A New Method of Identifying Key Industries: A Principal Component Analysis

2021 ◽  
Author(s):  
Lefteris Tsoulfidis ◽  
Ioannis Athanasiadis
Keyword(s):  
Principal Component ◽  
Economic Policies ◽  
Input Output ◽  
The Us ◽  
Slow Moving ◽  
Components Analysis ◽  
The Stability ◽  
Key Industries ◽  
Moving Process ◽  
Jel Classifications

Abstract This article using the principal components analysis identifies key industries and groups them into particular clusters. The data come from the US benchmark input-output tables of the years 2002, 2007, 2012 and the most recently published input-output table of the year 2019. We observe some intertemporal switches of industries both between and within the top clusters. The findings further suggest that structural change is a slow moving process and it takes time for some industries to move from one cluster to the other. This information may be proved important in the designation of effective economic policies by targeting particular industries and also for the stability properties of the economic system.JEL classifications: B24, B51, C67, D46, D57, E11, E32

scholarly journals Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

2022 ◽  
Author(s):  
Jaime González Maiz Jiménez ◽  
Adán Reyes Santiago
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Systematic Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Market Capitalization ◽  
The Us ◽  
Components Analysis

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

Note on the Factor Structure of the Millon Behavioral Health Inventory

1987 ◽  
Vol 60 (3) ◽  
pp. 799-802 ◽  
Author(s):  
Michael A. Murphy ◽  
Donald J. Tosi ◽  
Pamela Sharratt Wise ◽  
Dennis M. Eshbaugh
Keyword(s):  
Principal Component Analysis ◽  
Graduate Students ◽  
Behavioral Health ◽  
Factor Structure ◽  
Principal Component ◽  
Varimax Rotation ◽  
Components Analysis ◽  
The Stability ◽  
Pain Patients ◽  
Millon Behavioral Health Inventory

The factor structure of the Millon Behavioral Health Inventory was examined. Millon, Green, and Meagor in 1982 factor analyzed the responses to the inventory using a principal component analysis which gave four factors. Here procedures used by Millon, et al. were replicated. Principal components analysis ( N = 26 pain patients and 37 graduate students in counseling) with a varimax rotation resulted in four factors which essentially replicated the earlier findings. However, further study of the inventory should determine the stability of these factors with larger samples.

scholarly journals EXPRESS: Exploration of Principal Component Analysis: Deriving PCA Visually Using Spectra

2021 ◽  
pp. 000370282098784
Author(s):  
James Renwick Beattie ◽  
Francis Esmonde-White
Keyword(s):  
Principal Components ◽  
Principal Component ◽  
Original Data ◽  
Specific Chemical ◽  
Successive Refinement ◽  
Minimal Loss ◽  
Components Analysis ◽  
The Mathematical Model ◽  
Application Specific ◽  
Reconstructed Data

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal Components Analysis (PCA) is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning PCA is not well understood by many applied analytical scientists and spectroscopists who use PCA. The meaning of features identified through PCA are often unclear. This manuscript traces the journey of the spectra themselves through the operations behind PCA, with each step illustrated by simulated spectra. PCA relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of PCA, such the scores representing ‘concentration’ or ‘weights’. The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a PCA model shows how to interpret application specific chemical meaning of the PCA loadings and how to analyze scores. A critical benefit of PCA is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

scholarly journals Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

2021 ◽  
Vol 18 (9) ◽  
pp. 4560
Author(s):  
Mostafa Abbas ◽  
Thomas B. Morland ◽  
Eric S. Hall ◽  
Yasser EL-Manzalawy
Keyword(s):  
Functional Data ◽  
Time Series Data ◽  
Principal Component ◽  
State Level ◽  
The United States ◽  
Functional Principal Component Analysis ◽  
Series Data ◽  
Dynamic Correlation ◽  
The Us ◽  
Google Search

We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.

The great Chinese surprise: the rupture with the United States is real and is happening

2020 ◽  
Vol 96 (2) ◽  
pp. 419-437
Author(s):  
Xiangfeng Yang
Keyword(s):  
United States ◽  
The United States ◽  
Donald Trump ◽  
Ample Evidence ◽  
Pendulum Swing ◽  
The Us ◽  
Methodological Problems ◽  
Trade War ◽  
The Stability ◽  
The Individual

Abstract Ample evidence exists that China was caught off guard by the Trump administration's onslaught of punishing acts—the trade war being a prime, but far from the only, example. This article, in addition to contextualizing their earlier optimism about the relations with the United States under President Trump, examines why Chinese leaders and analysts were surprised by the turn of events. It argues that three main factors contributed to the lapse of judgment. First, Chinese officials and analysts grossly misunderstood Donald Trump the individual. By overemphasizing his pragmatism while downplaying his unpredictability, they ended up underprepared for the policies he unleashed. Second, some ingrained Chinese beliefs, manifested in the analogies of the pendulum swing and the ‘bickering couple’, as well as the narrative of the ‘ballast’, lulled officials and scholars into undue optimism about the stability of the broader relationship. Third, analytical and methodological problems as well as political considerations prevented them from fully grasping the strategic shift against China in the US.

scholarly journals Pre-Drawn Syringes of Comirnaty for an Efficient COVID-19 Mass Vaccination: Demonstration of Stability

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1029
Author(s):  
Francesca Selmin ◽  
Umberto M. Musazzi ◽  
Silvia Franzè ◽  
Edoardo Scarpa ◽  
Loris Rizzello ◽  
...  
Keyword(s):  
Mechanical Stress ◽  
Real Life ◽  
Storage Conditions ◽  
Lipid Nanoparticles ◽  
Mass Vaccination ◽  
Hydrodynamic Diameter ◽  
The Us ◽  
Real Mass ◽  
The Stability ◽  
And Storage

Moving towards a real mass vaccination in the context of COVID-19, healthcare professionals are required to face some criticisms due to limited data on the stability of a mRNA-based vaccine (Pfizer-BioNTech COVID-19 Vaccine in the US or Comirnaty in EU) as a dose in a 1 mL-syringe. The stability of the lipid nanoparticles and the encapsulated mRNA was evaluated in a “real-life” scenario. Specifically, we investigated the effects of different storing materials (e.g., syringes vs. glass vials), as well as of temperature and mechanical stress on nucleic acid integrity, number, and particle size distribution of lipid nanoparticles. After 5 h in the syringe, lipid nanoparticles maintained the regular round shape, and the hydrodynamic diameter ranged between 80 and 100 nm with a relatively narrow polydispersity (<0.2). Samples were stable independently of syringe materials and storage conditions. Only strong mechanical stress (e.g., shaking) caused massive aggregation of lipid nanoparticles and mRNA degradation. These proof-of-concept experiments support the hypothesis that vaccine doses can be safely prepared in a dedicated area using an aseptic technique and transferred without affecting their stability.

scholarly journals Multivariate analysis as a tool for measuring the stability of morphometric traits in Lycopersicon plants from in vitro culture

2000 ◽  
Vol 23 (2) ◽  
pp. 479-493 ◽  
Author(s):  
Guillermo Pratta ◽  
Roxana Zorzoli ◽  
Liliana Amelia Picardi
Keyword(s):  
Multivariate Analysis ◽  
In Vitro Culture ◽  
Principal Component ◽  
Eigenvalues And Eigenvectors ◽  
Morphometric Traits ◽  
Regenerated Plants ◽  
Phenotypic Structure ◽  
Regeneration Cycle ◽  
The Stability

The phenotypic stability of morphometric traits in Lycopersicon spp. (stem perimeter at the base, middle and top, and number of flowers per cluster) was measured by multivariate analysis through a progeny test in order to estimate the genetic stability of these traits. Principal components were calculated for two groups of Lycopersicon spp., non-regenerated plants and the progeny of regenerated plants. Analysis of variance was performed to support principal component analysis. Both groups presented similar eigenvalues and eigenvectors, while no significant differences were found between any of the traits studied. These results indicated that the phenotypic structure was the same among the progeny of regenerated and non-regenerated plants, so that no variation would occur in in vitro culture. Multivariate analysis proved to be an appropriate methodology for the measurement of the stability of morphometric traits after one regeneration cycle.

scholarly journals Impact of Project and Programme Aid on Economic Growth: A Cross Country Analysis

2018 ◽  
Vol 57 (2) ◽  
pp. 145-174
Author(s):  
Pervez Zamurrad Janjua ◽  
Malik Muhammad ◽  
Muhammad Usman
Keyword(s):  
Economic Growth ◽  
Principal Component ◽  
Economic Policies ◽  
Method Of Moment ◽  
System Equation ◽  
Moment Estimation ◽  
Country Analysis ◽  
Procurement Reform ◽  
Cross Country ◽  
The Impact

This study examines the impact of foreign aid instruments, namely Project Aid and Programme Aid, on economic growth of 27 aid-receiving countries. The study constructs a system of three equations, i.e. growth, investment and human capital. Using the Generalised Method of Moment estimation technique, the study concludes that while Project Aid has a positive and significant impact on economic growth, Programme Aid has an insignificant impact on economic growth. Additionally, the study finds that economic policies do enhance effectiveness of aid at aggregate level. Therefore, the capacity of aid-recipient countries to effectively use their resources for economic development needs due consideration. Keywords: Project Aid, Programme Aid, Economic Growth, Conditionality, Procurement Reform, System Equation Method, Generalised Method of Moment (GMM), Principal Component Analysis

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