THE LAW OF ONE PRICE: DEVELOPED AND DEVELOPING COUNTRY MARKET INTEGRATION

The Law of One Price (LOP) is important to models of international trade and exchange rate determination. This study investigates a variant of the LOP applied to developed and developing countries. The competing hypothesis are (1) that one price prevails in both developed and developing countries and (2) that one price prevails in developed countries and another single price in developing countries. Using data from an internationally competitive commodity (soybean meal), we found evidence favors the first hypothesis, although two large developing countries under study are active participants in regional trade integration, which may bias them against the first hypothesis.law of one price, developing countries, error-correction model, directed graphs, Demand and Price Analysis,

In Search of the "Bank Lending Channel": Causality Analysis for the Transmission Mechanism of U.S. Monetary Policy

The bank lending channel states that changes in monetary policy cause changes in bank loans thus causing changes in real income. This implies the Federal Reserve can influence real income by controlling the level of intermediated loans. We apply a new method to test for an operative bank lending channel in the transmission mechanism of monetary policy. Combining an error correction model with directed acyclic graphs, we explore the existence of a bank lending channel and the effectiveness of U.S. monetary policy since 1960. This paper shows when an operative bank lending channel existed, explains its impact, and evaluates other channels of monetary policy.Financial Economics,

Light Reflectance Characteristics and Remote Sensing of Waterlettuce

Waterlettuce ( Pistia stratiotes L.) is a free-floating exotic aquatic weed that often invades and clogs waterways in the southeastern United States. A study was conducted to evaluate the potential of using remote sensing technology to distinguish infestations of waterlettuce in Texas waterways. Field reflectance measurements showed that waterlettuce had higher visible green reflectance than associated plant species. Waterlettuce could be detected in both aerial color- infrared (CIR) photography and videography where it had light pink to pinkish-white image tonal responses. Computer analysis of CIR photographic and videographic images had overall accuracy assessments of 86% and 84%, respectively. (PDF contains 6 pages.

EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch

Designing the structure of neural networks is considered one of the most challenging tasks in deep learning, especially when there is few prior knowledge about the task domain. In this paper, we propose an Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of succession, extinction, mimicry, and gene duplication to search neural network structure from scratch with poorly initialized simple network and few constraints forced during the evolution, as we assume no prior knowledge about the task domain. Specifically, we first use primary succession to rapidly evolve a population of poorly initialized neural network structures into a more diverse population, followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession. Extinction is applied in both stages to reduce computational cost. Mimicry is employed during the entire evolution process to help the inferior networks imitate the behavior of a superior network and gene duplication is utilized to duplicate the learned blocks of novel structures, both of which help to find better network structures. Experimental results show that our proposed approach can achieve similar or better performance compared to the existing genetic approaches with dramatically reduced computation cost. For example, the network discovered by our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35].Comment: CVPR 201

Canonical correlation analysis of high-dimensional data with very small sample support

This paper is concerned with the analysis of correlation between two high-dimensional data sets when there are only few correlated signal components but the number of samples is very small, possibly much smaller than the dimensions of the data. In such a scenario, a principal component analysis (PCA) rank-reduction preprocessing step is commonly performed before applying canonical correlation analysis (CCA). We present simple, yet very effective approaches to the joint model-order selection of the number of dimensions that should be retained through the PCA step and the number of correlated signals. These approaches are based on reduced-rank versions of the Bartlett-Lawley hypothesis test and the minimum description length information-theoretic criterion. Simulation results show that the techniques perform well for very small sample sizes even in colored noise