scholarly journals Application of Multivariate Time Series Cluster Analysis to Regional Socioeconomic Indicators of Municipalities

2021 ◽  
Vol 29 (3) ◽  
pp. 39-51
Author(s):  
Valentas Gružauskas ◽  
Dalia Čalnerytė ◽  
Tautvydas Fyleris ◽  
Andrius Kriščiūnas
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Real Estate ◽  
Comparative Method ◽  
Multivariate Time Series ◽  
Principal Component ◽  
Distance Matrix ◽  
Socioeconomic Indicators ◽  
Real Estate Valuation ◽  
Socio Economic Development

Abstract The socio-economic development of municipalities is defined by a set of indicators in a period of interest and can be analyzed as a multivariate time series. It is important to know which municipalities have similar socio-economic development trends when recommendations for policy makers are provided or datasets for real estate and insurance price evaluations are expanded. Usually, key indicators are derived from expert experience, however this publication implements a statistical approach to identify key trends. Unsupervised machine learning was performed by employing K-means clusterization and principal component analysis for a dataset of multivariate time series. After 100 runs, the result with minimal summing error was analyzed as the final clusterization. The dataset represented various socio-economic indicators in municipalities of Lithuania in the period from 2006 to 2018. The significant differences were noticed for the indicators of municipalities in the cluster which contained the 4 largest cities of Lithuania, and another one containing 3 districts of the 3 largest cities. A robust approach is proposed in this article, when identifying socio-economic differences between regions where real estate is allocated. For example, the evaluated distance matrix can be used for adjustment coefficients when applying the comparative method for real estate valuation.

A model of the analysis of the dynamics and structure of socio-economic development (an example of the set of the largest Polish cities in the years 1998–2015)

2019 ◽  
Vol 63 (1) ◽  
pp. 25-37
Author(s):  
Lidia Mierzejewska ◽  
Jerzy Parysek
Keyword(s):  
Principal Component Analysis ◽  
Economic Development ◽  
Development Process ◽  
Principal Component ◽  
The State ◽  
Geographical Research ◽  
Development Policies ◽  
Socio Economic Development ◽  
Development Processes ◽  
State Of Affairs

Abstract The complexity of the reality studied by geographical research requires applying such methods which allow describing the state of affairs and ongoing changes in the best possible way. This study aims to present a model of research on selected aspects of the dynamics and structure of socio-economic development. The idea was to determine whether we deal with the process of reducing or widening the differences in terms of individual features. The article primarily pursues a methodological goal, and to a lesser extent an empirical one. The methodological objective of the paper was to propose and verify a multi-aspect approach to the study of development processes. The analyses carried out reveal that in terms of the features taken into account in the set of 24 of the largest Polish cities the dominating processes are those increasing differences between cities, which are unfavourable in the context of the adopted development policies aiming at reducing the existing disparities. In relation to the methodological objective, the results of the conducted research confirm the rationale of the application of the measures of dynamics and the feature variance to determine the character (dynamics and structure) of the socio-economic development process of cities. Comparatively less effective, especially for interpretation, is the application of principal component analysis and a multivariate classification, which is mainly the result of differences in the variance of particular features.

PROBLEMS AND PROSPECTS FOR THE DEVELOPMENT OF THE MORTGAGE LENDING MARKET IN RUSSIA

2020 ◽  
Vol 2 (12) ◽  
pp. 64-69
Author(s):  
Yu. A. DVORETSKAYA ◽  
◽  
K. S. MAKHNOVSKAYA ◽  
Keyword(s):  
Economic Development ◽  
Real Estate ◽  
Mortgage Lending ◽  
Residential Real Estate ◽  
Housing Provision ◽  
Mortgage Loans ◽  
Socio Economic Development

In connection with the transition to market principles for solving the issues of housing provision, one of the significant problems of the socio-economic development of Russia has become the multiple gap between the size of current monetary receipts of citizens and the high cost of residential real estate. This article examines the features of the mortgage lending market in Russia, the problems and prospects of its development, provides and analyzes the statistics of mortgage loans.

PATTERN RECOGNITION OF THE TERM STRUCTURE USING INDEPENDENT COMPONENT ANALYSIS

2006 ◽  
Vol 20 (02) ◽  
pp. 173-188 ◽  
Author(s):  
EDMOND HAOCUN WU ◽  
PHILIP L. H. YU
Keyword(s):  
Time Series ◽  
Pattern Recognition ◽  
Independent Component Analysis ◽  
Term Structure ◽  
Financial Time Series ◽  
Empirical Studies ◽  
Multivariate Time Series ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component

Term structure is a useful curve describing some financial asset as a function of time to maturity or expiration. In this paper, we propose to use Independent Component Analysis (ICA) to model the term structure of multiple yield curves. The idea is that we first employ ICA to decompose the multivariate time series, then we suggest two ICA methods for dimension reduction and pattern recognition of the term structure. We also compare the results by using an alternative method, Principal Component Analysis (PCA). The empirical studies suggest that the proposed ICA approaches outperform PCA methods in modeling the term structure. This model can be used in financial time series analysis as well as related financial applications.

scholarly journals Innovations in government management of the healthcare system: forecasting of covid-19 consequences in social, investment and business development

2020 ◽  
pp. 11-25
Author(s):  
Tetiana Vasylieva ◽  
Olha Kuzmenko ◽  
Naila Rashid Musayeva ◽  
Sergej Vojtovic ◽  
Maria Kascha ◽  
...  
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Fourier Series ◽  
Capital Investment ◽  
Innovation Policy ◽  
Additive Models ◽  
Negative Consequences ◽  
Time Series Decomposition ◽  
Socio Economic Development ◽  
The Impact

This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of the need for an innovative policy in the area of health protection at the link with the transformation of the social and economic development of the country through the pandemic COVID-19. The main goal of this study is to predict two scenarios for the development of the main indicators of the country's socio-economic development: considering the pandemic COVID-19 and the possible course of events without the influence of epidemiological threats. The systematization of literary sources and approaches to innovation and the determination of the volume of negative consequences for the national economy, due to the introduction of quarantine restrictions, has shown that this issue is quite relevant around the world. The study of the transformation of the trajectory of economic development of Ukraine in the article was carried out in the following logical sequence: 1) collection of statistical information, including 118 indicators of social development, the state of capital investment and business expectations of Ukrainian enterprises and screening of multicollinear indicators among them; 2) performing a time series decomposition separately for the interval 5 years before quarantine and taking into account the impact of the pandemic; 3) forecasting the consequences of the pandemic according to the investigated indicators of economic development in 2020-2022 by turning the time series into the Fourier series. The methodological tools of the study were methods of checking for multicollinearity by Pearson coefficients, decomposition of additive models into a trend and cyclic components, selection of cyclic oscillations by fast Fourier transform, extrapolation of constructed models for subsequent years, and quality control of constructed models by F-test quarterly data for 2015-2020 are selected. The study empirically confirms and theoretically proves that among the socio-economic development factors studied, most experienced significant transformations due to the introduction of quarantine restrictions by the government. This leads to the need for innovation policy in the health sector in order to minimize such consequences in the future. Keywords: Fourier series, forecasting, COVID-19, innovation, time series decomposition, health care.

scholarly journals Evaluation and Analysis of Socio-Economic Development Level and Management in Guangxi province of China

2021 ◽  
Vol 39 (5) ◽  
Author(s):  
Li Zongkeng ◽  
Li Zhuoran ◽  
Andrii Mykhailov ◽  
Wei Shi ◽  
Yang Zhuquan ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Economic Development ◽  
Comprehensive Evaluation ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Development Level ◽  
Socio Economic Development ◽  
Cluster Analysis Method ◽  
And Cluster Analysis

This article takes 14 regions in Guangxi as the research object, selects ten indicators that can measure the level of socio-economic development, establishes an index system for evaluating the regional socio-economic development level of Guangxi regions, and uses principal component analysis method and cluster analysis method carry out comprehensive evaluation and difference analysis among the economic development level of Guangxi regions. First, the primary component analysis method uses to comprehensively evaluate the economic development level of 14 regions in Guangxi. The results show that there are vast differences in the economic development levels of regions in Guangxi. Secondly, a systematic cluster analysis method uses to classify and analyze the differences between regions according to the similarity of economic development status. Finally, combined with the results of principal component analysis and cluster analysis, comprehensive evaluation analysis and discussion on the economic development status of various regions in Guangxi, and based on the evaluation results, proposed countermeasures for the socio-economic development and management in Guangxi province of China.

Socio-economic Development of Darjeeling Himalayas: Categorical Principal Component Analysis (CATPCA) and Ordinal Logistic Regression (OLR)

2021 ◽  
pp. 128-135
Keyword(s):  
Principal Component Analysis ◽  
Economic Development ◽  
Logistic Regression ◽  
Regional Development ◽  
Principal Component ◽  
Standard Of Living ◽  
Psychological Wellbeing ◽  
Socio Economic Development ◽  
Categorical Principal Component Analysis ◽  
Darjeeling Himalayas

The measurement of regional development plays a crucial role in improving the quality of life of local communities. However, the process of analyzing the regional progress was challenging as regional development was presented as a multidimensional concept. Nonetheless, the study's primary objective was to understand the indicators that genuinely reflect the development process's various dimensions in the northernmost district of West Bengal, Darjeeling Himalayas. Seven dimensions of development, namely psychological well-being, health, education, governance, safety and crime, energy and environment and standard of living were identified for analyzing the socio-economic development of the Darjeeling Himalaya. A questionnaire was framed and circulated in the region for the collection of data. By applying Categorical Principal Component Analysis (CATPCA), the data collected was aggregated into the above mentioned seven dimensions of development and analyzed the relationship between these development indicators through the Ordinal Logistic Regression model (OLR). The results showed that education and governance indicators had a significant impact on the psychological wellbeing. Governance was affected by psychological wellbeing, while standard of living was affected by psychological wellbeing and health indicators in the region.

Comparison of Socio Economic Development of Tarai and Nepal

2020 ◽  
Vol 3 (1) ◽  
pp. 67-76
Author(s):  
Dipak Duvey
Keyword(s):  
Growth Rate ◽  
Economic Development ◽  
Capita Income ◽  
Gdp Growth ◽  
Socioeconomic Indicators ◽  
Per Capita Income ◽  
Literacy Rate ◽  
Gdp Growth Rate ◽  
Socio Economic Development ◽  
Per Capita

The comparison of socio economic development of Tarai and Nepal is the comparison of development of total Nepal with its southern part Tarai. Socio economically southern belt of Nepal, Tarai is leading whole Nepal in development. There are not any significant impacts of conflicts of Tarai in one and half decade, in socio economic development of rural development of Tarai. The comparative study has selected timeline of 2004, 2011 and 2019 to collect and analyze the socioeconomic indicators based on data of Central Bureau of Statistics (CBS Data). It is the study of literacy rate, access to electricity, GDP Growth rate and Per capita income of Nepal and Tarai region in different point of time of conflicts and resiliencies. The literacy rate was 55%, 65%, and72% in Tarai and 49%, 60% and 69% in Nepal; access to electricity were 40%, 78% and 95% in Tarai and 37%, 65% and 96% in Nepal. Similarly, Gross Domestic Product (GDP) Growth rate was 5%, 5% and 7.2% in Tarai and 4.7 %, 3.4%, and 7.1% in Nepal; Per capita income in USD was 300, 629 and 1100 in Tarai and 286, 610, and 1034 in Nepal from 2004, 2011, and 2019respectively. Therefore, Tarai is leading Nepal in socio economic development.

MULTIVARIATE TIME SERIES WITH VARIOUS HIDDEN UNIT ROOTS, PART II

1999 ◽  
Vol 15 (4) ◽  
pp. 469-518 ◽  
Author(s):  
Stéphane Gregoir
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Principal Component ◽  
Unit Roots ◽  
Estimation Procedure ◽  
Specification Test ◽  
Statistical Tools ◽  
Repeated Applications ◽  
Correction Terms ◽  
Statistical Results

This paper extends the statistical results obtained by Gregoir and Laroque (1994, Journal of Econometrics 63, 183–214). It develops statistical tools to analyze multivariate time series that can be represented under an autoregressive equation of finite order with various polynomial error correction terms at various frequencies with possibly a non-null deterministic part as introduced by Gregoir (1999, Econometric Theory 15, 435–468). We propose an estimation procedure that proceeds through repeated applications of principal component analysis and a specification test for the omission of a polynomial relation of cointegration at each frequency.

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