Technical Change as a Stochastic Trend in a Fisheries Model

Technical change is generally seen as a major source of growth, but usually cannot be observed directly and measurement can be difficult. With only aggregate data, measurement puts further demands on the empirical strategy. Structural time series models and the state-space form are well suited for u...

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Published in:Marine Resource Economics
Main Author: Sturla Furunes Kvamsdal
Format: Text
Language:English
Published: MRE Foundation, Inc. 2016
Subjects:
Online Access:https://doi.org/10.1086/687931
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spelling ftbioone:10.1086/687931 2024-06-02T08:10:09+00:00 Technical Change as a Stochastic Trend in a Fisheries Model Sturla Furunes Kvamsdal Sturla Furunes Kvamsdal world 2016-07-15 text/HTML https://doi.org/10.1086/687931 en eng MRE Foundation, Inc. doi:10.1086/687931 All rights reserved. https://doi.org/10.1086/687931 Text 2016 ftbioone https://doi.org/10.1086/687931 2024-05-07T00:51:43Z Technical change is generally seen as a major source of growth, but usually cannot be observed directly and measurement can be difficult. With only aggregate data, measurement puts further demands on the empirical strategy. Structural time series models and the state-space form are well suited for unobserved phenomena, such as technical change. In fisheries, technical advance often contributes to increased fishing pressure, and improved productivity measures are important for managers concerned with efficiency or conservation. I apply a structural time series model with a stochastic trend to measure technical change in a Cobb-Douglas production function, considering both single equation and multivariate models. Results from the Norwegian Lofoten cod fishery show that the approach has both methodological and empirical advantages when compared with results from the general index approach, which has been applied in the literature.JEL Codes: C22, O39, Q22. Text Lofoten BioOne Online Journals Lofoten Marine Resource Economics 31 4 403 419
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description Technical change is generally seen as a major source of growth, but usually cannot be observed directly and measurement can be difficult. With only aggregate data, measurement puts further demands on the empirical strategy. Structural time series models and the state-space form are well suited for unobserved phenomena, such as technical change. In fisheries, technical advance often contributes to increased fishing pressure, and improved productivity measures are important for managers concerned with efficiency or conservation. I apply a structural time series model with a stochastic trend to measure technical change in a Cobb-Douglas production function, considering both single equation and multivariate models. Results from the Norwegian Lofoten cod fishery show that the approach has both methodological and empirical advantages when compared with results from the general index approach, which has been applied in the literature.JEL Codes: C22, O39, Q22.
author2 Sturla Furunes Kvamsdal
format Text
author Sturla Furunes Kvamsdal
spellingShingle Sturla Furunes Kvamsdal
Technical Change as a Stochastic Trend in a Fisheries Model
author_facet Sturla Furunes Kvamsdal
author_sort Sturla Furunes Kvamsdal
title Technical Change as a Stochastic Trend in a Fisheries Model
title_short Technical Change as a Stochastic Trend in a Fisheries Model
title_full Technical Change as a Stochastic Trend in a Fisheries Model
title_fullStr Technical Change as a Stochastic Trend in a Fisheries Model
title_full_unstemmed Technical Change as a Stochastic Trend in a Fisheries Model
title_sort technical change as a stochastic trend in a fisheries model
publisher MRE Foundation, Inc.
publishDate 2016
url https://doi.org/10.1086/687931
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op_source https://doi.org/10.1086/687931
op_relation doi:10.1086/687931
op_rights All rights reserved.
op_doi https://doi.org/10.1086/687931
container_title Marine Resource Economics
container_volume 31
container_issue 4
container_start_page 403
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