arXiv 25 Oct 2025 · Econometrics
arXiv:2510.22347 · PDF · DOI · OpenAlex · Extracted main text
Estimation and counterfactual analysis in dynamic structural models rely on assumptions about the dynamic process of latent variables, which may be misspecified. We propose a framework to quantify the sensitivity of scalar parameters of interest (e.g., welfare, elasticity) to such assumptions. We derive bounds on the scalar parameter by perturbing a reference dynamic process, while imposing a stationarity condition for time-homogeneous models or a Markovian condition for time-inhomogeneous models. The bounds are the solutions to optimization problems, for which we derive a computationally tractable dual formulation. We establish consistency, convergence rate, and asymptotic distribution for the estimator of the bounds. We demonstrate the approach with two applications: an infinite-horizon dynamic demand model for new cars in the United Kingdom, Germany, and France, and a finite-horizon dynamic labor supply model for taxi drivers in New York City. In the car application, perturbed price elasticities deviate by at most 15.24% from the reference elasticities, while perturbed estimates of consumer surplus from an additional $3,000 electric vehicle subsidy vary by up to 102.75%. In the labor supply application, the perturbed Frisch labor supply elasticity deviates by at most 76.83% for weekday drivers and 42.84% for weekend drivers.
appendix boundary found by appendix_command · 72% of the source is main text. Read the extracted text to check this.
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 | Christensen, Timothy and Connault, Benjamin (2023) Counterfactual sensitivity and robustness | 1.000 | 9 | 3 | 100% |
| 2 | Kalouptsidi, Myrto and Scott, Paul T and Souza-Rodrigues, Eduardo (2021) Linear IV regression estimators for structural dynamic discrete choice models | 1.000 | 6 | 3 | 100% |
| 3 | Schennach, Susanne M (2014) Entropic latent variable integration via simulation | 1.000 | 5 | 3 | 100% |
| 4 | Eckstein, Stephan and Nutz, Marcel (2024) Convergence rates for regularized optimal transport via quantization | 0.874 | 6 | 3 | 67% |
| 5 | Schiraldi, Pasquale (2011) Automobile replacement: a dynamic structural approach | 0.874 | 6 | 2 | 100% |
| 6 | Gowrisankaran, Gautam and Rysman, Marc (2012) Dynamics of consumer demand for new durable goods | 0.874 | 5 | 2 | 100% |
| 7 | Eckstein, Stephan and Nutz, Marcel (2022) Quantitative Stability of Regularized Optimal Transport and Convergence of Sinkhorn's Algorithm | 0.843 | 3 | 3 | 100% |
| 8 | Hotz, V Joseph and Miller, Robert A (1993) Conditional choice probabilities and the estimation of dynamic models | 0.843 | 3 | 3 | 100% |
| 9 | Rust, John (1987) Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher | 0.737 | 3 | 3 | 67% |
| 10 | Arcidiacono, Peter and Miller, Robert A (2011) Conditional choice probability estimation of dynamic discrete choice models with unobserved heterogeneity | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 104 scored citations.