Bayesian Variable Selection for Ultrahigh-dimensional Sparse Linear Models

We propose a Bayesian variable selection procedure for ultrahigh-dimensional linear regression models. The number of regressors involved in regression, $p_n$, is allowed to grow exponentially with $n$. Assuming the true model to be sparse, in the sense that only a small number of regressors contribute to this model, we propose a set of priors suitable for this regime. The model selection procedure based on the proposed set of priors is shown to be variable selection consistent when all the $2^{p_n}$ models are considered. In the ultrahigh-dimensional setting, selection of the true model among all the $2^{p_n}$ possible ones involves prohibitive computation. To cope with this, we present a two-step model selection algorithm based on screening and Gibbs sampling. The first step of screening discards a large set of unimportant covariates, and retains a smaller set containing all the active covariates with probability tending to one. In the next step, we search for the best model among the covariates obtained in the screening step. This procedure is computationally quite fast, simple and intuitive. We demonstrate competitive performance of the proposed algorithm for a variety of simulated and real data sets when compared with several frequentist, as well as Bayesian methods

Relationship-Specificity, Spatial Clustering and Production to Order Choice

We study the determinants of the firm-level choice to produce following an order placed by a downstream firm (production to order) or to produce in advance. We rationalize this choice through a simple theoretical model and apply it to a firm-level empirical analysis. Relying on a large dataset of Italian manufacturing firms, we show that two main variables affect this choice: the extent of spatial clustering of the industry, and the degree of product complexity and relationship-specificity of the goods that are traded. The sign of the impact of clustering on the choice of producing to order crucially depends on product complexity. If product complexity is high, production to order prevails when firms are clustered together. On the contrary, clustering is associated to production in advance for sectors where goods are standardized.

Supplier-Buyer Proximity and Production to Order Choice

We study the determinants of the firm-level choice to produce following an order placed by a downstream firm (production to order) or to produce in advance. We rationalize this choice through a simple theoretical model and apply it to a firm-level empirical analysis. Relying on a large panel of Italian manufacturing firms, we show that two main variables affect this choice: the distance between the supplier and the buyer and the degree of product differentiation in downstream industries where products are sold. The impact of proximity on the choice of producing to order crucially depends on the degree of product differentiation in downstream markets. We find that, in industries where average product differentiation is high, production to order prevails if the supplier is located close to the buyer. On the contrary, proximity is associated to production in advance in homogeneous sectors. We also find that, if suppliers are located in a different country from that of the buyers, they will tend to produce to order if product differentiation in downstream industries is low, and produce in advance if product differentiation is high. We also narrow the scope of our analysis to analyze the determinants of production to order originating from the same province where the supplier is located.Industrial Districts; Networks; Production to Order; Relationship-Specific Investments

Explaining the Size Distribution of Plants: An Approach Based on Civic Capital

We show that the distribution of plant size within narrowly defined industries is affected by the variation in the stock of civic capital that occurs at the provincial level. Data on plant size come from the 2001 Italian Census of Manufacturing and Services. Civic capital turns out to have a positive effect on both the average and the standard deviation of the plant size distribution. This effect is stronger in labor-intensive industries. The potential endogeneity of current civic capital is addressed by instrumenting it with historical variables. Our interpretation for these results is that civic capital is associated with reduced opportunistic behavior, which improves intra-firm cooperation and hampers the incidence of principal-agent problems, thus allowing plants to operate on a larger scale.