The Determinants of Fish Catch: A Quantile Regression Approach
Zeszyty Naukowe SGGW w Warszawie - Problemy Rolnictwa Światowego, 2021, vol.21(36), nr 2, s. 15-21
Słowa kluczowe
quantile regression fishers catch
Key words
quantile regression fishers catch
JEL Classification
Streszczenie
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.
Abstract
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.