Stationary and Time-Varying Patterns in Activity Diary Panel Data: Explorative Analysis with Association Rules

2002 ◽  
Vol 1807 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Bertold Keuleers ◽  
Geert Wets ◽  
Harry Timmermans ◽  
Theo Arentze ◽  
Koen Vanhoof
Keyword(s):  
Panel Data ◽  
Association Rules ◽  
Activity Patterns ◽  
Time Varying ◽  
Diary Data ◽  
Econometric Methods ◽  
Before And After ◽  
Scant Attention ◽  
Activity Diary ◽  
Daily Activity Patterns

The question of identifying temporal patterns in activity diary data has received only scant attention in the transportation literature, but interest is rapidly increasing. Most of the existing research uses well-known econometric methods to quantify change. Use of association rules to explore activity diary panel data, involving two waves, for possible stationary and time-varying patterns in activity-travel patterns is reported. The data for this analysis stem from the municipality of Voorhout in the Netherlands. Data were collected in 1997 and 1998 before and after opening of a new railway station. Results of the analysis indicate that specific household and individual attributes have a larger effect on daily activity patterns than others and that the effect of these attributes has significantly changed. Because changes in other sociodemographic attributes are almost nonexistent and activity patterns for communities are known to be stable, this study claims that the observed shifts in dependencies come from this new station.

scholarly journals FINANCIAL PROFITABILITY OF DEMERGED COMPANIES

2021 ◽  
Vol 14(63) (2) ◽  
pp. 173-182
Author(s):  
Constantin Duguleană ◽  
◽  
Liliana Duguleană ◽  
Keyword(s):  
Panel Data ◽  
Financial Performance ◽  
Management Strategy ◽  
Organizational Management ◽  
Complete Analysis ◽  
Financial Situation ◽  
Research Results ◽  
Econometric Methods ◽  
Before And After ◽  
Financial Profitability

The paper presents a complete analysis of the evolution of the profitability of some Romanian companies that decided to demerge in 2013. The sample of companies was analyzed with statistical and econometric methods of panel data, in the sub-periods before and after demerger: 2005-2013 and 2014-2019. The main objective of research was to find out if the organizational management strategy was beneficial for obtainingbetter economic and financial performance. The research results were extended to the population to characterize the financial situation of all Romanian companies in the same situation as those in the sample

scholarly journals Estimating latent group structure in time-varying coefficient panel data models

2019 ◽  
Author(s):  
Jia Chen
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Time Varying ◽  
Clustering Method ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Latent Group ◽  
Group Structures

Summary This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided.

Exploratory data analysis of activity diary data: a space–time GIS approach

2011 ◽  
Vol 19 (3) ◽  
pp. 394-404 ◽  
Author(s):  
Jie Chen ◽  
Shih-Lung Shaw ◽  
Hongbo Yu ◽  
Feng Lu ◽  
Yanwei Chai ◽  
...  
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Space Time ◽  
Diary Data ◽  
Exploratory Data ◽  
Activity Diary

GMM estimation of linear panel data models with time-varying individual effects

2001 ◽  
Vol 101 (2) ◽  
pp. 219-255 ◽  
Author(s):  
Seung Chan Ahn ◽  
Young Hoon Lee ◽  
Peter Schmidt
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Time Varying ◽  
Gmm Estimation ◽  
Individual Effects

scholarly journals The Intricacies of Children’s Physical Activity

2015 ◽  
Vol 47 (1) ◽  
pp. 269-275 ◽  
Author(s):  
Timothy A Brusseau
Keyword(s):  
Physical Activity ◽  
Physical Education ◽  
Activity Patterns ◽  
Activity Levels ◽  
Physical Activity Patterns ◽  
Change Behavior ◽  
Before And After ◽  
School Recess ◽  
Physical Activity Programming ◽  
Children’S Physical Activity

AbstractUnderstanding the physical activity patterns of youth is an essential step in preparing programming and interventions needed to change behavior. To date, little is known about the intricacies of youth physical activity across various physical activity segments (i.e. in school, out of school, recess, classroom physical activity, physical education, weekends, etc.). Therefore, the purpose of the study was to examine the physical activity patterns of elementary school children across various segments and during two seasons. A total of 287 fourth and fifth graders from the Southwest US wore the Yamax Digiwalker SW-200 pedometer for 7 consecutive days during the Fall and Spring seasons. Children were prompted to record their step counts when arriving and leaving school, before and after physical education and recess, as well as on the weekends. Means and standard deviations were calculated and ANOVAs and t tests were utilized to examine difference by sex, season, and segment. Youth were more active outside of school and on weekdays (p<0.05). Boys were generally more active than girls and all youth were more active during the milder Spring season. There is a clear need for Comprehensive School Physical Activity Programming and weekend physical activity opportunities. Furthermore, greater emphasis is needed on PE and across other activity segments for girls to increase their physical activity levels.

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