Spatial Weighting Matrix Selection in Spatial Lag Econometric Model

This paper investigates the choice of spatial weighting matrix in a spatial lag model framework. In the empirical literature the choice of spatial weighting matrix has been characterized by a great deal of arbitrariness. The number of possible spatial weighting matrices is large, which until recently was considered to prevent investigation into the appropriateness of the empirical choices. Recently Kostov (2010) proposed a new approach that transforms the problem into an equivalent variable selection problem. This article expands the latter transformation approach into a two-step selection procedure. The proposed approach aims at reducing the arbitrariness in the selection of spatial weighting matrix in spatial econometrics. This allows for a wide range of variable selection methods to be applied to the high dimensional problem of selection of spatial weighting matrix. The suggested approach consists of a screening step that reduces the number of candidate spatial weighting matrices followed by an estimation step selecting the final model. An empirical application of the proposed methodology is presented. In the latter a range of different combinations of screening and estimation methods are employed and found to produce similar results. The proposed methodology is shown to be able to approximate and provide indications to what the ‘true’ spatial weighting matrix could be even when it is not amongst the considered alternatives. The similarity in results obtained using different methods suggests that their relative computational costs could be primary reasons for their choice. Some further extensions and applications are also discussed

Choosing the Right Spatial Weighting Matrix in a Quantile Regression Model

This paper proposes computationally tractable methods for selecting the appropriate spatial weighting matrix in the context of a spatial quantile regression model. This selection is a notoriously difficult problem even in linear spatial models and is even more difficult in a quantile regression setup. The proposal is illustrated by an empirical example and manages to produce tractable models. One important feature of the proposed methodology is that by allowing different degrees and forms of spatial dependence across quantiles it further relaxes the usual quantile restriction attributable to the linear quantile regression. In this way we can obtain a more robust, with regard to potential functional misspecification, model, but nevertheless preserve the parametric rate of convergence and the established inferential apparatus associated with the linear quantile regression approach

A Quantile Regression Analysis of the Effect of Farmers’ Attitudes and Perceptions on Market Participation

The objective of this study is to investigate the subjective determinants of farmers’ participation in output markets in five EU New Member States (NMS) characterised by large semi-subsistence sectors. It employs quantile regression to model market participation reflecting the heterogeneity amongst farmers. The study also uses the Bayesian adaptive lasso to simultaneously select important covariates and estimate the corresponding quantile regression models. The empirical results show that only two variables affect all quantiles, while their effect varies across quantiles. Some of the remaining variables affect the share of output sold at the lower quantiles (i.e. for subsistence- and semi-subsistence-oriented farmers) only, whereas other variables are only significant at the upper quantiles (i.e. for more commercially oriented farms). Advisory services, and particularly agricultural business advice, and information and advice on markets and prices can facilitate the market participation of subsistence-oriented farms

Modelling the effects of subsistence on Bulgarian agricultural performance

No abstract is available for this item

Regime-switching Vector Error Correction Model (VECM) analysis of UK meat consumption

The asymptotic distributions of cointegration tests are approximated using the Gamma distribution. The tests considered are for the I(1), the conditional I(1), as well as the I(2) model. Formulae for the parameters of the Gamma distributions are derived from response surfaces. The resulting approximation is flexible, easy to implement and more accurate than the standard tables previously published

Transition, agriculture and the vanishing market

No abstract is available for this item