Archive for November, 2017

Indexing of Technical Change in Aggregated Data

November 29, 2017

My latest publication is forthcoming in Computational Economics and can be accessed here: rdcu.be/yvm5. 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.
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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:
http://www.sciencedirect.com/science/article/pii/S0304380017304192. The paper is part of the ARC-Change project, and is the first in a string of papers on interconnected issues.