This is an electronic version of the following article Kostov, P. (2009) A spatial quantile regression hedonic model of agricultural land prices, Spatial Economic Analysis, 4(1), 53-72. The final definite published version is available online at: http://dx.doi.org/10.1080/17421770802625957. 1 A spatial quantile regression hedonic model of agricultural land prices Philip Kostov University of Central Lancashire AbstractLand price studies typically employ hedonic analysis to identify the impact of land characteristics on price. Owing to the spatial fixity of land however, the question of possible spatial dependence in agricultural land prices arises. The presence of spatial dependence in agricultural land prices can have serious consequences for the hedonic model analysis. Ignoring spatial autocorrelation can lead to biased estimates in land price hedonic models. We propose using a flexible quantile regression based estimation of the spatial lag hedonic model allowing for varying effects of the characteristics and more importantly varying degrees of spatial autocorrelation. Applying this approach to a sample of agricultural land sales in Northern Ireland we find that the market effectively consists of two relatively separate segments. The larger of these two segments conforms to the conventional hedonic model with no spatial lag dependence, while the smaller much thinner market segment exhibit considerable spatial lag dependence.

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Kostov, Phillip (2010) Model boosting for spatial weighting matrix selection in spatial lag models. Environment and Planning B: Planning and Design, 37 (3). pp. 533 549. ISSN 0265 8135 It is advisable to refer to the publisher's version if you intend to cite from the work. [Kostov, 2010]. This is a postprint of a research article. The definitive, peer-reviewed and edited version of this article is published in Environment and Planning B: Planning and Design, volume 37, issue 3, pages 533-549, 2010, http://dx.doi.org/10.1068/b35137 1 Model boosting for spatial weighting matrix selection in spatial lag models Philip Kostov Lancashire Business School University of Central Lancashire AbstractThe spatial lag specification is often used in spatial econometrics. The choice of an appropriate spatial weighting matrix is an important outstanding methodological problem in the quantitative spatial dependence literature. This paper proposes applying a componentwise model boosting algorithm to deal with the issue of the choice of a spatial weighting matrix amongst a predetermined set of alternatives. The resulting procedure is computationally simple and easy to implement. We present an empirical application of the proposed methodology. Some possible extensions to a more general setting are discussed.

This paper discusses some beneficial effects of subsistence agriculture with emphasis on transition countries. Micro‐economic models of subsistence agriculture are reviewed and a two‐stage decision model, combining risk aversion and transaction costs explanations for subsistence is developed. The role of subsistence agriculture is addressed in a static comparison to a purely commercial agriculture. We argue that subsistence can play a stabilising role and have beneficial impacts on the agricultural sector when the resources it employs are unwanted by the commercial sector. The exact conditions under which the latter is true are analysed in a static general equilibrium framework. Employing the concept of the subsistence level of consumption, the paper demonstrates that these static effects can be valid in a dynamic perspective, provided additional conditions are met. Policy recommendations with regard to agricultural commercialisation are presented. These explicitly rely upon assumptions about the orientation of subsistence farmers. The lack of current research into this important behavioural feature of farmers in transition countries requires urgent action. There is urgent need for more research into the motivation, objectives and behaviour of subsistence farmers in rural economies of countries in transition.

Theoretical arguments suggest that capital structure will adjust to the dynamics of the corporate governance environment. In line with this prediction, we examine the impact of board characteristics on capital structure dynamics and the speed of adjustment. Using 2690 firm-year observations for 2009-2018, we find that firms in a stakeholder-oriented corporate governance environment adjust their leverage faster than those in a shareholder-oriented environment. We also find that corporate board characteristics influence firms' capital structure and speed of adjustment towards target leverage. Our findings are robust to alternative measures of leverage and endogeneity. The overall evidence supports the relevance of the corporate board's composition in both shareholder-oriented and stakeholder-oriented corporate governanc (CG) environments. We conclude that board composition mitigates agency conflict.