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 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.

Foreign Direct Investment, Financial Development and Economic Growth: A Panel Data Analysis

2017 ◽  
Vol 51 ◽  
pp. 11-24 ◽  
Author(s):  
Elya Nabila Abdul Bahri ◽  
Abu Hassan Shaari Md Nor ◽  
Nor Hakimah Haji Mohd Nor ◽  
Tamat Sarmidi
Keyword(s):  
Economic Growth ◽  
Foreign Direct Investment ◽  
Data Analysis ◽  
Panel Data ◽  
Financial Development ◽  
Direct Investment ◽  
Panel Data Analysis

scholarly journals Study on the Influence of Air Pressure and Temperature on PM2.5 by Multivariate Functional Linear Regression Model

2020 ◽  
Vol 194 ◽  
pp. 05009
Author(s):  
Jinjing Yang
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Functional Data Analysis ◽  
Linear Regression Model ◽  
Functional Data ◽  
Basis Functions ◽  
Air Pressure ◽  
The Internet ◽  
Functional Linear Regression

In recent years, the Internet has developed rapidly, and we have more and more ways to collect data. We find that many data have the characteristics of functions. We can use the important method of functional data analysis to analyze these data. The basic idea of functional data analysis is to treat data with functional properties as a whole for analysis and corresponding processing. In this paper, the daily air pressure, temperature and PM2.5 data of 49 cities with serious PM2.5 pollution in 2017 are sorted out. We use a multivariate functional linear regression model to discuss the influence of pressure and temperature on PM2.5 when the number of basis functions is different.

Sparseness and functional data analysis

2018 ◽  
Author(s):  
Gareth James
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Sequential Algorithm ◽  
Data Sets ◽  
Growth Data ◽  
Sparse Functional Data ◽  
Set Of Points ◽  
Sparse Set ◽  
Functional Linear Regression

This article considers two functional data analysis settings where sparsity becomes important: the first involves only measurements at a relatively sparse set of points and the second relates to variable selection in a functional case. It begins with a discussion of two data sets that fall into the ‘sparsely observed’ category, the ‘growth’ data and the ‘nephropathy’ data, both of which are used to illustrate alternative approaches for analysing sparse functional data. It then examines different classes of methods that can be applied to functional data, such as basis functions, mixed-effects models and local smoothing techniques, as well as specific methodologies for dealing with sparse functional data in the principal components, clustering, classification, and regression settings. Finally, it describes two approaches for performing regressions involving a functional predictor and a scalar response: SASDA (sequential algorithm for selecting design automatically) and FLiRTI (Functional Linear Regression That’s Interpretable).

scholarly journals Functional data analysis: Application to daily observation of COVID-19 prevalence in France

2022 ◽  
Vol 7 (4) ◽  
pp. 5347-5385
Author(s):  
Kayode Oshinubi ◽  
◽  
Firas Ibrahim ◽  
Mustapha Rachdi ◽  
Jacques Demongeot
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Geographical Location ◽  
Linear Regression Models ◽  
Data Set ◽  
Canonical Correlations ◽  
Before And After ◽  
Functional Principal Components ◽  
Functional Linear Regression

<abstract> <p>In this paper we use the technique of functional data analysis to model daily hospitalized, deceased, Intensive Care Unit (ICU) cases and return home patient numbers along the COVID-19 outbreak, considered as functional data across different departments in France while our response variables are numbers of vaccinations, deaths, infected, recovered and tests in France. These sets of data were considered before and after vaccination started in France. After smoothing our data set, analysis based on functional principal components method was performed. Then, a clustering using k-means techniques was done to understand the dynamics of the pandemic in different French departments according to their geographical location on France map. We also performed canonical correlations analysis between variables. Finally, we made some predictions to assess the accuracy of the method using functional linear regression models.</p> </abstract>

scholarly journals The Data-Driven Pattern for Healthy Behaviors of Car Drivers Based on Daily Records of Traffic Count Data From 2018 To 2019 Near Airports. A Functional Data Analysis

2020 ◽  
Author(s):  
Mohammad Fayaz ◽  
Alireza Abadi ◽  
Soheila Khodakarim ◽  
Mohammadreza Hoseini ◽  
Alireza Razzaghi
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Count Data ◽  
Functional Data ◽  
Data Driven ◽  
Time Varying ◽  
Traffic Demand ◽  
The Road ◽  
Hourly Data ◽  
Traffic Count

The road traffic injuries risk factors such as driving offenses and average speed are concerns for health organizations to reduce the number of injuries. Without any comprehensive view of each road, one cannot decide about the effective policy. In this manner, the data-driven policy will help to improve and assess the decisions. The count data near the road of two airports is surveyed for investigating the time-varying speed zones. The descriptive statistics, ANOVA, and functional data analysis were used. The hourly data of traffic counts for four different locations at the entrance of the two airports, international and domestics, were collected for one the year 2018 to 2019.The hourly pattern of driving offenses for each road was assessed and the to and from airport roads had different peaks (&lt;0.05). The hour, weekdays, type of airport, direction and their interactions were statistically significant (&lt;0.05) for the chance of driving offenses. The speed average during the day was statistically different (&lt;0.5) by the number of different types of vehicles. The traffic count data is a great resource for decision making in safe driving subjects such as driving offenses. With functional data analysis, we can analyze them to get the most of the characteristics of this data. The airports are public places with high traffic demand in all countries that yields the different pattern of traffic transportation, therefore we extract the factors that affect the driving offenses. Finally, we conclude that conducting a time-varying speed zone near the airports seems vital.

scholarly journals Functional Data Analysis: Transition from Daily Observation of COVID-19 Prevalence in France to Functional Curves

2021 ◽  
Author(s):  
Kayode Oshinubi ◽  
Firas Ibrahim ◽  
Mustapha Rachdi ◽  
Jacques Demongeot
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Geographical Location ◽  
Linear Regression Models ◽  
Data Set ◽  
Canonical Correlations ◽  
Before And After ◽  
Functional Principal Components ◽  
Functional Linear Regression

AbstractIn this paper we use the technique of functional data analysis to model daily hospitalized, deceased, ICU cases and return home patient numbers along the COVID-19 outbreak, considered as functional data across different departments in France while our response variables are numbers of vaccinations, deaths, infected, recovered and tests in France. These sets of data were considered before and after vaccination started in France. We used some smoothing techniques to smooth our data set, then analysis based on functional principal components method was performed, clustering using k-means techniques was done to understand the dynamics of the pandemic in different French departments according to their geographical location on France map and we also performed canonical correlations analysis between variables. Finally, we made some predictions to assess the accuracy of the method using functional linear regression models.

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