The Determinants of Fish Catch: A Quantile Regression Approach

The goal of this study is to use quantile regression (QR) to find predictors of fishers’ catch and compare it with OLS regression. The heterogeneous association across the different quantiles of the catch distribution was investigated using QR analysis. The findings reveal that the effect changes de...

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Main Author: Mary Pleños
Format: Article
Language:English
Published: Warsaw University of Life Sciences Press 2021-06-01
Series:Zeszyty Naukowe Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie. Problemy Rolnictwa Światowego
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Online Access:https://prs.sggw.edu.pl/article/view/2427
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author Mary Pleños
author_facet Mary Pleños
author_sort Mary Pleños
collection DOAJ
description The goal of this study is to use quantile regression (QR) to find predictors of fishers’ catch and compare it with OLS regression. The heterogeneous association across the different quantiles of the catch distribution was investigated using QR analysis. The findings reveal that the effect changes depending on where a fisher is in the catch distribution. In the OLS, there are several non-significant predictors that appear to be significant in quantile regression. By OLS regression, demographic variables have little effect on fishers’ catch; but, in quantile regression, marital status, fishing hours, and use of motorized boats appeared to have a relatively high impact at the top of the distribution.
format Article
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institution Kabale University
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publishDate 2021-06-01
publisher Warsaw University of Life Sciences Press
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series Zeszyty Naukowe Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie. Problemy Rolnictwa Światowego
spelling doaj-art-587dc3b6f353429aa6383be2b0ee9e672025-02-04T10:43:12ZengWarsaw University of Life Sciences PressZeszyty Naukowe Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie. Problemy Rolnictwa Światowego2081-69602544-06592021-06-0121210.22630/PRS.2021.21.2.6The Determinants of Fish Catch: A Quantile Regression ApproachMary Pleños0Visayas State University, PhilippinesThe goal of this study is to use quantile regression (QR) to find predictors of fishers’ catch and compare it with OLS regression. The heterogeneous association across the different quantiles of the catch distribution was investigated using QR analysis. The findings reveal that the effect changes depending on where a fisher is in the catch distribution. In the OLS, there are several non-significant predictors that appear to be significant in quantile regression. By OLS regression, demographic variables have little effect on fishers’ catch; but, in quantile regression, marital status, fishing hours, and use of motorized boats appeared to have a relatively high impact at the top of the distribution.https://prs.sggw.edu.pl/article/view/2427quantile regressionfisherscatch
spellingShingle Mary Pleños
The Determinants of Fish Catch: A Quantile Regression Approach
Zeszyty Naukowe Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie. Problemy Rolnictwa Światowego
quantile regression
fishers
catch
title The Determinants of Fish Catch: A Quantile Regression Approach
title_full The Determinants of Fish Catch: A Quantile Regression Approach
title_fullStr The Determinants of Fish Catch: A Quantile Regression Approach
title_full_unstemmed The Determinants of Fish Catch: A Quantile Regression Approach
title_short The Determinants of Fish Catch: A Quantile Regression Approach
title_sort determinants of fish catch a quantile regression approach
topic quantile regression
fishers
catch
url https://prs.sggw.edu.pl/article/view/2427
work_keys_str_mv AT maryplenos thedeterminantsoffishcatchaquantileregressionapproach
AT maryplenos determinantsoffishcatchaquantileregressionapproach