A study on the influence factors for the influx of NEET in OECD countries: An actual analysis employing the dynamic panel model (from the year 2005 to 2013)

2015 ◽  
Vol 26 (4) ◽  
pp. 85-115
Author(s):  
최용환
Keyword(s):  
Influence Factors ◽  
Oecd Countries ◽  
Dynamic Panel ◽  
Dynamic Panel Model ◽  
Panel Model

scholarly journals Regional differences in the capitalisation of first and second pillar payments of the CAP into land rental prices

2020 ◽  
Author(s):  
Klaus Salhofer ◽  
Paul Feichtinger
Keyword(s):  
Agricultural Policy ◽  
Regional Differences ◽  
Dynamic Panel ◽  
Accounting Data ◽  
Dynamic Panel Model ◽  
Panel Model ◽  
Land Rental ◽  
Part Time ◽  
High Yields ◽  
Large Farms

Abstract Nearly 80 per cent of Common Agricultural Policy (CAP) expenditures are spent on three different measures: first pillar payments (FPPs), agri-environmental payments (AEPs) and less favoured area payments (LFAPs). Based on a dynamic panel model and farm accounting data for Bavaria, we find that, on average, 30 per cent of FPPs, 40–50 per cent of LFAPs, but no relevant share of AEPs are capitalised into land rental prices. The capitalisation ratio varies considerably across regions. Above average capitalisation ratios for FPPs are observed in more favourable areas with high yields, a low grassland share and large farms. The same is true for LFAPs for areas with high yields, large farms and a greater share of part-time farmers.

scholarly journals X-Differencing and Dynamic Panel Model Estimation

2010 ◽  
Author(s):  
Chirok Han ◽  
Peter C. B. Phillips ◽  
Donggyu Sul
Keyword(s):  
Model Estimation ◽  
Dynamic Panel ◽  
Dynamic Panel Model ◽  
Panel Model

scholarly journals Does Climate Change Mitigation Activity Affect Crude Oil Prices? Evidence from Dynamic Panel Model

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Jude C. Dike
Keyword(s):  
Climate Change ◽  
Crude Oil ◽  
Climate Change Mitigation ◽  
Oil Prices ◽  
Carbon Intensity ◽  
Dynamic Panel ◽  
Dynamic Panel Model ◽  
Panel Model ◽  
Crude Oil Prices ◽  
Change Mitigation

This paper empirically investigates how climate change mitigation affects crude oil prices while using carbon intensity as the indicator for climate change mitigation. The relationship between crude oil prices and carbon intensity is estimated using an Arellano and Bond GMM dynamic panel model. This study undertakes a regional-level analysis because of the geographical similarities among the countries in a region. Regions considered for the study are Africa, Asia and Oceania, Central and South America, the EU, the Middle East, and North America. Results show that there is a positive relationship between crude oil prices and carbon intensity, and a 1% change in carbon intensity is expected to cause about 1.6% change in crude oil prices in the short run and 8.4% change in crude oil prices in the long run while the speed of adjustment is 19%.

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