Fiscal Decentralization and Trade Openness: A Canada-US Dynamic Panel Model

2017 ◽  
Author(s):  
Fred Olayele ◽  
Kwok Tong Soo
Keyword(s):  
Fiscal Decentralization ◽  
Trade Openness ◽  
Dynamic Panel ◽  
Dynamic Panel Model ◽  
Panel Model

scholarly journals Robust FDI Determinants in Sub-saharan African Countries

2017 ◽  
Vol 4 (5) ◽  
pp. 21
Author(s):  
Nvuh Njoya Youssouf
Keyword(s):  
Trade Openness ◽  
Maximum Likelihood Estimates ◽  
African Countries ◽  
Dynamic Panel ◽  
Dynamic Panel Model ◽  
Panel Model ◽  
Sub Saharan ◽  
Bayesian Averaging ◽  
Fdi Determinants ◽  
The Empirical Analysis

The aim of this paper is to identify the robustness of the determinants of FDI in sub-Saharan African countries over the period 1985 to 2012. This is done through the use of a linear dynamic panel model, estimated by the Bayesian Averaging of Maximum Likelihood Estimates (BAMLE) developed by Moral Benito (2012). The empirical analysis show the following key results: (i) natural resources and market size are the most robust determinants; (ii) inflation, infrastructure, human capital and trade openness are weak robust; (iii) corruption and political instability are very less robust determinants.

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.

Long-run relationship between inequality and growth in post-reform China: New evidence from dynamic panel model

2016 ◽  
Vol 41 ◽  
pp. 238-252 ◽  
Author(s):  
Tingting Li ◽  
Jennifer T. Lai ◽  
Yong Wang ◽  
Dingtao Zhao
Keyword(s):  
Dynamic Panel ◽  
Dynamic Panel Model ◽  
Panel Model ◽  
Long Run ◽  
New Evidence

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%.

scholarly journals Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when Both n and T Are Large

Econometrica ◽  
2002 ◽  
Vol 70 (4) ◽  
pp. 1639-1657 ◽  
Author(s):  
Jinyong Hahn ◽  
Guido Kuersteiner
Keyword(s):  
Fixed Effects ◽  
Dynamic Panel ◽  
Dynamic Panel Model ◽  
Panel Model

Impact of Macroeconomic Factors on Cash Holdings?: A Dynamic Panel Model

2018 ◽  
Vol 17 (1_suppl) ◽  
pp. S27-S53 ◽  
Author(s):  
Lalita Anand ◽  
M. Thenmozhi ◽  
Nikhil Varaiya ◽  
Saumitra Bhadhuri
Keyword(s):  
Exchange Rate ◽  
Cash Holdings ◽  
Oil Price ◽  
Oil Price Shocks ◽  
Stock Market Index ◽  
Price Shocks ◽  
Dynamic Panel ◽  
Macroeconomic Factors ◽  
Dynamic Panel Model ◽  
Panel Model

We examine if macroeconomic factors impact cash holdings and how it influences the speed of adjustment of cash to target levels using the Arellano-Bover/Blundell-Bond dynamic panel model. We analyse a balanced panel and find that exchange rate, stock market index and oil price shocks impact cash holdings positively. We also observe that firms hold more cash in expectation of GDP growth, oil price shocks, increase in credit spread, budget deficit and hold marketable securities in expectation of inflationary conditions in the economy, while they hold less cash in expectation of an increase in exchange rate and increase in long-term and short-term bond rates. We further substantiate that cash holdings in firms adjust to target cash reserves in a dynamic process. We provide strong evidence that macroeconomic policy decisions impact cash levels in firms.

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