arXiv 22 Oct 2020 · Econometrics
arXiv:2010.11482 · PDF · DOI · OpenAlex · Extracted main text
Estimation and inference in dynamic discrete choice models often relies on approximation to lower the computational burden of dynamic programming. Unfortunately, the use of approximation can impart substantial bias in estimation and results in invalid confidence sets. We present a method for set estimation and inference that explicitly accounts for the use of approximation and is thus valid regardless of the approximation error. We show how one can account for the error from approximation at low computational cost. Our methodology allows researchers to assess the estimation error due to the use of approximation and thus more effectively manage the trade-off between bias and computational expedience. We provide simulation evidence to demonstrate the practicality of our approach.
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 | Rust, John (1987) Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher | 0.811 | 4 | 2 | 100% |
| 2 | Aguirregabiria, Victor, & Mira, Pedro (2007) Sequential Estimation of Dynamic Discrete Games | 0.737 | 3 | 2 | 100% |
| 3 | Hotz, V. Joseph, & Miller, Robert A (1993) Conditional Choice Probabilities and the Estimation of Dynamic Models | 0.644 | 2 | 2 | 100% |
| 4 | Keane, Michael P., Todd, Petra E., & Wolpin, Kenneth I (2011) The Structural Estimation of Behavioral Models: Discrete Choice Dynamic Programming Methods and Applications, Chapter 4, Handboo… | 0.644 | 2 | 2 | 100% |
| 5 | Su, Che-Lin, & Judd, Kenneth L (2012) Constrained Optimization Approaches to Estimation of Structural Models | 0.644 | 2 | 2 | 100% |
| 6 | Keane, Michael P., & Wolpin, Kenneth I (1992) The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence | 0.511 | 2 | 1 | 100% |
| 7 | Abbring, & ystein Daljord (2020) Identifying the Discount Factor in Dynamic Discrete Choice Models | 0.405 | 1 | 1 | 100% |
| 8 | Aguirregabiria, Victor, & Mira, Pedro (2002) Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models | 0.405 | 1 | 1 | 100% |
| 9 | Arcidiacono, Peter, Bayer, Patrick, Bugni, Federico A., & James, Jon… (2013) Approximating High-dimensional Dynamic Models: Sieve Value Function Iteration | 0.405 | 1 | 1 | 100% |
| 10 | Bajari, Patrick, Benkard, C. Lanier, & Levin, Jonathan (2007) Estimating Dynamic Models of Imperfect Competition | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 15 scored citations.