Distributions of GDP across versions of the Penn World Tables: A functional data analysis approach

Chen, Tao; DeJuan, Joseph P.; Tian, Renfang

doi:10.1016/j.econlet.2018.05.038

Cited by 7 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many Technological level of the production sector of the global economy, which was formed at the beginning of market reforms, it cannot be considered progressive and corresponding to the level of developed countries [74][75][76][77][78][79][80][81].…”

Section: Discussionmentioning

confidence: 99%

Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators

Baboshkin

Yegina

Zemskova

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

This article aims to highlight various methods and approaches to grouping countries, according to the behavior of their open innovation indicators. GDP, inflation and unemployment are the most important indicators of the economic and social policies of states, allowing them to be evaluated and models built. To find the relationships between open innovation indicators the paper uses marginal analysis and feature reduction, as well as machine learning methods (shift to the mean, agglomerative clustering and random forest methods). The results showed that, after isolating all groups, the importance of the signs was established and the patterns of behavior of indicators for each group were compared and open innovation dynamics was analyzed. The conclusions showed that it is obvious that increasing the number of variables in the model and using more extensive indicators can greatly increase the accuracy, in contrast to the generally accepted simple classifications. This approach makes it possible to more accurately find the connections between sectors of the economy or between state economies in general. An accompanying result of the study was the clarification of the equality of open innovation indicators for the analysis of their interrelationships between countries.

show abstract

Section: Discussionmentioning

confidence: 99%

Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators

Baboshkin

Yegina

Zemskova

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

show abstract

“…We should note before moving forward that each patient's SBP was discretely recorded at different time points, while it indeed exits at any point in time over a continuous period of time. Thus, the underlying SBP process is a function over time intervals, and it was necessary to conduct functional data analysis (FDA) to first estimate the process before analysis (21,22). FDA is a nonparametric and continuous analysis technique proposed by Ramsay for functional data and has been shown to be an accurate estimation tool that can automatically adapt to the correct limit and recover the true underlying structure from discretely observed data in a wide variety of fields such as biomedical science, medicine, economics, finance, linguistics, psychology and sports (18,(23)(24)(25)(26)(27)(28).…”

Section: Methodsmentioning

confidence: 99%

Prognostic Impact of Blood Pressure Change Patterns on Patients With Aortic Dissection After Admission

Qiu

et al. 2022

Front. Cardiovasc. Med.

Self Cite

View full text Add to dashboard Cite

ObjectivesHypertension is a predominant risk factor for aortic dissection (AD), and blood pressure (BP) control plays a vital role in the management of AD. However, the correlation between BP change and the prognosis for AD remains unclear. This study aims to demonstrate the impact of BP change patterns on AD prognosis.MethodsThis retrospective study included AD patients at two institutions (Shanghai Ninth People's Hospital Affiliated with Shanghai Jiao Tong University School of Medicine and the Vascular Department of the First Affiliated Hospital of Anhui Medical University) between 2004 and 2018. The systolic BP (SBP) change patterns of these patients were analyzed by functional data analysis (FDA). The relationship between BP change patterns and the risk of adverse events (AEs) was assessed using survival analysis.ResultsA total of 458 patients with AD were eligible for analysis. The logistic regression analysis indicated that compared with that in patients with low SBP variation (SBPV), the incidence of AEs in patients with high SBPV was significantly higher (35.84 vs. 20.35%, OR 2.19, P < 0.001). The patients were divided into four categories (accelerating rise, accelerating drop, decelerating rise, and decelerating drop) based on their SBP patterns after FDA fitting. The results of Kaplan–Meier analysis showed that at the 15- and 20-min time points, the incidence of AEs in the decelerating-drop group was significantly lower than that in the accelerating-rise group (OR 0.19, P = 0.031 and OR 0.23, P = 0.050). However, at the 25- and 30-min time points, the difference between these four groups was not significant (OR 0.26, P = 0.08 and OR 0.29, P = 0.10).ConclusionsThis study classified AD patients into four groups according to the SBP change patterns the first 30 min following admission, of which those with accelerating rises in SBP are at the highest risk of AEs, while those with decelerating drops have the best prognosis in the first 24 h after admission. Clinical practitioners may benefit from analyzing patterns of in-hospital SBP.

show abstract

“…For such a feature, in our method we employ the FDA-based approach to fit the underlying process using local polynomial bases and obtain the forecasts by extending the movement of the process based on the boundary derivatives of the functional fitting. FDA is renowned for its ability to uncover the dynamics of unknown continuous processes without necessitating stringent assumptions about the data structure [26,37,38]. However, to the best of our knowledge, its application in tracing the f.d.…”

Section: Introductionmentioning

confidence: 99%

A Functional Data Approach for Continuous-Time Analysis Subject to Modeling Discrepancy under Infill Asymptotics

Chen,

Li,

Tian

2023

Mathematics

Self Cite

View full text Add to dashboard Cite

Parametric continuous-time analysis often entails derivations of continuous-time models from predefined discrete formulations. However, undetermined convergence rates of frequency-dependent parameters can result in ill-defined continuous-time limits, leading to modeling discrepancy, which impairs the reliability of fitting and forecasting. To circumvent this issue, we propose a simple solution based on functional data analysis (FDA) and truncated Taylor series expansions. It is demonstrated through a simulation study that our proposed method is superior—compared with misspecified parametric methods—in fitting and forecasting continuous-time stochastic processes, while the parametric method slightly dominates under correct specification, with comparable forecast errors to the FDA-based method. Due to its generally consistent and more robust performance against possible misspecification, the proposed FDA-based method is recommended in the presence of modeling discrepancy. Further, we apply the proposed method to predict the future return of the S&P 500, utilizing observations extracted from a latent continuous-time process, and show the practical efficacy of our approach in accurately discerning the underlying dynamics.

show abstract

Distributions of GDP across versions of the Penn World Tables: A functional data analysis approach

Cited by 7 publications

References 5 publications

Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators

Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators

Prognostic Impact of Blood Pressure Change Patterns on Patients With Aortic Dissection After Admission

A Functional Data Approach for Continuous-Time Analysis Subject to Modeling Discrepancy under Infill Asymptotics

Contact Info

Product

Resources

About