scholarly journals Reexamining Spatiotemporal Disparities of Financial Development in China Based on Functional Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Deqing Wang ◽  
Tianzhi Ye ◽  
Sihua Tian ◽  
Xu Wang
Keyword(s):  
Data Analysis ◽  
Financial Development ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Financial Structure ◽  
Three Dimensions ◽  
Theil Index ◽  
Financial Intermediary ◽  
Development Level ◽  
Structure Adjustment

Essentially, financial development is a continuous and time-varying process. This paper explicitly accounts for this by introducing functional data analysis to convert discrete observations into a smooth curve in order to represent the continuous process of financial development at the regional level. Regional disparities in financial development in China are reexamined from three dimensions: financial scale enlargement, financial structure adjustment, and financial intermediary efficiency. Disparities are then decomposed using an extended functional Theil index. It is necessary to introduce functional data into the research of financial development level. Through the functional research of China’s financial development level index of three different dimensions, the financial development level can be studied at the level of speed and acceleration, which fills up the research gap. The results imply that (1) the disparity in the level of financial scale enlargement and disparity in velocity and acceleration of financial intermediary efficiency are both statistically significant across regions, while the regional disparity in financial structure adjustment is insignificant, and (2) the downward trends of the total disparity in three dimensions are all dominated by the declining intraregional disparities. Since all 31 provinces have broken the traditional regional division, China does not need to implement region-specific protective policies in strengthening its deepening financial reforms.

scholarly journals Research on the Two-Way Time-Varying Relationship between Foreign Direct Investment and Financial Development Based on Functional Data Analysis

2021 ◽  
Vol 13 (11) ◽  
pp. 6033
Author(s):  
Deqing Wang ◽  
Qian Huang ◽  
Tianzhi Ye ◽  
Sihua Tian
Keyword(s):  
Foreign Direct Investment ◽  
Data Analysis ◽  
Financial Development ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Direct Investment ◽  
Mutual Influence ◽  
Time Varying ◽  
Promoting Effect ◽  
Functional Linear Regression

Studying how to achieve mutual promotion between financial development and foreign direct investment inflow contributes to the Chinese government’s work of formulating rational financial policy and FDI policy from a holistic point of view and promoting the healthy and ordered growth of the entire economy in China. Based on the provincial panel data from 2007 to 2018, this paper constructs comprehensive evaluation indexes for financial development and introduces functional data analysis (FDA) methods, extracts functional β-convergence from functional linear regression to analyze the two-way time-varying relationship and convergence and divergence between financial development and FDI in the country and the eastern, central, and western regions. The empirical results show that the mutual influence of FDI and financial development presents regional differences. In general, FDI has a promoting effect on financial development, while financial development has an inhibitory effect on FDI, and there is basically no convergence effect. Based on these conclusions, if the governments of various regions in China want to reduce the differences in financial development, promote coordinated financial development, and promote sustainable financial development, they should actively implement financial development policies, optimize the financial environment, and implement differentiated foreign investment policies to promote regional financial development.

scholarly journals Basis Expansions for Functional Snippets

Biometrika ◽  
2020 ◽  
Author(s):  
Zhenhua Lin ◽  
Jane-Ling Wang ◽  
Qixian Zhong
Keyword(s):  
Data Analysis ◽  
Convergence Rate ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Covariance Function ◽  
Covariance Estimation ◽  
Simulation Studies ◽  
Finite Sample ◽  
Covariance Functions ◽  
The Mean

Summary Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of interest to estimate both the mean and covariance functions. In this paper, we investigate mean and covariance estimation for functional snippets in which observations from a subject are available only in an interval of length strictly (and often much) shorter than the length of the whole interval of interest. For such a sampling plan, no data is available for direct estimation of the off-diagonal region of the covariance function. We tackle this challenge via a basis representation of the covariance function. The proposed estimator enjoys a convergence rate that is adaptive to the smoothness of the underlying covariance function, and has superior finite-sample performance in simulation studies.

scholarly journals Novel Analytic Approaches to Investigate Minute-Level Actigraphy and Associations With Physical Function

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 194-195
Author(s):  
Kaiyuan Hua ◽  
Sheng Luo ◽  
Katherine Hall ◽  
Miriam Morey ◽  
Harvey Cohen
Keyword(s):  
Physical Activity ◽  
Data Analysis ◽  
Physical Function ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Gait Speed ◽  
Community Dwelling ◽  
Accelerometer Data ◽  
Single Leg Stance ◽  
Minute Walk

Abstract Background. Functional decline in conjunction with low levels of physical activity has implications for health risks in older adults. Previous studies have examined the associations between accelerometry-derived activity and physical function, but most of these studies reduced these data into average means of total daily physical activity (e.g., daily step counts). A new method of analysis “functional data analysis” provides more in-depth capability using minute-level accelerometer data. Methods. A secondary analysis of community-dwelling adults ages 30 to 90+ residing in southwest region of North Carolina from the Physical Performance across the Lifespan (PALS) study. PALS assessments were completed in-person at baseline and one-week of accelerometry. Final analysis includes 669 observations at baseline with minute-level accelerometer data from 7:00 to 23:00, after removing non-wear time. A novel scalar-on-function regression analysis was used to explore the associations between baseline physical activity features (minute-by-minute vector magnitude generated from accelerometer) and baseline physical function (gait speed, single leg stance, chair stands, and 6-minute walk test) with control for baseline age, sex, race and body mass index. Results. The functional regressions were significant for specific times of day indicating increased physical activity associated with increased physical function around 8:00, 9:30 and 15:30-17:00 for rapid gait speed; 9:00-10:30 and 15:00-16:30 for normal gait speed; 9:00-10:30 for single leg stance; 9:30-11:30 and 15:00-18:00 for chair stands; 9:00-11:30 and 15:00-18:30 for 6-minute walk. Conclusion. This method of functional data analysis provides news insights into the relationship between minute-by-minute daily activity and health.

A kNN procedure in semiparametric functional data analysis

2021 ◽  
pp. 109028
Author(s):  
Silvia Novo ◽  
Germán Aneiros ◽  
Philippe Vieu
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data

scholarly journals Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1305
Author(s):  
Feliu Serra-Burriel ◽  
Pedro Delicado ◽  
Fernando M. Cucchietti
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Vegetation Index ◽  
Vegetation Recovery ◽  
Functional Responses ◽  
Elapsed Time ◽  
Synthetic Controls ◽  
Available Information

In recent years, wildfires have caused havoc across the world, which are especially aggravated in certain regions due to climate change. Remote sensing has become a powerful tool for monitoring fires, as well as for measuring their effects on vegetation over the following years. We aim to explain the dynamics of wildfires’ effects on a vegetation index (previously estimated by causal inference through synthetic controls) from pre-wildfire available information (mainly proceeding from satellites). For this purpose, we use regression models from Functional Data Analysis, where wildfire effects are considered functional responses, depending on elapsed time after each wildfire, while pre-wildfire information acts as scalar covariates. Our main findings show that vegetation recovery after wildfires is a slow process, affected by many pre-wildfire conditions, among which the richness and diversity of vegetation is one of the best predictors for the recovery.

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