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Solving Dynamic Discrete Choice Models: Integrated or Expected Value Function?

Patrick Kofod Mogensen

arXiv 11 Jan 2018 · Econometrics · 1 citations (OpenAlex)

arXiv:1801.03978 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Dynamic Discrete Choice Models (DDCMs) are important in the structural estimation literature. Since the structural errors are practically always continuous and unbounded in nature, researchers often use the expected value function. The idea to solve for the expected value function made solution more practical and estimation feasible. However, as we show in this paper, the expected value function is impractical compared to an alternative: the integrated (ex ante) value function. We provide brief descriptions of the inefficacy of the former, and benchmarks on actual problems with varying cardinality of the state space and number of decisions. Though the two approaches solve the same problem in theory, the benchmarks support the claim that the integrated value function is preferred in practice.

Citation extraction

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1John Rust (1987) Optimal replacement of gmc bus engines: An empirical model of harold zurcher0.9285480%
2Rust John (1988) Maximum likelihood estimation of discrete control processes0.81142100%
3Che-Lin Su and Kenneth L. Judd (2012) Constrained optimization approaches to estimation of structural models0.64422100%
4Victor Aguirregabiria and Pedro Mira (2010) Dynamic discrete choice structural models: A survey0.58531100%
5Victor Aguirregabiria and Pedro Mira (2002) Swapping the nested fixed point algorithm: A class of estimators for discrete markov decision models0.51121100%
6Andriy Norets (2010) Continuity and differentiability of expected value functions in dynamic discrete choice models0.51121100%
7John Rust (2000) Nested fixed point algorithm documentation manual0.40511100%
8Igal Hendel and Aviv Nevo (2006) Measuring the implications of sales and consumer inventory behavior0.40511100%
9John Rust (1994) Structural estimation of markov decision processes0.40511100%
10Fedor Iskhakov, Jinhyuk Lee, John Rust, Bertel Schjerning, and Kyoun… (2016) Comment on “constrained optimization approaches to estimation of structural models”0.000110%

Showing the top 10 of 10 scored citations.