Inter-industry trade and heterogeneous firms: Country size matters

This study investigates how industries with different patterns of firm heterogeneity are distributed across countries by developing a three-sector general-equilibrium model. There are two manufacturing industries in our setting: one in which firm productivity is homogeneous and the other in which it is heterogeneous. The higher degree of firm heterogeneity in the latter reflects the larger difference in firm heterogeneity between industries. We show that the larger country is more specialized in the industry with heterogeneous (homogeneous) firms when trade costs are low (high) and that an increase in the inter-industry difference in firm heterogeneity fosters the larger country's degree of specialization in the industry with heterogeneous firms. We also disclose the trade patterns across countries and show how they respond to trade liberalization. Moreover, wages are found to be higher in the larger country, with an increase in the inter-industry difference in firm heterogeneity enlarging the wage inequality across countries

A Bayesian Approach to Control Loop Performance Diagnosis Incorporating Background Knowledge of Response Information

To isolate the problem source degrading the control loop performance, this work focuses on how to incorporate background knowledge into Bayesian inference. In an effort to reduce dependence on the amount of historical data available, we consider a general kind of background knowledge which appears in many applications. The knowledge, known as response information, is about what faults can possibly affect each of the monitors. We show how this knowledge can be translated to constraints on the underlying probability distributions and introduced in the Bayesian diagnosis. In this way, the dimensionality of the observation space is reduced and thus the diagnosis can be more reliable. Furthermore, for the judgments to be consistent, the set of posterior probabilities of each possible abnormality that are computed from different observation subspaces is synthesized to obtain the partially ordered posteriors. The eigenvalue formulation is used on the pairwise comparison matrix. The proposed approach is applied to a diagnosis problem on an oil sand solids handling system, where it is shown how the combination of background knowledge and data enhances the control performance diagnosis even when the abnormality data are sparse in the historical database

Learnability of Gaussians with flexible variances

Copyright © 2007 Yiming Ying and Ding-Xuan ZhouGaussian kernels with flexible variances provide a rich family of Mercer kernels for learning algorithms. We show that the union of the unit balls of reproducing kernel Hilbert spaces generated by Gaussian kernels with fexible variances is a uniform Glivenko-Cantelli (uGC) class. This result confirms a conjecture concerning learnability of Gaussian kernels and verifies the uniform convergence of many learning algorithms involving Gaussians with changing variances. Rademacher averages and empirical covering numbers are used to estimate sample errors of multi-kernel regularization schemes associated with general loss functions. It is then shown that the regularization error associated with the least square loss and the Gaussian kernels can be greatly improved when °exible variances are allowed. Finally, for regularization schemes generated by Gaussian kernels with fexible variances we present explicit learning rates for regression with least square loss and classification with hinge loss