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Technical Change as a Stochastic Trend in a Fisheries Model

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  • Sturla Furunes Kvamsdal

Abstract

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.

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  • Sturla Furunes Kvamsdal, 2016. "Technical Change as a Stochastic Trend in a Fisheries Model," Marine Resource Economics, University of Chicago Press, vol. 31(4), pages 403-419.
    • Handle: RePEc:ucp:mresec:doi:10.1086/687931
    DOI: 10.1086/687931
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    1. Fuentes, J. Rodrigo & Morales, Marco, 2011. "On The Measurement Of Total Factor Productivity: A Latent Variable Approach," Macroeconomic Dynamics, Cambridge University Press, vol. 15(2), pages 145-159, April.
    2. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    3. Daniel V. Gordon & Rögnvaldur Hannesson, 2015. "The Norwegian Winter Herring Fishery: A Story of Technological Progress and Stock Collapse," Land Economics, University of Wisconsin Press, vol. 91(2), pages 362-385.
    4. Harvey, A C, et al, 1986. "Stochastic Trends in Dynamic Regression Models: An Application to the Employment-Output Equations," Economic Journal, Royal Economic Society, vol. 96(384), pages 975-985, December.
    5. Streibel, Mariane & Harvey, Andrew, 1993. "Estimation of simultaneous equation models with stochastic trend components," Journal of Economic Dynamics and Control, Elsevier, vol. 17(1-2), pages 263-287.
    6. Roberto Esposti, 2000. "Stochastic Technical Change and Procyclical TFP The Case of Italian Agriculture," Journal of Productivity Analysis, Springer, vol. 14(2), pages 119-141, September.
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    1. Sturla Furunes Kvamsdal, 2019. "Indexing of Technical Change in Aggregated Data," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 901-920, March.
    2. Pettersen, Ingrid Kristine & Brækkan, Eivind Hestvik & Myrland, Øystein, 2018. "Are Norwegian fishermen selling in the same market?," Journal of Commodity Markets, Elsevier, vol. 12(C), pages 9-18.
    3. Anderson, James L. & Asche, Frank & Garlock, Taryn, 2018. "Globalization and commoditization: The transformation of the seafood market," Journal of Commodity Markets, Elsevier, vol. 12(C), pages 2-8.
    4. Kvamsdal, Sturla F. & Sandal, Leif K. & Poudel, Diwakar, 2020. "Ecosystem wealth in the Barents Sea," Ecological Economics, Elsevier, vol. 171(C).
    5. Asche, Frank & Cojocaru, Andreea L. & Gaasland, Ivar & Straume, Hans-Martin, 2018. "Cod stories: Trade dynamics and duration for Norwegian cod exports," Journal of Commodity Markets, Elsevier, vol. 12(C), pages 71-79.
    6. Anna M. Birkenbach & Andreea L. Cojocaru & Frank Asche & Atle G. Guttormsen & Martin D. Smith, 2020. "Seasonal Harvest Patterns in Multispecies Fisheries," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(3), pages 631-655, March.

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