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

2017 ◽  
Vol 44 (5) ◽  
pp. 358-366 ◽  
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.

scholarly journals Fuzzy panel data analysis

2021 ◽  
Vol 48 (3) ◽  
Author(s):  
Muhammet O. Yalçin ◽  
◽  
Nevin Güler Dincer ◽  
Serdar Demir ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Panel Data ◽  
Cross Section ◽  
Time Series Data ◽  
Panel Data Analysis ◽  
Series Data ◽  
Percentage Error ◽  
Cross Sectional ◽  
Statistical Assumptions

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.

An Examination of Audit Report Lag for Banks: A Panel Data Approach

2000 ◽  
Vol 19 (2) ◽  
pp. 159-174 ◽  
Author(s):  
B. Charlene Henderson ◽  
Steven E. Kaplan
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Explanatory Power ◽  
Omitted Variables ◽  
Audit Report ◽  
Cross Sectional ◽  
Cross Sectional Analysis ◽  
The Cross ◽  
Sectional Analysis

This study investigates the determinants of audit report lag (ARL) for a sample of banks. Researchers have been interested in the determinants of ARL, in part, because it impacts the timeliness of public disclosures. However, prior ARL research has relied exclusively on regression analysis of cross-sectional samples of companies from many industries. In addition to focusing exclusively on banks, panel data analysis is introduced and compared with cross-sectional analysis to demonstrate its power in dynamic settings and its potential to improve estimation. Results reveal important differences between cross-sectional analysis and panel data analysis. First, bank size is negatively related to ARL in cross-section but positively related to ARL using panel data analysis. The cross-sectional size estimate is subject to omitted variables bias, and furthermore, cross-sectional analysis fails to capture variation in size over time in relation to ARL. Panel data analysis both accounts for omitted variables and captures the dynamics of the relationship between size and ARL. As well, the panel data model's explanatory power far exceeds that of the cross-sectional model. This is primarily due to the panel model's use of firm-specific intercepts that both capture the role of reporting tradition and eliminate heterogeneity bias. Thus, panel data analysis proves to be a powerful tool in the analysis of ARL.

scholarly journals Financing structure of portuguese start-ups

2017 ◽  
Vol 7 (4-1) ◽  
pp. 135-147
Author(s):  
Liliana Raquel R. Silva ◽  
Luís M. P. Gomes
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Financial Crisis ◽  
Panel Data Analysis ◽  
Financial Structure ◽  
Cross Sectional ◽  
Legal Form ◽  
Financing Structure ◽  
Creative Solutions ◽  
Start Ups

The context where the companies operate has become more challenging given the binomial competitiveness and financial crisis. Market imbalances are an opportunity to explore creative solutions that characterize Start-Ups’ profiles. However, its innovative character carries risks that determine major funding difficulties. This way this article aims to investigate the influence of a set of variables in the composition of the financial structure of Portuguese Start-Ups. The methodology used is based on a cross-sectional data, integrating multivariate regressions (Logit, Tobit, and OLS), enriched by panel data analysis. The results show that company’s size, assets structure and legal form are statistically relevant.

scholarly journals Capital Investments and Manufacturing Firms’ Performance: Panel-Data Analysis

2020 ◽  
Vol 12 (4) ◽  
pp. 1689 ◽  
Author(s):  
Vanja Grozdić ◽  
Branislav Marić ◽  
Mladen Radišić ◽  
Jarmila Šebestová ◽  
Marcin Lis
Keyword(s):  
Data Analysis ◽  
Firm Performance ◽  
Panel Data ◽  
Fixed Effects ◽  
Panel Data Analysis ◽  
Manufacturing Firms ◽  
Capital Investments ◽  
Short Term ◽  
Term Performance

The main goal of this study was to examine the effects of capital investments on firm performance, using panel-data analysis. For this purpose, financial data were gathered for 60 manufacturing firms based in Serbia, in the period from 2004 to 2016. The main research hypotheses were developed in accordance with the definition, nature, and time aspect of capital investments. Therefore, empirical expectation of this study was that the relationship between capital investments and firm performance should be positive—they probably bring losses to the firm in the short term, but they should increase firm performance in the long term. Finally, the results have indeed shown that capital investments have statistically significant negative effect on the short-term performance, but positive effect on the long-term performance of the analyzed firms, while controlling for time-fixed effects and certain internal factors.

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

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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