Bootstrapping Cointegrating Regressions

In this paper, we consider bootstrapping cointegrating regressions. It is shown that the method of bootstrap, if properly implemented, generally yields consistent estimators and test statistics for cointegrating regressions. We do not assume any specific data generating process, and employ the sieve bootstrap based on the approximated finite-order vector autoregressions for the regression errors and the firrst differences of the regressors. In particular, we establish the bootstrap consistency for OLS method. The bootstrap method can thus be used to correct for the finite sample bias of the OLS estimator and to approximate the asymptotic critical values of the OLS-based test statistics in general cointegrating regressions. The bootstrap OLS procedure, however, is not efficient. For the efficient estimation and hypothesis testing, we consider the procedure proposed by Saikkonen (1991) and Stock and Watson (1993) relying on the regression augmented with the leads and lags of differenced regressors. The bootstrap versions of their procedures are shown to be consistent, and can be used to do inferences that are asymptotically valid. A Monte Carlo study is conducted to investigate the finite sample performances of the proposed bootstrap methods.

Deep Metric Learning via Facility Location

Learning the representation and the similarity metric in an end-to-end fashion with deep networks have demonstrated outstanding results for clustering and retrieval. However, these recent approaches still suffer from the performance degradation stemming from the local metric training procedure which is unaware of the global structure of the embedding space. We propose a global metric learning scheme for optimizing the deep metric embedding with the learnable clustering function and the clustering metric (NMI) in a novel structured prediction framework. Our experiments on CUB200-2011, Cars196, and Stanford online products datasets show state of the art performance both on the clustering and retrieval tasks measured in the NMI and Recall@K evaluation metrics.Comment: Submission accepted at CVPR 201

Carbon Catcher Design Report

Overview. The design of the overall Carbon Catcher project can be separated into four distinct systems, each of which is assigned a specialized committee. The committee names and responsibilities are listed below: Air Mover The overall goal for the Air Mover committee is the design of the turbine assembly. As the overall goal of the project is to collect and separate carbon dioxide from the air, one of the most important parts is to actually get the air to pass through the carbon-catching membrane. Passive air would not give a significant enough yield rate to make the carbon dioxide collection rate impactful, thus air must be sucked through a vacuum/turbine. Membrane The goal of Membrain is to create a membrane that can filter out CO2 through various methods. These methods are limited, due to there being such variety, to certain techniques and membrane material types that have been decided, prior, by the committee. Most membranes will be geared towards utilizing temperature and pressure along with gaseous speed and flow rate. In addition, examining certain treatments, such as regeneration of material, and replacements will be looked into as well, to see how it fares in sustainability. Carbon Storer The Carbon Storer committee will design a store and transport system for fluid CO2 after it is extracted from the atmosphere. Primary considerations include geological solutions, cost-effective materials, and analysis methods to improve overall capacity and efficiency. Additionally, the committee will select an environmentally and economically sustainable method of recycling the captured CO2. PyControl The PyControl committee will design a series of sensors and actuators, which will primarily support the sequestration and pipeline systems present in the Carbon Storer Committee and direct air capture system in Air Mover. The design can be broken into four control layers: Input/Output, Field Controllers, Data, and Supervisory. Goal The overarching goal of Carbon Catcher is to design a cost-effective, scalable atmospheric carbon dioxide removal system that is capable of being deployed in a variety of urban environments and may fit a variety of different customer requirements or requests