Inference in Dynamic Discrete Choice Problems under Local Misspecification

@article{Bugni2016InferenceID,
  title={Inference in Dynamic Discrete Choice Problems under Local Misspecification},
  author={F. Bugni and T. Ura},
  journal={arXiv: Methodology},
  year={2016}
}
  • F. Bugni, T. Ura
  • Published 2016
  • Mathematics, Economics
  • arXiv: Methodology
Dynamic discrete choice models are typically estimated using heavily parametrized econometric frameworks, making them susceptible to model misspecification. This paper investigates how misspecification can affect the results of inference in these models. For tractability reasons, we consider a local misspecification framework in which specification errors are assumed to vanish with the sample size. However, we impose no restrictions on the rate at which these errors vanish. We consider a… Expand
5 Citations

Tables from this paper

Minimizing Sensitivity to Model Misspecification
  • 28
  • PDF
Sensitivity Analysis using Approximate Moment Condition Models
  • 35
  • Highly Influenced
  • PDF
9 O ct 2 01 8 Minimizing Sensitivity to Model Misspecification ∗
  • PDF

References

SHOWING 1-10 OF 31 REFERENCES
Swapping the Nested Fixed-Point Algorithm: a Class of Estimators for Discrete Markov Decision Models
  • 379
  • Highly Influential
  • PDF
A Simulation Estimator for Dynamic Models of Discrete Choice
  • 305
  • PDF
Large sample estimation and hypothesis testing
  • 2,955
  • Highly Influential
  • PDF
Maximum Likelihood Estimation of Misspecified Models
  • 4,385
  • PDF
Conditional Choice Probability Estimation of Dynamic Discrete Choice Models With Unobserved Heterogeneity
  • 322
  • Highly Influential
  • PDF
...
1
2
3
4
...