scholarly journals Evaluating the Immediate Response of Country-Wide Health Systems to the Covid-19 Pandemic: Applying the Gray Incidence Analysis Model

2021 ◽  
Vol 9 ◽  
Author(s):  
Tehmina Fiaz Qazi ◽  
Muhammad Zeeshan Shaukat ◽  
Abdul Aziz Khan Niazi ◽  
Abdul Basit
Keyword(s):  
Health Systems ◽  
Secondary Data ◽  
Real Data ◽  
Gray Relational Analysis ◽  
Future Research ◽  
Analysis Model ◽  
Cross Sectional ◽  
Data Set ◽  
Southern Africa Development Community ◽  
Incidence Analysis

The purpose of the study is to evaluate county-wide health systems using the data set of the first wave of the COVID-19 pandemic. The overall design of study comprises a literature review, secondary data, and a mathematical analysis. It is a cross-sectional quantitative study following a deductive approach. It uses the data of the first wave of the COVID-19 pandemic taken from the website of Worldometer as of April 8, 2020. The study uses a gray incidence analysis model (commonly known as Gray Relational Analysis, i.e., GRA) as its research methodology. On the basis of the results of GRA, a classification has been made under a predetermined scheme of ensigns: much better, better, somewhat better, fair, poor, somewhat worse, and worse health systems. There are a total 211 countries that have been divided into the seven aforementioned categories. Findings of the study show that Southern Africa Development Community (SADC) countries fall predominantly under the much better ensign, whereas Organization for Economic Co-operation and Development (OECD), Schengen Area (SA), and/or European Union (EU) countries fall under the worse ensign. Pakistan falls under the ensign of poor. It is an original attempt to evaluate the response of health systems based on real data using a scientific methodology. The study provides valuable information about the health systems of the countries for forming an informed opinion about the health systems herein. The study provides useful new information for stakeholders and a new framework for future research.

scholarly journals Evaluating Unemployment through Grey Incidence Analysis Model: A Study of One Hundred Thirteen Selected Countries

2021 ◽  
Vol VI (I) ◽  
pp. 23-35
Author(s):  
Abdul Aziz Khan Niazi ◽  
Tehmina Fiaz Qazi ◽  
Abdul Basit
Keyword(s):  
Secondary Data ◽  
Analysis Model ◽  
Data Set ◽  
Development Indicators ◽  
Low Level ◽  
New Information ◽  
Grey Incidence Analysis ◽  
High Level ◽  
Incidence Analysis

The purpose of the study is to gauge the unemployment level of selected one hundred and thirteen countries. The design of the study includes a survey of the literature, extraction of relevant data and analysis. The study follows a quantitative paradigm of research that uses secondary data set taken from the website of World Development Indicators (WDI). The analysis encompasses selected countries based on the availability of data. The data has been analyzed using Grey Incidence Analysis Model, commonly known as GRA. For interpretation of the results, the methodology has been augmented with the scheme of ensigns (i.e. classification of countries into Extremely Low, Very Low, Low, Moderate, High, Very High, Extremely High) of the level of unemployment. Results show that J&APR have an extremely low level of unemployment and member countries of SADC have an extremely high level of unemployment. Pakistan fall under the ensign of very low, therefore have a low level of unemployment. It is valuable to study equally useful for governments, academia and the international community. This study provides critical new information on the phenomenon.

Detecting common breaks in the means of high dimensional cross-dependent panels

2021 ◽  
Author(s):  
Lajos Horváth ◽  
Zhenya Liu ◽  
Gregory Rice ◽  
Yuqian Zhao
Keyword(s):  
Panel Data ◽  
Common Factors ◽  
Real Data ◽  
Change Points ◽  
High Dimensional ◽  
Asymptotic Results ◽  
Cross Sectional ◽  
Data Set ◽  
Monte Carlo Simulation Study ◽  
Cross Sectional Dependence

Abstract The problem of detecting change points in the mean of high dimensional panel data with potentially strong cross–sectional dependence is considered. Under the assumption that the cross–sectional dependence is captured by an unknown number of common factors, a new CUSUM type statistic is proposed. We derive its asymptotic properties under three scenarios depending on to what extent the common factors are asymptotically dominant. With panel data consisting of N cross sectional time series of length T, the asymptotic results hold under the mild assumption that min {N, T} → ∞, with an otherwise arbitrary relationship between N and T, allowing the results to apply to most panel data examples. Bootstrap procedures are proposed to approximate the sampling distribution of the test statistics. A Monte Carlo simulation study showed that our test outperforms several other existing tests in finite samples in a number of cases, particularly when N is much larger than T. The practical application of the proposed results are demonstrated with real data applications to detecting and estimating change points in the high dimensional FRED-MD macroeconomic data set.

Spatial analysis of Twitter sentiment and district-level housing prices

2019 ◽  
Vol 12 (2) ◽  
pp. 173-189
Author(s):  
Christopher Hannum ◽  
Kerem Yavuz Arslanli ◽  
Ali Furkan Kalay
Keyword(s):  
News Media ◽  
Housing Prices ◽  
District Level ◽  
Future Research ◽  
Scoring Method ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
Property Prices ◽  
Price Appreciation

Purpose Studies have shown a correlation and predictive impact of sentiment on asset prices, including Twitter sentiment on markets and individual stocks. This paper aims to determine whether there exists such a correlation between Twitter sentiment and property prices. Design/methodology/approach The authors construct district-level sentiment indices for every district of Istanbul using a dictionary-based polarity scoring method applied to a data set of 1.7 million original tweets that mention one or more of those districts. The authors apply a spatial lag model to estimate the relationship between Twitter sentiment regarding a district and housing prices or housing price appreciation in that district. Findings The findings indicate a significant but negative correlation between Twitter sentiment and property prices and price appreciation. However, the percentage of check-in tweets is found to be positively correlated with prices and price appreciation. Research limitations/implications The analysis is cross-sectional, and therefore, unable to answer the question of whether Twitter can Granger-cause changes in housing markets. Future research should focus on creation of a property-focused lexicon and panel analysis over a longer time horizon. Practical implications The findings suggest a role for Twitter-derived sentiment in predictive models for local variation in property prices as it can be observed in real time. Originality/value This is the first study to analyze the link between sentiment measures derived from Twitter, rather than surveys or news media, on property prices.

scholarly journals Measuring Relationship Quality in an International Study

2016 ◽  
Vol 28 (8) ◽  
pp. 920-930 ◽  
Author(s):  
Jill M. Chonody ◽  
Jacqui Gabb ◽  
Mike Killian ◽  
Priscilla Dunk-West
Keyword(s):  
Factor Analysis ◽  
Relationship Quality ◽  
Structural Equation ◽  
Community Sample ◽  
International Study ◽  
Equation Modeling ◽  
Future Research ◽  
Cross Sectional ◽  
Data Set ◽  
Convergent Construct Validity

Objective: This study reports on the operationalization and testing of the newly developed Relationship Quality (RQ) scale, designed to assess an individual’s perception of his or her RQ in their current partnership. Methods: Data were generated through extended sampling from an original U.K.-based research project, Enduring Love? Couple relationships in the 21st century. This mixed methods study was designed to investigate how couples experience, understand, and sustain their long-term relationships. This article utilizes the cross-sectional, community sample ( N = 8,132) from this combined data set, drawn primarily from the United Kingdom, United States, and Australia. A two-part approach to scale development was employed. An initial 15-item pool was subjected to exploratory factor analysis leading into confirmatory factor analysis using structural equation modeling. Results: The final 9-item scale evidenced convergent construct validity and known-groups validity along with strong reliability. Conclusion: Implications for future research and professional practice are discussed.

scholarly journals Effect of input credit on smallholder farmers’ output and income

2018 ◽  
Vol 78 (1) ◽  
pp. 98-115 ◽  
Author(s):  
Adam Iddrisu ◽  
Isaac Gershon Kodwo Ansah ◽  
Paul Kwame Nkegbe
Keyword(s):  
Smallholder Farmers ◽  
Northern Region ◽  
Northern Ghana ◽  
Primary Data ◽  
Future Research ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
Future Policy ◽  
On Farm

Purpose The purpose of this paper is to examine the effect of input credit on smallholder farmers’ output and income using Masara N’Arziki support project in Northern Ghana. Design/methodology/approach A cross-sectional primary data set was used to estimate the effect of project participation on farm output, yield and income using propensity score matching (PSM) methods. Findings The findings are that project participation is skewed towards experienced farmers with big-sized households and farms. The effect of project on outcomes is somewhat unsatisfactory in the sense that participation only raises output and yield, but not income. Research limitations/implications The paper only examined the project effect on farm outcomes among smallholder farmers participating in the programme in just one operational area in the Northern region. Future research should consider all the operational areas for an informed generalisation of findings. Practical implications Greater benefits to farmers from programme participation would require project management to review the contractual arrangement so that the high cost of input credit is significantly reduced. Originality/value The paper applied the PSM to estimate the effect of project participation on farm output, yield and income among smallholder farmers which is non-existent in the literature on the study area, at least as far as we know. This paper can inform future policy on the direction and nature of support for smallholder farmers in Northern Ghana.

Bayesian Structural Time Series

2020 ◽  
Vol 12 (1) ◽  
pp. 54-61
Author(s):  
Abdullah M. Almarashi ◽  
Khushnoor Khan
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Predictive Accuracy ◽  
Local Level ◽  
Real Life ◽  
Secondary Data ◽  
Real Data ◽  
Data Set ◽  
Structural Time Series ◽  
Structural Time

The current study focused on modeling times series using Bayesian Structural Time Series technique (BSTS) on a univariate data-set. Real-life secondary data from stock prices for flying cement covering a period of one year was used for analysis. Statistical results were based on simulation procedures using Kalman filter and Monte Carlo Markov Chain (MCMC). Though the current study involved stock prices data, the same approach can be applied to complex engineering process involving lead times. Results from the current study were compared with classical Autoregressive Integrated Moving Average (ARIMA) technique. For working out the Bayesian posterior sampling distributions BSTS package run with R software was used. Four BSTS models were used on a real data set to demonstrate the working of BSTS technique. The predictive accuracy for competing models was assessed using Forecasts plots and Mean Absolute Percent Error (MAPE). An easyto-follow approach was adopted so that both academicians and practitioners can easily replicate the mechanism. Findings from the study revealed that, for short-term forecasting, both ARIMA and BSTS are equally good but for long term forecasting, BSTS with local level is the most plausible option.

Transnational immigrant entrepreneurship: effects of home-country entrepreneurial ecosystem factors

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Carson Duan ◽  
Bernice Kotey ◽  
Kamaljeet Sandhu
Keyword(s):  
Home Country ◽  
Secondary Data ◽  
Small Sample ◽  
Future Research ◽  
Entrepreneurial Ecosystem ◽  
Data Set ◽  
Immigrant Entrepreneurship ◽  
Case Study Methodology ◽  
Content Type ◽  
Policies And Programs

PurposeThe purpose of this theoretical paper is to explore how immigrants' home-country entrepreneurial ecosystem (EE) factors impact transnational immigrant entrepreneurs (TIEs). The paper draws on the dual embeddedness and transnational entrepreneurship theories to explore how the home-country EE influences transnational immigrant entrepreneurship (TIE).Design/methodology/approachThis research adopted a qualitative case study methodology involving content analysis of secondary data. It analyzed data set against the existing EE framework to constructively explore the home-country effects.FindingsThe findings reveal that all home-country EE domains and associated factors affect TIEs. The paper established six testable propositions with regard to the home-country EE domains: accessible market, human capital, social culture, infrastructure and business support and government policies. A number of new factors were identified for each home-country EE domain. Finally, the paper provided future research directions.Research limitations/implicationsCare has to be taken in generalizing the findings from this research due to the small sample of contemporary Chinese immigrants in Australia and New Zealand. The propositions also require empirical testing.Practical implicationsThe findings contribute to the TIE literature by identifying new factors of the home-country EE and presenting testable propositions. The results have impact on immigration policies and programs.Social implicationsTransnational immigrant entrepreneurship can be a pathway to help immigrants to integrate into mainstream society. The findings from this article indirectly contribute to immigrant social development.Originality/valueThis original article fills research gaps by analyzing how home-country EE elements affect TIE. It reveals that the EE framework is effective for investigating it.

scholarly journals A Path Analysis of Latino Parental, Teenager and Cultural Variables in Teenagers' Sexual Attitudes, Norms, Self-Efficacy, and Sexual Intentions

2015 ◽  
Vol 23 (3) ◽  
pp. 500-511 ◽  
Author(s):  
Vanessa Pirani Gaioso ◽  
Antonia Maria Villarruel ◽  
Lynda Anne Wilson ◽  
Andres Azuero ◽  
Gwendolyn Denice Childs ◽  
...  
Keyword(s):  
Path Analysis ◽  
Self Efficacy ◽  
Sexual Communication ◽  
Sexual Attitudes ◽  
Secondary Data ◽  
Subjective Norms ◽  
Future Research ◽  
Cross Sectional ◽  
Cultural Variables ◽  
Correlational Design

OBJECTIVE: to test a theoretical model based on the Parent-Based Expansion of the Theory of Planned Behavior examining relation between selected parental, teenager and cultural variables and Latino teenagers' intentions to engage in sexual behavior.METHOD: a cross-sectional correlational design based on a secondary data analysis of 130 Latino parent and teenager dyads.RESULTS: regression and path analysis procedures were used to test seven hypotheses and the results demonstrated partial support for the model. Parent familism and knowledge about sex were significantly associated with parents' attitudes toward sexual communication with their teenagers. Parent Latino acculturation was negatively associated with parents' self-efficacy toward sexual communication with their teenagers and positevely associated with parents' subjective norms toward sexual communication with their teenagers. Teenager knowledge about sex was significantly associated with higher levels of teenagers' attitudes and subjective norms about sexual communication with parents. Only the predictor of teenagers' attitudes toward having sex in the next 3 months was significantly associated with teenagers' intentions to have sex in the next 3 months.CONCLUSION: the results of this study provide important information to guide future research that can inform development of interventions to prevent risky teenager sexual behavior among Latinos.

Are family firms good neighbours? A spatial analysis of Italian technology-intensive firms

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Stefano Amato ◽  
Valentina Pieroni ◽  
Nicola Lattanzi ◽  
Giampaolo Vitali
Keyword(s):  
Family Firms ◽  
Secondary Data ◽  
Spatial Proximity ◽  
Future Research ◽  
Family Status ◽  
Cross Sectional ◽  
Productivity Spillovers ◽  
Content Type ◽  
Family Enterprises

PurposeA burgeoning body of evidence points out the importance of spatial proximity in influencing firm efficiency besides internal characteristics. Nevertheless, the family status of the firm has been traditionally overlooked in that debate. Therefore, this study aims to investigate productivity spillovers stemming from the geographical closeness to innovators and family firms.Design/methodology/approachUsing secondary data on Italian technology-intensive manufacturing firms, the paper exploits spatial econometric models to estimate productivity spillovers across firms.FindingsAs regards the presence of spatial dependence, this study reveals that a firm's level of efficiency and productivity is influenced by that of nearby firms. Specifically, three main results emerge. First, spatial proximity to innovators is beneficial for the productivity of neighbouring firms. Second, closeness to family firms is a source of negative externalities for spatially proximate firms. However, and this is the third result, the adverse effect vanishes when the nearby family firms are also innovators.Research limitations/implicationsAs the study relies on cross-sectional data, future research should explore productivity spillovers in a longitudinal setting. Additionally, the channels through which productivity spillovers occur should be measured.Practical implicationsThe study highlights the importance of co-location for public policy initiatives to strengthen the competitiveness of firms and, indirectly, that of localities and regions. Moreover, the findings show the crucial role of innovation in mitigating the productivity gap between family and non-family firms.Social implicationsNotwithstanding the advent of the digital era, spatial proximity and localized social relationships are still a relevant factor affecting firms' performance.Originality/valueBy exploring the role of family firms in influencing the advantages of geographical proximity, this study contributes to the growing efforts to explore family enterprises across spatial settings.

scholarly journals Discriminating evidence accumulation from urgency signals in speeded decision making

2015 ◽  
Vol 114 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Guy E. Hawkins ◽  
Eric-Jan Wagenmakers ◽  
Roger Ratcliff ◽  
Scott D. Brown
Keyword(s):  
Decision Making ◽  
Empirical Support ◽  
Real Data ◽  
Positive Outcome ◽  
Future Research ◽  
Recent Theory ◽  
Data Set ◽  
Evidence Accumulation ◽  
Gating Model ◽  
Noisy Information

The dominant theoretical paradigm in explaining decision making throughout both neuroscience and cognitive science is known as “evidence accumulation”—the core idea being that decisions are reached by a gradual accumulation of noisy information. Although this notion has been supported by hundreds of experiments over decades of study, a recent theory proposes that the fundamental assumption of evidence accumulation requires revision. The “urgency gating” model assumes decisions are made without accumulating evidence, using only moment-by-moment information. Under this assumption, the successful history of evidence accumulation models is explained by asserting that the two models are mathematically identical in standard experimental procedures. We demonstrate that this proof of equivalence is incorrect, and that the models are not identical, even when both models are augmented with realistic extra assumptions. We also demonstrate that the two models can be perfectly distinguished in realistic simulated experimental designs, and in two real data sets; the evidence accumulation model provided the best account for one data set, and the urgency gating model for the other. A positive outcome is that the opposing modeling approaches can be fruitfully investigated without wholesale change to the standard experimental paradigms. We conclude that future research must establish whether the urgency gating model enjoys the same empirical support in the standard experimental paradigms that evidence accumulation models have gathered over decades of study.

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