Fuzzy panel data analysis

In statistical and econometric researches, three types of data are mostly used as cross-section, time series and panel data. Cross-section data are obtained by collecting the observations related to the same variables of many units at constant time. Time series data are data type consisted of observations measured at successive time points for single unit. Sometimes, the number of observations in cross-sectional or time series data is insufficient for carrying out the statistical or econometric analysis. In that cases, panel data obtained by combining cross-section and time series data are often used. Panel data analysis (PDA) has some advantages such as increasing the number of observations and freedom degree, decreasing of multicollinearity, and obtaining more efficient and consistent predictions results with more data information. However, PDA requires to satisfy some statistical assumptions such as “heteroscedasticity”, “autocorrelation”, “correlation between units”, and “stationarity”. It is too difficult to hold these assumptions in real-time applications. In this study, fuzzy panel data analysis (FPDA) is proposed in order to overcome these drawbacks of PDA. FPDA is based on predicting the parameters of panel data regression as triangular fuzzy number. In order to validate the performance of efficiency of FPDA, FPDA, and PDA are applied to panel data consisted of gross domestic production data from five country groups between the years of 2005-2013 and the prediction performances of them are compared by using three criteria such mean absolute percentage error, root mean square error, and variance accounted for. All analyses are performed in R 3.5.2. As a result of analysis, it is observed that FPDA is an efficient and practical method, especially in case required statistical assumptions are not satisfied.

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Pengaruh Dana Desa Dan Alokasi Dana Gampong Terhadap Kemiskinan Di Kecamatan Makmur Kabupaten Bireuen

Jurnal Ekonomi Regional Unimal ◽

10.29103/jeru.v3i1.3197 ◽

2020 ◽

Vol 3 (1) ◽

pp. 11

Author(s):

Aida Fitri ◽

Khairil Anwar

Keyword(s):

Time Series ◽

Data Analysis ◽

Panel Data ◽

Cross Section ◽

Time Series Data ◽

Panel Data Analysis ◽

Series Data ◽

Analysis Method ◽

Fund Allocation ◽

Data Analysis Method

This study aims to determine how much Influence funds and village fund allocation have on poverty in Makmur District, Bireuen Regency. This study uses the panel data analysis method. Which is a combination of time-series data from 2015 to 2019, and a cross-section involving 27 villages and results in 135 observations. The results show that village funds have a negative and significant effect on poverty in the Makmur sub-district. Meanwhile, the allocation of village fund has no significant effect on poverty in the Makmur sub-district.Keywords:Village Fund, VillageFund Allocation, Poverty.

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Determinant of Car Demand in Java Island

Economics Development Analysis Journal ◽

10.15294/edaj.v8i1.24947 ◽

2019 ◽

Vol 8 (1) ◽

pp. 1-8

Author(s):

Dyah Candra Kirana ◽

Prasetyo Ari Bowo

Keyword(s):

Data Analysis ◽

Panel Data ◽

Cross Section ◽

Time Series Data ◽

Panel Data Analysis ◽

Least Square ◽

Series Data ◽

Cross Section Data ◽

Section Data ◽

Per Capita

The purpose of this research is to examine factors that affect car demand in Java Island in 2012-2016. The research method used in this research is panel least square The data used in this research is panel data. The panel data consists of time series data (2012-2106) and cross section data (six province in Java Island, those are DKI Jakarta, Jawa Barat, Jawa Tengah, DI Yogyakarta, Jawa Timur, and Banten). Data were obtained from Central Bureau of Statistic Republic of Indonesia (BPS). Data analysis used is panel data analysis. The results showed that income per capita, population, and inflation have simultan effect on car demand in Java Island in 2012-2016. Per capita income has a positive and significant effect on car demand in Java Island in 2012-2016. Population has a positive and significant effect on car demand in Java Island in 2012-2016. Inflation has positive and insignificant effect on car demand in Java Island in 2012-2016.

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Determinan Pengangguran Terdidik Lulusan Universitas di Pulau Jawa

Jurnal Ekonomi Pembangunan ◽

10.23960/jep.v10i3.280 ◽

2021 ◽

Vol 10 (3) ◽

pp. 178-187

Author(s):

Leni Anjarwati ◽

Whinarko Juliprijanto

Keyword(s):

Time Series ◽

Data Analysis ◽

Cross Section ◽

Quantitative Methods ◽

Negative Impact ◽

Positive Impact ◽

Panel Data Analysis ◽

Series Data ◽

Cross Section Data ◽

Section Data

This study aims to determine the factors that influence educated unemployment in Java. The data used in this study is secondary data using quantitative methods. Data analysis uses panel data analysis which is a combination of time series and cross-section data. The time-series data uses data for the 2015-2019 period and cross-section data from 6 provinces on the island of Java. The results showed that simultaneously all variables had a significant effect on the level of educated unemployment. While partially shows that the variable level of education and PMDN have a significant positive impact on educated unemployment, and the UMR variable has a significant negative impact on educated unemployment.

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WHY PANEL DATA?

The Singapore Economic Review ◽

10.1142/s0217590805001937 ◽

2005 ◽

Vol 50 (02) ◽

pp. 143-154 ◽

Cited By ~ 25

Author(s):

CHENG HSIAO

Keyword(s):

Time Series ◽

Panel Data ◽

Cross Section ◽

Human Behavior ◽

Time Series Data ◽

Data Modeling ◽

Data Availability ◽

Series Data ◽

Single Cross Section ◽

Single Cross

We explain the proliferation of panel data studies in terms of (i) data availability; (ii) the heightened capacity for modeling the complexity of human behavior than a single cross-section or time series data can possibly allow; and (iii) challenging methodology. Advantages and issues of panel data modeling are also discussed.

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Cross-Sectional and Time Series Data as the Basis for Panel Modelling: The Case of Kidnappings in México From 2010 to 2019

International Journal of Statistics and Probability ◽

10.5539/ijsp.v10n2p68 ◽

2021 ◽

Vol 10 (2) ◽

pp. 68

Author(s):

Juan Bacilio Guerrero Escamilla ◽

Arquímedes Avilés Vargas

Keyword(s):

Time Series ◽

Panel Data ◽

Operations Research ◽

Data Model ◽

Time Series Data ◽

Series Data ◽

Data Modelling ◽

Estimation Of Parameters ◽

Cross Sectional ◽

Gross Domestic Product Growth

This paper presents the elements entailing the building of a panel data model on the basis of both cross-sectional and time series dimensions, as well as the assumptions implemented for the model application; this, with the objective of focusing on the main elements of the panel data modelling, its way of building, the estimation of parameters and their ratification. On the basis of the methodology of operations research, a practical application exercise is made to estimate the number of kidnapping cases in Mexico based on several economic indicators, finding that from the two types of panel data analyzed in this research, the best adjustment is obtained through the random-effects model, and the most meaningful variables are the Gross domestic product growth and the informal employment rate from the period 2010 to 2019 in each of the states. Thus, it is illustrated that panel data modelling present a better adjustment of data than any other type of models such as linear regression and time series analysis.

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Psychometric network models from time-series and panel data

10.31234/osf.io/8ha93 ◽

2019 ◽

Author(s):

Sacha Epskamp

Keyword(s):

Time Series ◽

Panel Data ◽

Graphical Models ◽

Latent Variables ◽

Large Scale ◽

Time Series Data ◽

Series Data ◽

Single Subject ◽

Time Interval ◽

Cross Sectional

Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGM)---an undirected network model of partial correlations---between observed variables of cross-sectional data or single subject time-series data. This assumes that all variables are measured without measurement error, which may be implausible. In addition, cross-sectional data cannot distinguish between within-subject and between-subject effects. This paper provides a general framework that extends GGM modeling with latent variables, including relationships over time. These relationships can be estimated from time-series data or panel data featuring at least three waves of measurement. The model takes the form of a graphical vector-autoregression model between latent variables and is termed the ts-lvgvar when estimated from time-series data and the panel-lvgvar when estimated from panel data. These methods have been implemented in the software package psychonetrics, which is exemplified in two empirical examples, one using time-series data and one using panel data, and evaluated in two large-scale simulation studies. The paper concludes with a discussion on ergodicity and generalizability. Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rest on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.

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GRANGER CAUSALITY AND STRUCTURAL CAUSALITY IN CROSS-SECTION AND PANEL DATA

Econometric Theory ◽

10.1017/s0266466616000086 ◽

2016 ◽

Vol 33 (2) ◽

pp. 263-291 ◽

Cited By ~ 4

Author(s):

Xun Lu ◽

Liangjun Su ◽

Halbert White

Keyword(s):

Time Series ◽

Panel Data ◽

Cross Section ◽

Conditional Independence ◽

Time Series Data ◽

Structural Heterogeneity ◽

Series Data ◽

Direct Link ◽

Structural Effects ◽

Definition Of

Granger noncausality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G–) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable,G–non-causality follows from structural noncausality, and with suitable conditions (e.g., separability or monotonicity), structural causality also impliesG–causality. This justifies using tests ofG–non-causality to test for structural noncausality under the key conditional exogeneity assumption for both cross-section and panel data. We pay special attention to heterogeneous populations, allowing both structural heterogeneity and distributional heterogeneity. Most of our results are obtained for the general case, without assuming linearity, monotonicity in observables or unobservables, or separability between observed and unobserved variables in the structural relations.

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Panel data analysis of surface skid resistance for various pavement preventive maintenance treatments using long term pavement performance (LTPP) data

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2016-0540 ◽

2017 ◽

Vol 44 (5) ◽

pp. 358-366 ◽

Cited By ~ 3

Author(s):

Qiang Joshua Li ◽

You Zhan ◽

Guangwei Yang ◽

Kelvin C.P. Wang ◽

Chaohui Wang

Keyword(s):

Time Series ◽

Data Analysis ◽

Panel Data ◽

Preventive Maintenance ◽

Panel Data Analysis ◽

Pavement Performance ◽

Skid Resistance ◽

Cross Sectional ◽

Slurry Seal

Various preventive maintenance (PM) treatments have been employed to restore pavement skid resistance for enhanced safety. This paper investigates the effectiveness of PM treatments using panel data analysis (PDA). Panel data analysis investigates the differences of cross-sectional information among treatments, but also the time-series changes within each treatment over time. Panel data with multiple years of friction data for four treatments (thin overlay, slurry seal, crack seal, and chip seal) at various climate, traffic, and pavement conditions are obtained from 255 long term pavement performance (LTPP) testing sections. Both fixed- and random-effects models are developed to evaluate pavement skid resistance performance and to identify the most influencing factors. Results from the PDA models are compared to those from traditional ordinary regression models. Slurry seal is demonstrated to be the most effective treatment. Five factors (precipitation, freezing index, humidity, traffic, and pavement age) are identified to be significant for pavement friction. Fixed-effects panel model is selected for the development of friction prediction models. This study not only demonstrates the capability of PDA for analyzing friction data with cross-sectional and time-series characteristics, but also can assist engineers in selecting the most effective PM treatments for the desired level of skid resistance to reduce traffic crashes.

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