The optimal prediction of cross-sectional proportions in categorical panel-data analysis

1999 ◽  
Vol 27 (2) ◽  
pp. 373-382 ◽  
Author(s):  
Ping Zhang
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Optimal Prediction ◽  
Cross Sectional

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.

How does environmental performance map into environmental disclosure?

2019 ◽  
Vol 10 (1) ◽  
pp. 62-96 ◽  
Author(s):  
Hani Tadros ◽  
Michel Magnan
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Environmental Performance ◽  
Panel Data Analysis ◽  
Environmental Disclosure ◽  
Cross Sectional ◽  
Content Type ◽  
Period Effects ◽  
Environmental Disclosures ◽  
Sustainability Reports

Purpose Focusing on a sample of firms from environmentally sensitive industries over several years, this study aims to reexamine the association between environmental disclosure and environmental performance. Design/methodology/approach The authors use a panel data analysis to examine how the interaction between environmental performance and economic and legitimacy factors influence firms’ environmental disclosures. Findings Results suggest that environmental performance moderates the effect of economic and legitimacy incentives on firms’ propensity to provide proprietary environmental disclosure, with both sets of incentives being influential. More specifically, there appears to be a reporting bias based on the firm’s environmental performance whereas the high-performers disclose more environmental information in the three following vehicles: annual report, 10-K and sustainability reports combined. Results also show that economic and legitimacy factors influence the disclosure decisions of the low and high environmental performers differently. Practical implications Understanding the determinants of environmental disclosure for high and low environmental performers helps regulators to close the reporting gap between these firms. Social implications There is little evidence to suggest that firms with low-environmental performance attempt to use their disclosures to legitimize their environmental operations. Originality/value The study examines environmental disclosures of 78 firms over a period of 14 years in annual, 10-K and sustainability reports. The panel data analysis controls for significant cross-sectional and period effects.

scholarly journals Cross-Sectional Dependence in Panel Data Analysis

2011 ◽  
Vol 31 (5) ◽  
pp. 483-531 ◽  
Author(s):  
Vasilis Sarafidis ◽  
Tom Wansbeek
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Cross Sectional ◽  
Cross Sectional Dependence

scholarly journals Relationship of Foreign Trade and Economic Growth in Eurasian Economy: Panel Data Analysis

2017 ◽  
Vol 9 (9) ◽  
pp. 1 ◽  
Author(s):  
Nazife Özge Kilic ◽  
Murat Beser
Keyword(s):  
Economic Growth ◽  
Data Analysis ◽  
Panel Data ◽  
Foreign Trade ◽  
Panel Data Analysis ◽  
Homogeneity Test ◽  
Causality Test ◽  
Cross Sectional ◽  
Using Data ◽  
Relationship Of

In this study, relationship between foreign trade and economic growth had been examined for the countries of Eurasia Economic Union by using data in era of 1992-2015 with the help of panel data analysis. First of all, cross-sectional dependency and homogeneity test had been done in the study and it had been concluded that there is cross-sectional dependency in between the series. For this purpose, unit root and causality test considering the cross-sectional dependency had been applied. Relationship between the variables had been analyzed with the panel causality test developed by Konya (2006). It had been determined that there is bi-directional causality from growth to export and unidirectional causality from growth to import.

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.

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