Full-Information Selection Bias Correction for Discrete Choice Models with Observation-Conditional Regressors

Chen, Y. Allen; Haynie, Alan C.; Anderson, Christopher M.

doi:10.1086/719794

i

Full-Information Selection Bias Correction for Discrete Choice Models with Observation-Conditional Regressors

2023
By Chen, Y. Allen ; Haynie, Alan C. ; Anderson, Christopher M.

Details

Journal Title:

Journal of the Association of Environmental and Resource Economists
Personal Author:

Chen, Y. Allen ; Haynie, Alan C. ; Anderson, Christopher M.
NOAA Program & Office:

NMFS (National Marine Fisheries Service) ; AFSC (Alaska Fisheries Science Center) ; NWFSC (Northwest Fisheries Science Center)
Description:

We examine self-selection in polychotomous choice models that construct attribute values for each alternative conditioned on observed choices. Using observations made only when the alternative was chosen ignores private information which was a basis for the decision, biasing resulting estimates. We suggest a full-information maximum likelihood procedure that performs well at the extremes of the choice set in our sample, and use an “identification at infinity” weighting to identify levels. We apply the model to understanding fishing location choice in the economically significant Bering Sea pollock fishery, where expected catches at each location are constructed from harvests observed when that location is chosen.
Keywords:

Fisheries Pollock Polychotomous choice models
Source:

Journal of the Association of Environmental and Resource Economists 2023 10:1, 231-261
DOI:

https://doi.org/10.1086/719794
Document Type:

Journal Article
Place as Subject:

Bering Sea
Rights Information:

Accepted Manuscript
Compliance:

Submitted
Main Document Checksum:

urn:sha256:02913eff6a895cde716905e5399623d202d7688be263a17a88e6de9c66504f0b
Download URL:

https://repository.library.noaa.gov/view/noaa/47897/noaa_47897_DS1.pdf
File Type:

[PDF - 4.52 MB ]

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