Despite attempts to get around the Jacobian in fitting spatial econometric models by using GMM and other approximations, it remains a central problem for maximum likelihood estimation. In principle, and for smaller data sets, the use of the eigenvalues of the spatial weights matrix provides a...

This paper studies extensive form games with perfect information and simultaneous moves, henceforth called games with public information. On this class, we prove that all communication equilibrium payoffs can be obtained without mediator by cheap-talk procedures. The result encompasses repeated...

Multiple testing analysis, based on clustering methodologies, is usually applied in Microarray Data Analysis for comparisons between pair of groups. In this paper, we generalize this methodology to deal with multiple comparisons among more than two groups obtained from microarray expressions of...

The recent financial crisis has highlighted once more that interconnectedness in the financial system is a major source of systemic risk. I suggest a practical way to levy regulatory capital charges based on the degree of interconnectedness among financial institutions. Namely, the charges are...

This paper presents empirical evidence on the impact of competition on firm productivity. Using firm-level observations from the World Bank Enterprise Survey database, we find a positive and robust causal relationship between our proxies for competition and our measures of productivity. We also...

Korea’s statistics are generally of a high quality, and reflect a significant improvement since the last Report on the Observance of Standards and Codes. Korea’s adherence to internationally accepted statistical practices requires cross-cutting recommendations, and the development...

This paper studies generic properties of Markov perfect equilibria in dynamic stochastic games. We show that almost all dynamic stochastic games have a finite number of locally isolated Markov perfect equilibria. These equilibria are essential and strongly stable. Moreover, they all admit...

In this paper we propose a general framework to characterize and solve the optimization problems underlying a large class of sparsity based regularization algorithms. More precisely, we study the minimization of learning functionals that are sums of a differentiable data term and a convex non...