A dynamic programming approach for quickly estimating large network-based MEV models

Tien Mai, Emma Frejinger, Mogens Fosgerau and Fabian Bastin

Transportation Research Part B: Methodological, 2017, vol. 98, issue C, 179-197

Abstract: We propose a way to estimate a family of static Multivariate Extreme Value (MEV) models with large choice sets in short computational time. The resulting model is also straightforward and fast to use for prediction. Following Daly and Bierlaire (2006), the correlation structure is defined by a rooted, directed graph where each node without successor is an alternative. We formulate a family of MEV models as dynamic discrete choice models on graphs of correlation structures and show that the dynamic models are consistent with MEV theory and generalize the network MEV model (Daly and Bierlaire, 2006). Moreover, we show that these models can be estimated quickly using the concept of network flows and the nested fixed point algorithm (Rust, 1987). The main reason for the short computational time is that the new formulation allows to benefit from existing efficient solution algorithms for sparse linear systems of equations.

Keywords: Multivariate extreme value models; Dynamic programming; Discrete choice; Maximum likelihood estimation; Nested fixed point algorithm; Value iteration (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.trb.2016.12.017

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