Cleaning the database
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LOMPO Aguima Aime Bernard,Burkina Faso,SPORD Thank you for acknowledging my comment, I cannot agree less that it is always enriching to bridge methodological discussions across micro and macro contexts. Particularly, I have been trying my hands on how to infuse both macro and micro datasets together in research and it has been a rewarding (and honestly, stressful) journey.
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LOMPO Aguima Aime Bernard,Burkina Faso,SPORD LIPEDE Omolola Mary,Nigeria,GPSPD This is definitely not my domain, I appreciate the concepts and how the both of you have shed light on it.
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Thank you for sharing these insightful imputation techniques. This is a valuable contribution for practitioners working with panel data in Stata. That said, it's important to emphasize that users should not only learn how to apply these techniques, but also clearly understand when and why they are appropriate, as well as the assumptions that underlie them.
Every imputation method, whether simple or advanced, alters the original dataset to some extent. While imputation is intended to mitigate the negative impacts of missing data, it also introduces new sources of uncertainty. If applied carelessly or without checking assumptions, imputation can result in biased estimates, misleading inferences, or spurious relationships in the data.
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LOMPO Aguima Aime Bernard,Burkina Faso,SPORD Very fine analysis, Bernard. I share your point of view. I don't think it's absolutely necessary, since it's based on the assumption that data trends will continue. But in the event of exogenous or endogenous shocks, this can completely reverse and skew the nature of the data. It is for this reason that the contemporary literature agrees that it is not necessary for the panel to be absolutely cylindrical in order to make estimates. Ref. Nomo et al.(2025) Amba (2024).
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Thank you, SOUMTANG BIME Valentine, Cameroon, DES-P and BANENGAI KOYAMA Torcia Chanelle,Central African Republic,MFGD, for your thoughtful response and for clarifying your stance. SOUMTANG BIME Valentine, Cameroon, DES-P, I appreciate your nuanced interpretation regarding the assumption of trend continuity and your citation of Nomo et al. (2025) and Amba (2024) as support for the relaxation of cylindrical panel requirements.
That said, while I acknowledge that perfect cylindricality is not a strict necessity as indeed confirmed in parts of the literature my concern was not about the formal necessity of a balanced panel per se, but rather about the potential for biases or misinterpretations arising from the structure of your data. Specifically, in the presence of structural breaks or policy interventions (endogenous or exogenous), an unbalanced panel can lead to time-varying sample composition, which may in turn affect the stability and comparability of your coefficients.
Again, thank you for engaging so constructively with the feedback.
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Thank you @kemtko
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LOMPO Aguima Aime Bernard,Burkina Faso,SPORD totally agree
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LOMPO Aguima Aime Bernard,Burkina Faso,SPORD LIPEDE Omolola Mary,Nigeria,GPSPD SOUMTANG BIME Valentine, Cameroon, DES-P BANENGAI KOYAMA Torcia Chanelle,Central African Republic,MFGD SYAHUKA Hilda, Uganda, DOA Thank you all for these rich contributions. As someone working with community level datasets, I’ve found both micro and macro perspectives incredibly valuable. Ensuring data completeness is especially critical when our work directly informs interventions in underserved populations. Imputation, particularly MICE, has helped us maintain the reliability of our insights while accounting for local constraints like infrastructure gaps or survey non-responsiveness. That said, I agree, whether micro or macro, it’s essential to balance methodological rigor with contextual realities, and to remain transparent about our assumptions and limitations. Great to see this exchange of approaches!
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True indeed IITUMBA Ndinelao,Namibia,PCKMD, striking a balance in methodical rigor erases gaps in a real-life context. It brings to life lived experiences, and blending facts.
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can imputed estimates be relied on?
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This overview of imputation techniques for panel data is insightful and highlights the importance of addressing missing values to preserve analysis quality. The distinction between single and multiple imputation is clearly articulated, emphasizing that while single imputation is quick, multiple imputation offers a more robust solution. This guidance will be invaluable for users working with Stata to ensure more accurate and reliable results in their analyses.
