Composite Quasi-Maximum Likelihood Estimation of Dynamic Panels with Group-Specific Heterogeneity and Spatially Dependent Errors
Bibliographic record
Abstract
This paper proposes a new method to estimate dynamic panel data models with spatially \ndependent errors that allows for known/unknown group-specific patterns of slope heterogeneity. \nAnalysis of this model is conducted in the framework of composite quasi-likelihood (CL) \nmaximization. The proposed CL estimator is robust against some misspecification of the unobserved \nindividual/group-specific fixed effects. Since our CL method is based on the idea \nof doing regressions involving common-group stochastic trends, no endogeneity problem will \narise. Therefore, unlike existing methods the proposed estimator does not require the use of \nintrumental variables nor bias correction/reduction. Clustering and estimation of the parameters \nof interest involve a large-scale non-convex mixed-integer programming problem, which \ncan then be solved via a new efficient approach developed based on DC (Difference-of-Convex \nfunctions) programming and the DCA (DC algorithm). Suppose that the number of time periods \nand the size of spatial domain grow simultaneously, asymptotic theory is derived for both \ncases where the covariates are stationary and nonstationary. An extensive Monte Carlo simulation \nis also provided to examine the finite-sample performance of the proposed estimator. \nOur method is then applied to study the long-run relationship between saving and investment \nrates. The empirical findings reconcile various empirical approaches to capital mobility in the \nliterature; and there exists substantial capital mobility in some countries while no conclusion \nabout capital mobility can be drawn in other countries. Applied economists can easily implement \nthe method by using the companion software to this paper.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".