arXiv 3 Jun 2026 · Econometrics
arXiv:2606.04356 · PDF · DOI · OpenAlex · Extracted main text
This study examines sequential algorithms with the Zero Jacobian Property (ZJP) for estimating structural models subject to equilibrium constraints. For the Maximum Likelihood Estimation (MLE) and the Generalized Method of Moments (GMM), the current study shows that these algorithms attains fast (near-quadratic) local convergence in large samples to the solution of the constrained optimization problem. If consistent initial estimates of the parameters are available, the algorithms yield an asymptotically efficient estimator even after one iteration. It then proposes a novel algorithm called Sequential Linearly Constrained (SLC) algorithm, which is applicable to a broader class of structural models than existing methods. A key advantage of the SLC algorithm is that it can be implemented without explicitly computing the Jacobian of the equilibrium constraints and can be multiple times faster than the Nested Fixed Point (NFXP) approach. The current study illustrates its performance through two numerical experiments: a dynamic discrete game with time-varying unobserved heterogeneity and a dynamic demand model.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Su, Che-Lin and Judd, Kenneth L (2012) Constrained optimization approaches to estimation of structural models | 1.000 | 5 | 3 | 100% |
| 2 | Aguirregabiria, Victor and Mira, Pedro (2002) Swapping the nested fixed point algorithm: A class of estimators for discrete Markov decision models | 0.941 | 6 | 4 | 83% |
| 3 | Sun, Yutec and Ishihara, Masakazu (2019) A computationally efficient fixed point approach to dynamic structural demand estimation | 0.928 | 5 | 3 | 80% |
| 4 | Dearing, Adam and Blevins, Jason R (2025) Efficient and convergent sequential pseudo-likelihood estimation of dynamic discrete games | 0.901 | 26 | 8 | 73% |
| 5 | Lee, Jinhyuk and Seo, Kyoungwon (2015) A computationally fast estimator for random coefficients logit demand models using aggregate data | 0.874 | 6 | 5 | 67% |
| 6 | Aguirregabiria, Victor and Marcoux, Mathieu (2021) Imposing equilibrium restrictions in the estimation of dynamic discrete games | 0.874 | 6 | 4 | 67% |
| 7 | Fukasawa, Takeshi (2024) Fast and simple inner-loop algorithms of static/dynamic BLP estimations self | 0.860 | 11 | 4 | 64% |
| 8 | Aguirregabiria, Victor and Mira, Pedro (2007) Sequential estimation of dynamic discrete games | 0.843 | 4 | 3 | 75% |
| 9 | Egesdal, Michael and Lai, Zhenyu and Su, Che-Lin (2015) Estimating dynamic discrete-choice games of incomplete information | 0.811 | 4 | 2 | 100% |
| 10 | Sawadogo, Sidi (2025) Efficient and Debiased Estimation of Dynamic Discrete Choice Models | 0.737 | 5 | 3 | 40% |
Showing the top 10 of 54 scored citations.