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| Creator |
Pickup, Mark
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| Publisher |
Harvard Dataverse
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| Description |
The papers in this symposium use Monte Carlo simulations to demonstrate the consequences of estimating time series models with variables that are of different orders of integration. In this summary, I do the following: very briefly outline what we learn from the papers; identify an apparent contradiction that might increase, rather than decrease, confusion around the concept of a balanced time series model; suggest a resolution; and identify a few areas of research that could further increase our understanding of how variables with different dynamics might be combined. In doing these things, I suggest there is still a lack of clarity around how a research practitioner demonstrates balance, and demonstrates what Pickup and Kellstedt (2020) call I(0) balance.
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| Subject |
Social Sciences
time series equation balance |
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| Contributor |
Pickup, Mark
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