Three Essays on Identification and Estimation using Sample Combination in a Missing Data Context
Bibliographic record
Abstract
This thesis is concerned with identification and estimation in two different contexts of missing data.These data settings and their associated empirical challenges were faced during the analysis of micro survey data from the Bank of Canada on payment behaviors.However, the methodological approaches adopted or developed here are of wider interest and applicability.In both situations, identification is achieved by employing data combination strategies.The first chapter uses panel data to understand the impact of retail payment innovations on cash usage while accounting for unobserved heterogeneity.The challenging feature of the data pertains to high rates (about 50 percent) of non-ignorable attrition.This missing data problem is addressed by the use of refreshment samples, which allow one to correct for potential attrition bias due to general forms of attrition instead of relying on rather restrictive assumptions.The methodological contribution is to provide identification of a three-period attrition probability function, and to discuss how to control simultaneously for non-ignorable attrition and item nonresponse.The following chapters deal with the common missing data case in which the variables of interest to a research question are not all available in one single data set, and data combination is required.In contrast with most sample combination methods I propose, in the second chapter, an identification strategy that does not rely on either units or variables in common across the samples to be combined.Rather, I exploit the availability of a third sample where an aggregate distribution of the variables i My first words of acknowledgements go to my thesis co-supervisors, Kim P. Huynh and Marcel Voia.There would be much more to say, but thank you for believing in me from the start and for guiding me along the way.To my colleague and co-author Heng Chen, thanks for opening up the world of econometric identification to me.I've learned so much from working with you.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".