Disaggregating Input-Output Models

This Technical Document describes the steps to disaggregate an industry sector in an I-O table using Python. A disaggregation method that based on weight factors is used in this document. The calculation has three parts. The first part is to calculate the disaggregated input matrix DIM, which represents all the input from common sectors into the new sectors. The second part is to calculate the disaggregated output matrix DOM, which represents all possible output weights of new sectors into common sectors. The last part is to calculate the intra matrix DINM, which represents the allocation of intra-industry sales in the disaggregated sectors. This document states the problem and introduces the calculation method of the paper by Wolsky (1984), then follows with an example based on the data of the paper by Linder et al. (2013)

Three Essays on Investigating Province-Level Carbon Dioxide Emissions in China

The three essays in this dissertation study the influential factors of energy-related carbon dioxide emission intensity, whether the province-level carbon dioxide emission intensity is convergence, and how the spatial panel data models perform in forecasting against non-spatial panel data models for province-level carbon dioxide emissions in China.;The first essay entitled Spatial Analysis of China Province-Level CO 2 Emission Intensity offers a unique contribution to the literature by investigating the influential factors of energy-related carbon dioxide emission intensity among a panel of 30 provinces in China covering the period 1990-2010. This study uses novel spatial panel data models to analyze the influential factors of energy-related emission intensity, which are characterized by spatial dependence. It is found that emission intensities are negatively affected by per-capita, province-level GDP and population density. This relationship implies that promoting the local economic development and population concentration may help to reduce CO2 emission intensity. In addition, emission intensities are positively affected by energy consumption structure and transportation structure. These empirical evidences indicate that Chinese government should encourage the development of less carbon-intensive energy resources and further fuel efficiency standards in its transportation sector. Finally, energy prices have been found that there is no significant effect on emission intensities. This finding may suggest that the Chinese government should further deregulate energy prices to reduce artificial price distortions.;The second essay entitled Province-Level Convergence of China CO 2 Emission Intensity further explores the convergence of province-level CO2emission intensity among a panel of 30 provinces in China over the period 1990-2010. This study use a novel, spatial dynamic panel data model to evaluate an empirically testable hypothesis of convergence among provinces. Based on the estimation results, I find evidence that CO2emission intensities are converging across provinces in China. Moreover, the rate of convergence is higher with the dynamic panel data model (conditional convergence) than with a cross-sectional regression model (absolute convergence). This result suggests that the individual effects that are ignored in cross-sectional regressions potentially create omitted variable bias and the panel data framework arguably offers a more precise (efficiency) rate of convergence. Finally, it is found that province-level CO2emission intensities are spatially correlated, and the rate of convergence, when controlling for spatial autocorrelation, is higher than with the non-spatial models. This result indicates that technological spillovers, embodied in both the unobserved individual effects and the spatial autocorrelation coefficient, have a direct effect on the rate of convergence of carbon intensity among provinces.;The third essay entitled Forecasting Province-Level CO2Emissions in China examines the performance of spatial panel data models by comparing forecasts of province-level CO2emissions against empirical reality using dynamic, spatial panel data models with and without fixed effects. From a policy standpoint, understanding how to predict emissions is important for designing climate change mitigation policies. From a statistical standpoint, it is important to test spatial econometrics models to see if they are a valid strategy to describe the underlying data. The results of this essay suggest that the best model of forecasting province-level CO2emissions is the spatio-temporal panel data model with controlling the fixed effects. The findings demonstrate the importance of considering not only spatial and temporal dependence, but also the heterogeneous characteristics within each province

Separation and identification of mouse brain tissue microproteins using top‐down method with high resolution nanocapillary liquid chromatography mass spectrometry

Microproteins and endogenous peptides in the brain contain important substances that have critical roles in diverse biological processes, contributing to signal transduction and intercellular signaling. However, variability in their physical or chemical characteristics, such as molecule size, hydrophobicity, and charge states, complicate the simultaneous analysis of these compounds, although this would be highly beneficial for the field of neuroscience research. Here, we present a top-down analytical method for simultaneous analysis of microproteins and endogenous peptides using high- resolution nanocapillary LC-MS/MS. This method is detergent-free and digestion-free, which allows for extracting and preserving intact microproteins and peptides for direct LC-MS analysis. Both higher energy collision dissociation and electron-transfer dissociation fragmentations were used in the LC-MS analysis to increase the identification rate, and bioinformatics tools ProteinGoggle and PEAKS Studio software were utilized for database search. In total, we identified 471 microproteins containing 736 proteoforms, including brain-derived neurotrophic factor and a number of fibroblast growth factors. In addition, we identified 599 peptides containing 151 known or potential neuropeptides such as somatostatin-28 and neuropeptide Y. Our approach bridges the gap for the characterization of brain microproteins and peptides, which permits quantification of a diversity of signaling molecules for biomarker discovery or therapy diagnosis in the future

Research on the Risk Spillover Effect Between Financial Markets in China: Based on Dynamic CoES Model

This paper put forward a dynamic ΔCoES model to study the time-varying risk spillover between China’s stock market, exchange market and bond market from January 2007 to January 2017, based on the ΔCoVaR model proposed by Adrian and Brunnermeier (2016). The results show that the risk spillover of financial market in China is time-varying and asymmetric. During the financial crisis, the level of risk spillover between financial markets in China is higher than the average of spillover in the whole sample. During the “stock crash” of 2015, the risk spillover level of the stock market to the bond market and the foreign exchange market is higher than the average risk spillover level of the sample and the risk spillover level from the bond market to the stock market and the foreign exchange market is also higher than the average risk spillover level of the sample. After the exchange rate reform on August 11th of 2015, the risk spillover from exchange market to stock market and the bond market showed an upward trend, and in 2016, it was higher than that of the previous 8 years

Video elicited physiological signal dataset considering indoor temperature factors

IntroductionHuman emotions vary with temperature factors. However, most studies on emotion recognition based on physiological signals overlook the influence of temperature factors. This article proposes a video induced physiological signal dataset (VEPT) that considers indoor temperature factors to explore the impact of different indoor temperature factors on emotions.MethodsThis database contains skin current response (GSR) data obtained from 25 subjects at three different indoor temperatures. We selected 25 video clips and 3 temperatures (hot, comfortable, and cold) as motivational materials. Using SVM, LSTM, and ACRNN classification methods, sentiment classification is performed on data under three indoor temperatures to analyze the impact of different temperatures on sentiment.ResultsThe recognition rate of emotion classification under three different indoor temperatures showed that anger and fear had the best recognition effect among the five emotions under hot temperatures, while joy had the worst recognition effect. At a comfortable temperature, joy and calmness have the best recognition effect among the five emotions, while fear and sadness have the worst recognition effect. In cold temperatures, sadness and fear have the best recognition effect among the five emotions, while anger and joy have the worst recognition effect.DiscussionThis article uses classification to recognize emotions from physiological signals under the three temperatures mentioned above. By comparing the recognition rates of different emotions at three different temperatures, it was found that positive emotions are enhanced at comfortable temperatures, while negative emotions are enhanced at hot and cold temperatures. The experimental results indicate that there is a certain correlation between indoor temperature and physiological emotions