Fiscal Resilience Index - A Proposition and Evidence of Emerging Market

Fiscal resilience is essential to maintain economic stability and sustainability. Until now, there are no mutually agreed indicators to show a country's fiscal resilience. This study aims to explore the possibility of forming the index of fiscal resiliency that captures more than one underlying variable that are more comprehensive as opposed to the most current practices that use only one narrow variable. The Principal Component Analysis (PCA) method is applied to build the foundation of the index, whilst the trial is experimentally conducted as a case study of Indonesia as an emerging market in 1995-2020. Using the PCA method produces an index model of fiscal resiliency formed by the variables of government revenue, spending, debt, and macroeconomic conditions. The use of such Fiscal Resilience Index (FRI) as the case of Indonesia in the period 1995-2020 shows a reasonably consistent result which is in line with the underlying condition of the country during such period. It gives a negative figure, which means Indonesia is in a bad fiscal condition due to its budget deficit strategy.

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A Methodology for Evaluation and Distribution of Patent Applications to INPI-BR Patent Examiners: A Case Study in the Electricity Field

10.5772/intechopen.98400 ◽

2021 ◽

Author(s):

Cesar Vianna Moreira Júnior ◽

Daniel Marques Golodne ◽

Ricardo Carvalho Rodrigues

Keyword(s):

Principal Component ◽

Patent Classification ◽

Technological Field ◽

International Patent ◽

Relevant Variables ◽

Pca Method ◽

Patent Applications ◽

Examination Process

This paper presents the development of a new methodology for evaluation and distribution of patent applications to the examiners at the Brazilian Patent Office considering a specific technological field, represented by classification of the application according to the International Patent Classification (IPC), and the variables corresponding to the volume of data of the application and its complexity for the examination process. After identifying the most relevant variables, such as the Specific Areas of Expertise (ZAE) of the examiners, a mathematical model was developed, including: (a) application of the principal component analysis (PCA) method; (b) calculation of a General Complexity Ratio (IGC); (c) classification into five classes (very light, light, moderate, heavy and very heavy) according to IGC average ranges and standard deviations; (d) implementation of a logic of distribution, compensating very heavy applications with very light ones, and light applications with heavy ones; and (e) calculation of a Distribution Balancing Ratio (IBD), considering the differences between the samples’ medians. The model was validated using a sample of patent applications including, in addition to the identified variables, the time for substantive examination by the examiner. Then, a correlation analysis of the variables with time and a comparison of the classifications according to the time and the IGC generated by the model were carried out. The results obtained showed a high correlation of the IGC with time, above 80%, as well as correct IGC classes in more than 80% of applications. The model proposed herein suggests that the three main relevant variables are: total number of pages, total number of claims, and total number of claim pages.

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Research on Tourism Network Index Model Based on Baidu Index --- A Case Study of Sanya

MATEC Web of Conferences ◽

10.1051/matecconf/201822805017 ◽

2018 ◽

Vol 228 ◽

pp. 05017

Author(s):

Caixia Chen ◽

Chun Shi ◽

Jue Chen

Keyword(s):

Time Series Data ◽

Principal Component ◽

Series Data ◽

Analysis Method ◽

Index Model ◽

Network Search ◽

Weighted Analysis ◽

Development Level ◽

The Stability

Tourism index is a "barometer" to reflect the overall development level of tourism. The tourism index compiled by historical data can not reflect the real situation accurately with the increasing influence of network events on tourism In the Internet era. This study collects time series data of tourism network search by Baidu index tool and uses data mining method and principal component analysis method to detect and standardize the stability of the data. The spss system and the weighted analysis method are used to construct the tourism network index model. Finally, the model detection is carried out by comparing the actual tourism data in Sanya. This study is an important supplement to the existing tourism index.

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A hybrid PCA-AHP-Multi-grade Fuzzy approach to assess marketing-based flexibility

Marketing Intelligence & Planning ◽

10.1108/mip-07-2017-0144 ◽

2018 ◽

Vol 36 (2) ◽

pp. 213-229 ◽

Cited By ~ 4

Author(s):

Ruchi Mishra ◽

Onkar Nath Mishra

Keyword(s):

Hybrid Approach ◽

Principal Component ◽

Point Of View ◽

Fuzzy Approach ◽

Content Type ◽

Pca Method ◽

Plant Level ◽

Insight Into ◽

Number Of Firms

Purpose The purpose of this paper is to propose a novel hybrid approach to assess marketing-based flexibility with respect to its source factors, enablers and attributes. Design/methodology/approach The study demonstrates an application of a hybrid principal component analysis (PCA)-analytical hierarchical process (AHP)-multi-grade fuzzy approach (MFA) to measure marketing-based flexibility. Using PCA method, attributes, enablers and source factors of marketing-based flexibility were identified and a conceptual model was developed. AHP and MFA were used to compute marketing-based flexibility index. Findings The proposed approach measures existing level of marketing-based flexibility and therefore it identifies weak areas that should be taken care to improve flexibility. Research limitations/implications The scope of the study is limited to plant level. The validity of the proposed approach is shown using a case study. For generalisation point of view, the application of this proposed approach should be investigated in a large number of firms in different industrial settings. Practical implications The study gives a reliable and valid method, which combines both statistical and MCDM techniques to measure existing level of flexibility and identify weak areas for flexibility improvement. Originality/value The findings provide insight into factors that should be worked upon to improve flexibility.

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Range of soil and climate characteristics appropriate for Pistacia atlantica forest development and rehabilitation (case study: Fars province, Iran)

Journal of Water and Land Development ◽

10.1515/jwld-2017-0008 ◽

2017 ◽

Vol 32 (1) ◽

pp. 71-78 ◽

Cited By ~ 1

Author(s):

Masoud Nejabat ◽

Mohammadreza Negahdarsaber ◽

Gholamreza Ghahari

Keyword(s):

Soil Depth ◽

Principal Component ◽

Soil Characteristics ◽

Limiting Factors ◽

Soil Conditions ◽

Fars Province ◽

Pistacia Atlantica ◽

Pca Method ◽

Physical And Chemical

Abstract Investigation of ranges of soil and climate characteristics appropriate for the tolerant species: Pistacia atlantica subsp. mutica according to field study was the main objective of this research. This study was carried out based on random sampling across 20×20 km wild pistachio forests of Fars province (Iran). Results showed that mountainous and hilly lands are the main land types that pistachio species have evolved on. Statistical analysis of physical and chemical soil characteristics based on principal component analysis (PCA) method showed that wide ranges in soil characteristics, even up to about 40% differentiation in some measured properties, did not restricts this subspecies natural growth. The main growth limiting factors were shallow soil depth and light soil texture that decreased storage capacity of soil moisture, necessary for wild pistachios survival during drought and long dry periods. Climatic elements were analysed through the same approach and showed that temperature, precipitation and wind with overall variability of 85.9% were the most effectual factors. Pistacia atlantica subsp. mutica is one of the species refractory to various soil conditions and adapted to weak soils for the establishment and rehabilitation of forests in semi-arid regions.

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Source Apportionment of Air Pollution: A Case Study In Malaysia

Jurnal Teknologi ◽

10.11113/jt.v72.2934 ◽

2014 ◽

Vol 72 (1) ◽

Cited By ~ 2

Author(s):

Azman Azid ◽

Hafizan Juahir ◽

Mohd Ekhwan Toriman ◽

Azizah Endut ◽

Mohd Khairul Amri Kamarudin ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Source Apportionment ◽

Principal Component ◽

Quality Parameters ◽

Effective Management ◽

Chemometric Technique ◽

Pca Method ◽

Point Pollution

Air pollution is becoming a major environmental issue in Malaysia. This study focused on the identification of potential sources of variations in air quality around the study area based on the data obtained from the Malaysian Department of Environment (DOE). Eight air quality parameters in ten monitoring stations for seven years (2006 – 2012) were gathered. The Principal Component Analysis (PCA) method from chemometric technique was applied to identify the source identification of pollution around the study area. The PCA method has identified methane (CH4), non-methane hydrocarbon (NmHC), total hydrocarbon (THC), ozone (O3) and particulate matter under 10 microns (PM10) are the most significant parameters around the study area. From the study, it can be concluded that the application of the PCA method in chemometric techniques can be applied for the source apportionment purpose. Hence, this study indicated that for the future and effective management of the Malaysian air quality, an effort should be placed as a priority in controlling point and non-point pollution sources.

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Hydrothermal Minerals Mapping using based on Remotely Sensed Data from Sentinel 2 Sattelite: a Case Study in Vinh Phuc Province, Northern Vietnam

Mining science and technology ◽

10.17073/2500-0632-2019-4-309-317 ◽

2020 ◽

Vol 4 (4) ◽

pp. 309-317

Author(s):

Le Hung Trinh

Keyword(s):

Principal Component ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Blue Band ◽

Northern Vietnam ◽

Hydrothermal Mineralization ◽

Pca Method ◽

Edge Band ◽

Sentinel 2

This paper presents the experiences obtained in the application of Principal Component Analysis (PCA) method to map hydrothermal minerals based on remotely sensed data. In this study, Sentinel-2B MultiSpectral Instrument (MSI) image is used to detect distribution of hydroxyl-bearing minerals in Vinh Phuc province, northern Vietnam. Four bands of Sentinel-2B image including blue band (band 2), Vegetation Red Edge band (band 8A) and SWIR bands (band 11 and 12) are used to calculate the Principal Components, then and then select the Principal Component, which containing provides information on the hydrothermal minerals information. The obtained results findings show that the methodology and data are effective in detecting and mapping hydrothermal mineralization.

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