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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MRE Foundation, Inc.
2016
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Online Access: | https://doi.org/10.1086/687931 |
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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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Open Polar |
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BioOne Online Journals |
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ftbioone |
language |
English |
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 |
op_coverage |
world |
geographic |
Lofoten |
geographic_facet |
Lofoten |
genre |
Lofoten |
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Lofoten |
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 |
op_container_end_page |
419 |
_version_ |
1800755977950068736 |