scholarly journals Could the Stock Return be a Leading Indicator of the Economic Growth in the Depression? Analysis Based on Nonlinear Dynamic Panel Model

10.5772/63629 ◽  
2016 ◽  
Author(s):  
Yuan-Ming Lee ◽  
Kuan-Min Wang
Keyword(s):  
Economic Growth ◽  
Nonlinear Dynamic ◽  
Stock Return ◽  
Dynamic Panel ◽  
Dynamic Panel Model ◽  
Panel Model ◽  
Leading Indicator

scholarly journals DETERMINANT OF BANK RUNS IN INDONESIA: BAD LUCK OR FUNDAMENTAL?

2011 ◽  
Vol 14 (1) ◽  
pp. 51-73
Author(s):  
Iskandar Simorangkir
Keyword(s):  
Economic Growth ◽  
Banking Crisis ◽  
Jel Classification ◽  
Bank Runs ◽  
Bank Performance ◽  
Estimation Result ◽  
Dynamic Panel ◽  
Full Sample ◽  
Dynamic Panel Model ◽  
Panel Model

This paper examine the determinant of bank runs in Indonesia that includes economic fundamental,bank performance, and self-fulfilling prophecy factors for all banks either in full sample periods between years 1990 - 2005 or in banking crisis periods between years 1997 - 1998. The bank runs determinant uses dynamic panel model from Arrelano-Bond. Estimation result shows that self-fulfilling prophecy, bank monetary performance which is rentability and fixed credit ratio and macroeconomic condition which is economic growth, inflation and real exchange rate influence bank runs in Indonesia. Bank runs determinant in banking crisis between year 1997 - 1998 also shows a result that is not really different from bank runs determinant during full sample periods between years 1990 - 2005.JEL Classification: C29, C33, G21Key Words: Bank runs, Banking Crisis, Arrelano Bond Dynamic Panel

scholarly journals Economic Diversity and the Resource Curse: A Dynamic Panel Model

2018 ◽  
Author(s):  
Fred Olayele ◽  
Kwok Soo
Keyword(s):  
Economic Growth ◽  
Resource Curse ◽  
Dynamic Panel Data ◽  
Policy Implications ◽  
Panel Data Models ◽  
Dynamic Panel ◽  
Dynamic Panel Model ◽  
Economic Diversity ◽  
Panel Model ◽  
The Impact

This paper contributes to the debate on the impact of economic diversity and the resource curse on economic growth. We use dynamic panel data models on data on Canadian and US sub-national jurisdictions. We find evidence for a positive relationship between diversity and growth. Based on the Krugman Specialization Index, our analysis shows that the required threshold for not having the resource curse is 0.209. Above this threshold, the marginal contribution of natural resources to economic growth is lower for a more diversified regional economy than a less diversified one. We highlight the policy implications of these findings.

scholarly journals Insurance Development and Economic Growth

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
HuiShan Lee
Keyword(s):  
Economic Growth ◽  
Gdp Per Capita ◽  
Dynamic Panel ◽  
Income Levels ◽  
Dynamic Panel Model ◽  
Insurance Sector ◽  
Panel Model ◽  
Per Capita ◽  
The Relationship ◽  
Insurance Development

This study aims to investigate the effect of continent and initial GDP per capita level of a country on the relationship between insurance activities and economic growth. This study considers panel data consists of 123 countries from 1967 to 2014. Both static panel model and dynamic panel model are used to evaluate the effect of both continent and initial GDP per capita level of a country to the economic growth. The findings show significant causal relationship between insurance development and economic growth. However, the relationship is varied in different countries due to different initial income levels and locations. The effect of insurance development on economic growth of a country is indirect because it depends on the performance of the investment of insurers. Therefore, policymakers should consider their own country’s special characteristics when formulating a policy. Policymakers should clearly understand the nature of their insurance sector such as interconnectedness between financial sector and insurance sector, whether to promote insurance sector to grow their economy. By understanding the effect of continent and the initial GDP per capita level, policymakers could formulate and implement more effective policies on their country’s insurance sector to ensure the prosperity of the country’s economic growth.

scholarly journals Profitability and Its Determinants in Turkish Manufacturing Industry: Evidence from a Dynamic Panel Model

2017 ◽  
Vol 9 (8) ◽  
pp. 66 ◽  
Author(s):  
Ozcan Isik ◽  
Umit Firat Tasgin
Keyword(s):  
Economic Growth ◽  
Firm Size ◽  
Financial Risk ◽  
Manufacturing Industry ◽  
Stock Exchange ◽  
Dynamic Panel Data ◽  
Working Capital ◽  
Dynamic Panel ◽  
Dynamic Panel Model ◽  
Panel Model

Our paper empirically analyses the factors that determine the profitability of 120 manufacturing firms listed in Borsa Istanbul Stock Exchange during the period 2005-2012. Estimation results from dynamic panel data model taking into account the endogeneity of variables indicate that lagged profitability, firm size, financial risk, R&D costs, net working capital, and economic growth are the most important variables affecting firm profitability. More specifically, profitability is positively and significantly affected by past profitability, firm size in terms of total sales, net working capital, and economic growth. On the other hand, R&D costs and financial risk have a dampening effect on the profitability.

scholarly journals Determinants of Economic Growth in Indonesia: A Dynamic Panel Model

2020 ◽  
Vol 7 (11) ◽  
pp. 147-156
Author(s):  
Agus Tri BASUKI ◽  
◽  
Yunastiti PURWANINGSIH ◽  
Albertus Maqnus SOESILO ◽  
Mulyanto MULYANTO
Keyword(s):  
Economic Growth ◽  
Dynamic Panel ◽  
Dynamic Panel Model ◽  
Panel Model ◽  
Determinants Of Economic Growth

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.

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