Posts Tagged ‘ARC-Change’

Indexing of Technical Change in Aggregated Data

November 29, 2017

My latest publication is forthcoming in Computational Economics and can be accessed here: The abstract:

The Baltagi–Grifn general index of technical change for panel data has earlier been applied to aggregated data via the use of period dummy variables. Period dummies force modeling into estimation of the latent level of technology through choice of dummy structure. Period dummies also do not exploit the full information set because the order of observations within periods is ignored. To resolve these problems, I suggest estimating the empirical equation for all possible structures of the dummy variables. The average over the different dummy coefcient estimates provides an index of technical change. More generally, the method estimates a general, model-free trend in linear models. I demonstrate the method with both simulated and real data.
The paper is essentially a real simple idea that works well in many situations and solves a difficult problem. I came up with this idea while working on this paper, and can as such be viewed as a spin-off from that. I gather that methodological papers seldom are spin-offs from empirical apply-this-different-method-to-this-data papers, but that is what happened here. I am quite proud of this paper and regard it as my most innovative and important contribution so far. What surprises me most is that, from what I can tell, no-one seems to have thought of this simple idea before.

A bridge between continuous and discrete-time bioeconomic models: Seasonality in fisheries

November 2, 2017

I recently published a paper together with good colleagues in Spain and Norway. The paper is published in the journal Ecological Modelling and is on the problem of setting up corresponding fisheries economics models in continuous and discrete time. Here is the abstract:

We develop a discretization method to construct a discrete finite-time bioeconomic model, corresponding to bioeconomic models with continuous-time growth function, but allowing the analysis of seasonality in fisheries. The discretization method consists of three steps: first, we estimate a proper growth function for the continuous-time model with the Ensemble Kalman Filter. Second, we use the Runge-Kutta method to discretize the growth function. Third, we use the Bellman approach to analyze the optimal management of seasonal fisheries in a discrete-time setting. We analyze both the case of quarterly harvest and the case of monthly harvest, and we compare these cases with the case of annual harvest. We find that seasonal harvesting is a win–win optimal solution that provides higher harvest, higher optimal steady state equilibrium, and higher economic value than annual harvesting. We also demonstrate that the discretization method overcomes the errors and preserves the strengths of both continuous and discrete-time bioeconomic models.

For some time, the paper is freely downloadable here: The paper is part of the ARC-Change project, and is the first in a string of papers on interconnected issues.

ARC-Change web page

October 26, 2016

Earlier this year, I posted on a new research project, ARC-Change, on climate change in the Arctic and its consequences for governance and resource industries. The project is now underway, and recently the project web page went online (click logo below to go to the site).


Up the ante on bioeconomic submodels of marine food webs

September 20, 2016

My new paper is available for free download for 50 days (until November 9, 2016). The paper is published in the journal Ecological Economics and discusses modeling of marine food webs such that economic analysis is viable. At the core of our approach lie the ensemble Kalman filter, something I have used earlier. In this new application, we go further in reducing model parameter dimensionality and move beyond the filtering routine to estimate certain structure parameters. We also apply a data transformation that deal with previously overlooked endogeneity in stock level data. We use all this to estimate a model of the largest pelagic fish stocks in the Norwegian Sea. The abstract:

eeWhile economists have discussed ecosystem-based fisheries management and similar concepts, little attention has been devoted to purposeful modeling of food webs. Models of ecosystems or food webs that make economic analysis viable should capture as much as possible of system structure and dynamics while balancing biological and ecological detail against dimensionality and model complexity. Relevant models need strong, empirical content, but data availability may inhibit modeling efforts. Models are bound to be nonlinear, and model and observational uncertainty should be included. To deal with these issues and to improve modeling of ecosystems or food webs for use in ecosystem-based fisheries management analysis, we suggest the data assimilation method ensemble Kalman filtering. To illustrate the method, we model the dynamics of the main, pelagic species in the Norwegian Sea. In order to reduce parameter dimensionality, the species are modeled to rely on a common carrying capacity. We also take further methodological steps to deal with a still high number of parameters. Our best model captures much of the observed dynamics in the fish stocks while the estimated model error is moderate.

The paper is part of the EINSAM project.

New Research Project on Climate Change and the Arctic

April 5, 2016

I will take part in a new research project on climate change in the Arctic. The project carries the long name “ARCtic Marine Resources under Climate Change: Environmental, Socio-Economic Perspectives and Governance,” or ARC-Change for short. From the project description:

As we enter the Anthropocene, climate change moves the parameters we live within. First and foremost, these changes will take place in Arctic regions, regions that already are subject to substantial, large scale natural variability and where higher temperatures and retreating sea ice will redefine boundaries of biological life, ecological structure, and commercial and social opportunities. Complex interactions and causal mechanisms exist from the physical impact, in terms of temperature, ocean currents, via biological and ecological adaptations, in terms of habitat expansion, growth conditions, species interactions, via social and business enterprise, in terms of new fishing areas, trade routes, mineral wealth, to governance implications, in terms of pressure on existing agreements on fishing, surveillance, and commercial activity. A cross-sectorial and cross-disciplinary perspective is needed to investigate and understand climate change impacts. ARC-Change will study some of these interlinkages, from the physical and biological to the economical and governmental, while brining together expertise from an array of disciplines and institutions.

More in the press release.

Update: The ARC-Change webpage.