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Using Halton Sequences in Random Parameters Logit Models

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  • Tong Zeng

Abstract

Quasi-random numbers that are evenly spread over the integration domain have become used as alternatives to pseudo-random numbers in maximum simulated likelihood problems to reduce computational time. In this paper, we carry out Monte Carlo experiments to explore the properties of quasi-random numbers, which are generated by the Halton sequence, in estimating the random parameters logit model. We vary the number of Halton draws, the sample size and the number of random coefficients. We show that increases in the number of Halton draws influence the efficiency of the random parameters logit model estimators only slightly. The maximum simulated likelihood estimator is consistent. We find that it is not necessary to increase the number of Halton draws when the sample size increases for this result to be evident.

Suggested Citation

  • Tong Zeng, 2016. "Using Halton Sequences in Random Parameters Logit Models," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 5(1), pages 1-4.
    • Handle: RePEc:spt:stecon:v:5:y:2016:i:1:f:5_1_4
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    1. De Salvo, Maria & Scarpa, Riccardo & Capitello, Roberta & Begalli, Diego, 2020. "Multi-country stated preferences choice analysis for fresh tomatoes," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 9(3), December.
    2. Perez Padilla, Mitzi, 2017. "Risk, time and social preferences : Evidence from large scale experiments," Other publications TiSEM 4f97c2b7-a709-4627-96bd-b, Tilburg University, School of Economics and Management.
    3. Lim, Siew & Wachenheim, Cheryl, 2022. "Predicted enrollment in alternative attribute Conservation Reserve Program contracts," Land Use Policy, Elsevier, vol. 117(C).
    4. Wen Fu & Jaeyoung Lee, 2022. "Relationship between Vehicle Safety Ratings and Drivers’ Injury Severity in the Context of Gender Disparity," IJERPH, MDPI, vol. 19(10), pages 1-14, May.

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