2015
DOI: 10.1016/j.reseneeco.2015.04.003
|View full text |Cite
|
Sign up to set email alerts
|

Waste and organized crime in regional environments

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
5
0
1

Year Published

2015
2015
2023
2023

Publication Types

Select...
6
3
1

Relationship

1
9

Authors

Journals

citations
Cited by 78 publications
(6 citation statements)
references
References 45 publications
0
5
0
1
Order By: Relevance
“…In addition, the presence of first-order autocorrelation shows the need to establish a dynamic model. Furthermore, to acquire accurate and efficient parameter estimates, and to improve the robustness of our results, we eliminated cross-sectional dependence among errors by creating a year-dummy control variable [43][44][45][46]. The abovementioned conditions lead us to establish a dynamic panel vector autoregressive (VAR) model of first differences to test the relationships between the variables in question.…”
Section: Empirical Testmentioning
confidence: 99%
“…In addition, the presence of first-order autocorrelation shows the need to establish a dynamic model. Furthermore, to acquire accurate and efficient parameter estimates, and to improve the robustness of our results, we eliminated cross-sectional dependence among errors by creating a year-dummy control variable [43][44][45][46]. The abovementioned conditions lead us to establish a dynamic panel vector autoregressive (VAR) model of first differences to test the relationships between the variables in question.…”
Section: Empirical Testmentioning
confidence: 99%
“…So, checking the cross-sectional dependence is key to finding consistent and reliable results using the GMM parameter. Notably, creating the year-dummy control variable can check cross-individual correlation (D'Amato et al. , 2015; Roodman, 2009a,b; Sarafidis et al.…”
Section: Methodsmentioning
confidence: 99%
“…Hence, we employ a dependent variable in the previous period as an independent variable to control for autocorrelation. To control for cross-individual correlation, we create year dummy control variables following Sarafidis et al [61] and Roodman [65] to improve the robustness of our results [66]. The presence of unit roots demonstrates that the first-differenced variables should be used to estimate an empirical model.…”
Section: Empirical Analysismentioning
confidence: 99%