Factors affecting economic output in developed countries: A copula approach to sample selection with panel data

Chinnakum, Warattaya; Sriboonchitta, Songsak; Pastpipatkul, Pathairat

doi:10.1016/j.ijar.2013.01.005

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…4. We have included in our basic model six determinants of economic growth, which are remittances, gross fixed capital formation, unemployment rate, FDI inflows, the real exchange rate and a dummy variable in times of crisis (Barcenilla et al, 2019;Chinnakum et al, 2013;de la Fuente-Mella et al, 2019;Fadejeva & Melihovs, 2008;Goliuk, 2017;Norkus, 2016;Rusu & Roman, 2018;Yazdi, 2019;Yucel, 2014).…”

Section: Resultsmentioning

confidence: 99%

Migration, Covid, Remittances and Economic Growth in the Baltic States: A Regional Perspective

Přívarová

2021

EUrASEANs

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The Baltic states have faced high emigration flows since joining the EU in 2004. While integrating to the EU, the region has faced many economic and political issues, including migration due to Covid-19 and various economic factors. These issues revealed the weaknesses of their economic policies, financial systems and social security systems. Though, after the EU accession in 2004, the Baltic economies demonstrated rapid economic growth, but the financial and economic crisis has evidently shown their weaknesses. The Baltic states is a crucial example of the regions where the current emigration has drawn the attention of policy makers seeking to reduce the possible negative effects of the emigration of young and skilled workers. We have employed fixed effects and OLS estimation to conduct our research. Our regression analysis has demonstrated that remittances positively and significantly contribute to the economic growth of the Baltics. However, we emphasize the importance of further analysis of the geography of remittances to the region, especially under conditions of Brexit given the high share of remittances which the Baltics were receiving from the United Kingdom during years. The effects of Brexit on remittances flows from the United Kingdom to the Baltics will definitely take place in the future, thus, shifts in emigration flows and remittances should be in focus.

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Section: Resultsmentioning

confidence: 99%

Migration, Covid, Remittances and Economic Growth in the Baltic States: A Regional Perspective

Přívarová

2021

EUrASEANs

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“…Tests for capturing different groups, heteroscedasticity and error distribution are shown in Table 4. In our basic model, we have included six determinants of economic growth which are: remittances, gross fixed capital formation, unemployment rate, FDI inflows, the real exchange rate and a dummy variable in times of crisis (Barcenilla et al, 2019;Chinnakum et al, 2013;de la Fuente-Mella et al, 2019;Fadejeva & Melihovs, 2008;Goliuk, 2017;Norkus, 2016;Rusu & Roman, 2018;Yazdi, 2019;Yucel, 2014).…”

Section: Resultsmentioning

confidence: 99%

Emigration, Remittances and Economic Growth in the Baltic States: A Regional Perspective

Přívarová

2020

EUrASEANs

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The Baltic States have been facing higher emigration flows since joining the EU in 2004. While integrating to the EU, this region has faced many economic and political issues, which revealed the weaknesses of their economic policies, financial systems and social security systems. Even though after the EU accession in 2004 the Baltic economies have demonstrated rapid economic growth, still their financial performance under the economic crisis has evidently shown their weaknesses. The Baltic States is a crucial example of the regions where the current emigration has drawn the attention of the policy-makers seeking to reduce the possible negative effects from the emigration of young and well-skilled workers. Here, we have employed fixed effects and OLS estimation methods to conduct our research analysis. Our regression analysis has demonstrated that remittances positively and significantly contribute to the economic growth of the Baltic. However, we also need to emphasize the importance of further analysis of the geography of remittances to the region, especially under the conditions of Brexit, given the fact that for many years a large share of remittances the Baltics were receiving from the United Kingdom. The effects of Brexit on the remittances flows from the United Kingdom to the Baltics will definitely take place in the future, thus, shifts in emigration flows and remittances should be in the focus of future research.

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“…Panel data (also known as longitudinal or cross-sectional time-series data) is a dataset that contains regularly repeated observations on the same objects. It allows us to control for variables that we cannot observe or measure such as cultural factors or differences in business practices across companies, or variables that change over time but not across entities [44]. A panel data model is an econometric model based on panel data to analyze the relationship between variables, which is widely used to assess the effects of socioeconomic drivers and the characteristics of energy consumption on CO 2 emissions [45][46][47].…”

Section: Panel Data Modelmentioning

confidence: 99%

Carbon Footprint Accounting and Influencing Factors Analysis for Forestry Enterprises in the Key State-Owned Forest Region of the Greater Khingan Range, Northeast China

Wang

Lin

et al. 2023

Sustainability

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This paper constructed a carbon footprint calculation model and analyzed the carbon footprint characteristics and impact mechanism of forestry enterprises in the Greater Khinggan Range, northeast China, based on the survey and statistical data during 2017–2021. The process-based life cycle assessment (LCA) was used to calculate the total carbon footprint and carbon footprint intensity; then, a panel data model combined with ridge regression was used to explore the impacts of different factors on the carbon footprint of the forestry enterprises. Results showed that the forestry enterprises’ total carbon footprint and carbon footprint intensity showed a general trend of increasing first and then decreasing from 2017 to 2021. The average annual carbon footprint of the forestry enterprises ranged from 2354 t CO2-eq to 24,354 t CO2-eq, and the average annual carbon footprint intensity ranged from 3.48 kg CO2-eq hm−2 to 31.76 kg CO2-eq hm−2. Fire area, the number of hired labor, and vehicle usage intensity are significant driving factors of the carbon footprint in forestry enterprises. The study results can provide references for policy formulation in relation to carbon footprint control in forest regions.

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Factors affecting economic output in developed countries: A copula approach to sample selection with panel data

Cited by 15 publications

References 30 publications

Migration, Covid, Remittances and Economic Growth in the Baltic States: A Regional Perspective

Migration, Covid, Remittances and Economic Growth in the Baltic States: A Regional Perspective

Emigration, Remittances and Economic Growth in the Baltic States: A Regional Perspective

Carbon Footprint Accounting and Influencing Factors Analysis for Forestry Enterprises in the Key State-Owned Forest Region of the Greater Khingan Range, Northeast China

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