Journal articles: 'Dynamic threshold panel'

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Ramírez-Rondán, N. R. "Maximum likelihood estimation of dynamic panel threshold models." Econometric Reviews 39, no. 3 (June 14, 2019): 260–76. http://dx.doi.org/10.1080/07474938.2019.1624401.

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Seo, Myung Hwan, Sueyoul Kim, and Young-Joo Kim. "Estimation of dynamic panel threshold model using Stata." Stata Journal: Promoting communications on statistics and Stata 19, no. 3 (September 2019): 685–97. http://dx.doi.org/10.1177/1536867x19874243.

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In this article, we develop a command, xthenreg, that implements the first-differenced generalized method of moments estimation of the dynamic panel threshold model that Seo and Shin (2016, Journal of Econometrics 195: 169–186) proposed. Furthermore, we derive the asymptotic variance formula for a kink-constrained generalized method of moments estimator of the dynamic threshold model and provide an estimation algorithm. We also propose a fast bootstrap algorithm to implement the bootstrap for the linearity test. We illustrate the use of xthenreg through a Monte Carlo simulation and an economic application.

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Hu, Yi, Dongmei Guo, Ying Deng, and Shouyang Wang. "Estimation of Nonlinear Dynamic Panel Data Models with Individual Effects." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/672610.

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This paper suggests a generalized method of moments (GMM) based estimation for dynamic panel data models with individual specific fixed effects and threshold effects simultaneously. We extend Hansen’s (Hansen, 1999) original setup to models including endogenous regressors, specifically, lagged dependent variables. To address the problem of endogeneity of these nonlinear dynamic panel data models, we prove that the orthogonality conditions proposed by Arellano and Bond (1991) are valid. The threshold and slope parameters are estimated by GMM, and asymptotic distribution of the slope parameters is derived. Finite sample performance of the estimation is investigated through Monte Carlo simulations. It shows that the threshold and slope parameter can be estimated accurately and also the finite sample distribution of slope parameters is well approximated by the asymptotic distribution.

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Sotima Jocelyn, KOUSSERE. "INFLATION GROWTH NEXUS IN WAEMU: A DYNAMIC PANEL THRESHOLD ANALYSIS." International Journal of Social Science and Economic Research 5, no. 12 (December 30, 2020): 3721–30. http://dx.doi.org/10.46609/ijsser.2020.v05i12.002.

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Zhao, Wei Tao, Tian Jun Yu, and Xiao Dong Yang. "Flutter Analysis of Viscoelastic Panels in Supersonic Flow." Advanced Materials Research 710 (June 2013): 256–59. http://dx.doi.org/10.4028/www.scientific.net/amr.710.256.

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In this paper, dynamic instability behavior of a linear viscoelastic panel in supersonic flow is investigated. The quasi-steady piston theory of supersonic flow is employed for the aerodynamic pressure. The partial differential governing equation of isotropic flat panel is derived by introducing viscoelastic structural damping based on Kelvins model. The panel governing equation is transformed into a set of ordinary differential equations via the Galerkin approach. First-order state equations are afterwards obtained and solved by means of a standard eigenvalue calculation. The dynamic instability of viscoelstic panels is predicted by the feature of characteristic roots. The phenomena of coupled-mode flutter without structural damping and single-mode flutter with structural damping induced by the supersonic flow are observed for the different dynamic pressure values. Results indicate that structural damping plays an important role for the stability of panels flutter. Flutter threshold keeps decreasing as viscoelastic structural damping is increased.

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Ramos-Herrera, María del Carmen, and María A. Prats. "Fiscal Sustainability in the European Countries: A Panel ARDL Approach and a Dynamic Panel Threshold Model." Sustainability 12, no. 20 (October 15, 2020): 8505. http://dx.doi.org/10.3390/su12208505.

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We analyze the fiscal sustainability hypothesis for a panel of 20 European Union countries from 2000 to 2019. In particular, we employ new econometric methodologies that, to the best of our knowledge, are applied for the first time to the study of sovereign fiscal policy sustainability in these economies. Specifically, we estimate the panel ARDL technique, distinguishing between short- and long-run coefficients because the order of integration of our variables is not the same. Moreover, a panel threshold model with endogeneity is considered to investigate whether, departing from a particular threshold, there is different behavior between the government primary balance and public debt, both taken as a ratio of potential GDP. Finally, the panel Granger causality test is implemented to determine the direction of causality or the existence of bidirectional causality.

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Tan, Sai. "The Dynamic Panels With Threshold Effect of China's OFDI on Host Country Technological Progress." International Journal of Information Systems in the Service Sector 14, no. 2 (April 2022): 1–12. http://dx.doi.org/10.4018/ijisss.290544.

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Using the dynamic panel threshold model and data of 41 countries along “the Belt and Road” from 2003 to 2017, this paper empirically tests the impact of China’s outward foreign direct investment (OFDI) on technological progress of these countries and its threshold effect. The results show that OFDI exerts different effects on technological progress of countries along the route within different thresholds range. Based on the classification of different country types, when FD is below than the threshold value, China’s OFDI in transitional and developing countries has no significant impact on their technological progress, while developed countries have a more significant positive correlation; other, when FD is higher than the threshold value, both types of countries are significantly positively correlated, and the coefficient is greater in developed countries than in transitional and developing countries. In further research, it is divided into two periods based on the time node of 2013.

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Kremer, Stephanie, Alexander Bick, and Dieter Nautz. "Inflation and growth: new evidence from a dynamic panel threshold analysis." Empirical Economics 44, no. 2 (March 14, 2012): 861–78. http://dx.doi.org/10.1007/s00181-012-0553-9.

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Dang, Viet Anh, Minjoo Kim, and Yongcheol Shin. "Asymmetric capital structure adjustments: New evidence from dynamic panel threshold models." Journal of Empirical Finance 19, no. 4 (September 2012): 465–82. http://dx.doi.org/10.1016/j.jempfin.2012.04.004.

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Sirag, Abdalla, Bolaji Tunde Matemilola, Siong Hook Law, and A. N. Bany-Ariffin. "Does environmental Kuznets curve hypothesis exist? Evidence from dynamic panel threshold." Journal of Environmental Economics and Policy 7, no. 2 (October 4, 2017): 145–65. http://dx.doi.org/10.1080/21606544.2017.1382395.

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Oloo, Michael, Mary Mbithi, and Martin Oleche. "Threshold Effect of Macroeconomic Convergence Criteria on Real GDP Growth Rate within the East African Community." European Journal of Development Studies 2, no. 2 (March 16, 2022): 11–25. http://dx.doi.org/10.24018/ejdevelop.2022.2.2.67.

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This study investigates how the thresholds set by the East African Monetary Committee (EAMC) have impacted the growth of the East African Countries (Kenya, Tanzania, Uganda, Rwanda, and Burundi). The analysis establishes the actual thresholds supported by a panel data model, running from 2005 to 2020, which are drawn from World Development Indicators data from the World Bank website. The actual thresholds obtained are compared to the thresholds adopted by EAMC. This data was analysed using a Dynamic Threshold Panel model; the results show a slight deviation of the actual (optimal) data thresholds from the thresholds adopted as the EAC criteria for the formation of a Monetary Union. Therefore, there is need to reconsider the thresholds criteria to enable them to become feasible for the member states to achieve so as to form the Monetary Union.

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Zaghdoudi, Taha. "Threshold Effect in the Relationship Between External Debt and Economic Growth: A Dynamic Panel Threshold Specification." Journal of Quantitative Economics 18, no. 2 (September 13, 2019): 447–56. http://dx.doi.org/10.1007/s40953-019-00182-y.

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Sirag, Abdalla, and Norashidah Mohamed Nor. "Out-of-Pocket Health Expenditure and Poverty: Evidence from a Dynamic Panel Threshold Analysis." Healthcare 9, no. 5 (May 3, 2021): 536. http://dx.doi.org/10.3390/healthcare9050536.

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The current study investigated the association between out-of-pocket health expenditure and poverty using macroeconomic data from a sample of 145 countries from 2000 to 2017. In particular, it was examined whether the relationship between out-of-pocket health expenditure and poverty was contingent on a certain threshold level of out-of-pocket health spending. The dynamic panel threshold method, which allows for the endogeneity of the threshold regressor (out-of-pocket health expenditure), was used. Three indicators were adopted as poverty measures, namely the poverty headcount ratio, the poverty gap index, and the poverty gap squared index. At the same time, out-of-pocket health expenditure was measured as a percentage of total health expenditure. The results showed the validity of the estimated threshold models, indicating that only beyond the turning point, which was about 29 percent, that out-of-pocket health spending led to increased poverty. When heterogeneity was controlled for in the sample, using the World Bank income classification, the findings showed variations in the estimated threshold, with higher values for the low- and lower-middle-income groups, as compared to the high-income group. For the lower-income groups, below the threshold for out-of-pocket health expenditure, it had a positive or insignificant effect on poverty reduction, while it led to higher poverty above the threshold. Further, the sampled countries were divided into regions, according to the World Health Organization. Generally, improving health care systems through tolerable levels of out-of-pocket health expenditure is an inevitable step toward better health coverage and poverty reduction in many developing countries.

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Vukovic, Darko, Alexander Zobov, and Ekaterina Degtereva. "The nexus between tourism and regional real growth: dynamic panel threshold testing." Journal of the Geographical Institute Jovan Cvijic, SASA 72, no. 1 (2022): 111–16. http://dx.doi.org/10.2298/ijgi2201111v.

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This study analyzes the nexus between tourism and regional real growth for European regions at the Nomenclature of territorial units for statistics (NUTS), level 2, for the period 1995-2019. The study uses the dynamic panel threshold model to analyze complex relations between variables. As the dependent variable, we chose real growth rate of regional gross value added at basic prices by NUTS 2 regions. The independent variable is regional arrivals at tourist accommodation, while the control variables are health, household income, and employment at NUTS 2 regional level. The study found the threshold variable for 95% confidence interval. The marginal effects in the low inflation regime are higher compared to marginal effects in the high inflation regime. The study results support tourism-led growth hypothesis, indicating tourism as a one of the main drivers of regional growth. This research contributes to rare literature in application of dynamic panel threshold model in tourism. As an implication, this study can be used as a methodological approach to analyze the impact of different variables (not only tourism, but also innovations, technology, well-being, etc.) on regional growth, especially in countries with high regional differences, such as the Commonwealth of Independent States (CIS), Latin America, etc.

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Qasim Alabed, Qusai Mohammad, Fathin Faizah Said, Zulkefly Abdul Karim, Mohd Azlan Shah Zaidi, and Mohammed Daher Alshammary. "Energy–Growth Nexus in the MENA Region: A Dynamic Panel Threshold Estimation." Sustainability 13, no. 22 (November 11, 2021): 12444. http://dx.doi.org/10.3390/su132212444.

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This study provides new evidence regarding the nonlinear relationship between energy consumption and economic growth in the Middle East and North Africa (MENA) region for the 1990–2014 period. The empirical estimation is conducted using a dynamic panel threshold model. We found one threshold in the relationship between energy consumption and economic growth and one threshold in the relationship between carbon dioxide (CO2) emissions and economic growth. The results indicate that energy consumption positively and significantly affects economic growth in the low energy consumption regime. In contrast, it has a negative and significant impact on economic growth in the high energy consumption regime. Moreover, CO2 emissions are positively and significantly related to economic growth in the low regime of CO2 emissions. Nevertheless, the relationship between CO2 emissions and economic growth in the high CO2 emissions regime is negative and significant. Therefore, policymakers should implement other effective energy policies, such as stricter regulations on CO2 emissions, increase energy efficiency, and replace fossil fuels with cleaner energy sources to avoid unnecessary CO2 emissions and combat global warming. Future studies should identify the root causes of failures and issues in real time for inflation and link the energy–growth nexus to achieving the 2030 Sustainable Development Goals (SDGs) Agenda, Goal 7: Affordable and Clean Energy.

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Kurul, Zühal. "Nonlinear relationship between institutional factors and FDI flows: Dynamic panel threshold analysis." International Review of Economics & Finance 48 (March 2017): 148–60. http://dx.doi.org/10.1016/j.iref.2016.12.002.

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Vinayagathasan, Thanabalasingam. "Inflation and economic growth: A dynamic panel threshold analysis for Asian economies." Journal of Asian Economics 26 (June 2013): 31–41. http://dx.doi.org/10.1016/j.asieco.2013.04.001.

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Gørgens, Tue, and Allan H. Würtz. "Threshold Regression with Endogeneity for Short Panels." Econometrics 7, no. 2 (May 22, 2019): 23. http://dx.doi.org/10.3390/econometrics7020023.

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This paper considers the estimation of dynamic threshold regression models with fixed effects using short panel data. We examine a two-step method, where the threshold parameter is estimated nonparametrically at the N-rate and the remaining parameters are estimated by GMM at the N -rate. We provide simulation results that illustrate advantages of the new method in comparison with pure GMM estimation. The simulations also highlight the importance of the choice of instruments in GMM estimation.

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Bandura, Witness, and Temitope Leshoro. "Inflation and financial development in sub-Saharan Africa." Ekonomski anali 67, no. 233 (2022): 85–112. http://dx.doi.org/10.2298/eka2233085b.

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This paper analyses the relationship between inflation and financial development in sub-Saharan Africa. This issue is important because the level beyond which the inflation rate affects financial development in sub-Saharan Africa is not known, despite the underdeveloped financial system in the region. This paper presents a model in which inflation is endogenously determined, and uses a dynamic panel threshold approach which takes this factor into consideration to ensure improved results. The pure cross-section method and non-dynamic panel threshold approaches were also utilised. Strong evidence of a negative impact of inflation on financial development is obtained, which increases with a rise in inflation. An average inflation threshold of about 5% is established, below which a positive impact on financial development is found. There is, however, a negative relationship beyond the observed threshold. The paper provides policy recommendations as to how authorities should contain inflation to facilitate financial sector development in the region.

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Kon, Yee-Qin, Mui-Yin Chin, and Sheue-Li Ong. "The Threshold Level of Economic Growth in BRI and Non-BRI Developing Countries for Better Environmental Quality." Jurnal Institutions and Economies 15, no. 1 (January 1, 2023): 1–25. http://dx.doi.org/10.22452/ijie.vol15no1.1.

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This study investigates the nexus between economic growth and carbon dioxide (CO2) emissions. Specifically, this study employs a dynamic panel threshold model to identify and compare the threshold levels of economic growth between developing countries participating and not participating in the Belt and Road Initiative (BRI). The findings signify a nonlinear relationship between the sampled countries’ economic growth and their CO2 emissions in both categories. Furthermore, the result of dynamic panel threshold model reveals that the threshold level of economic growth, in the category of BRI developing countries, was lower (US$ 887.49) as compared to non-BRI developing countries (US$ 1358.57). This implies that the impact of economic growth in developing countries participating in the BRI was less harmful to the environment, and that these countries are more alert to environmental issues once the threshold of their gross domestic product per capita was achieved. Hence, Chinese authorities should put in more effort in facilitating sustainable infrastructure development to attract more developing countries to join the BRI to mitigate environmental degradation.

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Abdulqadir, Idris Abdullahi, and Soo Y. Chua. "Asymmetric impact of exchange rate pass-through into employees' wages in sub-Saharan Africa: panel non-linear threshold estimation." Journal of Economic Studies 47, no. 7 (April 28, 2020): 1629–47. http://dx.doi.org/10.1108/jes-03-2019-0128.

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PurposeThe purpose of this article is to investigate the asymmetric impact of exchange rate pass-through (ERPT) on employees' wages via consumer prices in 15 major oil-exporting countries from sub-Saharan Africa over the period 1996-2017 using the panel threshold regression model.Design/methodology/approachThe methodology used in this article was built on non-linear panel threshold regression models developed by Hansen (1996, 1999) threshold regression. The authors first tested for the existence of threshold-effect in ERPT and wage nexus using 1,000 bootstrap replications and 400 grid searches to obtain an optimal threshold. We also estimated that asymmetric ERPT on employees' wages reacts differently when the inflation-threshold exceeds beyond a 15.12% threshold level.FindingsOur findings showed that asymmetric ERPT is incomplete and indicates that an increase by one standard deviation in real exchange rate causes a decline in employees' wages by 2.69%.Research limitations/implicationsThe policy implications of our results are drawn from the significant threshold estimates. However, a significant threshold value of 15.12 is an inflation-threshold estimates that split our 330 observations into the lower (upper) regimes. Further, an inflation rate beyond the threshold value is likely to have an asymmetric ERPT on employees' wages in the 15 major oil-exporting sub-Saharan African (SSA) countries.Practical implicationsThe practical implication of the study is when ERPT exceeds the threshold, the effect of real exchange rate variations is passed on to employees' wages. It is widely believed that labor productivity increase with increased minimum wages. Nevertheless, there is contention as regards the effects on employment and poverty. As rising goods prices make the minimum wage increased homogeneous of degree zero.Social implicationsConsiderable increased ERPT on imported goods reduces employees' wages purchasing ability from import-dependent countries through import prices. Once it has documented, this also reduces welfare via deteriorations of marginal propensity to consume (MPC) and marginal propensity to savings (MPS).Originality/valueThis article integrates labor purchasing power into the analysis of ERPT using non-linear dynamic panel heterogeneous threshold regression. It extends the Hansen (1996, 1999) dynamic panel threshold models to exchange rate pass-through in SSA economies.

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Matemilola, Bolaji Tunde, A. N. Bany-Ariff, W. N. W. Azman-Sain, and Annuar Md Nassir. "Non-linearity in Debt and Return Relationship: Evidence from Dynamic Panel Threshold Method." Journal of Applied Sciences 16, no. 9 (August 15, 2016): 438–44. http://dx.doi.org/10.3923/jas.2016.438.444.

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Ho, Sy-Hoa, and Jamel Saadaoui. "Bank credit and economic growth: A dynamic threshold panel model for ASEAN countries." International Economics 170 (August 2022): 115–28. http://dx.doi.org/10.1016/j.inteco.2022.03.001.

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Zare, Roohollah. "Nonlinear Impacts of Public Debt on Economic Growth: A Dynamic Panel Threshold Approach." Journal of Planning and Budgeting 24, no. 4 (February 1, 2020): 43–56. http://dx.doi.org/10.29252/jpbud.24.4.43.

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Liang, Kai Qiao. "A Positive Study of Risk Behavior Based on Dynamic Panel Data." Applied Mechanics and Materials 522-524 (February 2014): 887–91. http://dx.doi.org/10.4028/www.scientific.net/amm.522-524.887.

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The paper studies risk behavior and capital buffers of Chinas commercial banks under capital regulatory whose core is capital adequacy ratio. We find that the regulatory pressure doesnt significantly affect the risk behavior of commercial banks. It doesnt affect the capital adjustment of commercial banks which have already violated regulatory capital requirement. Whereas, commercial bank which doesnt violate the regulatory requirement but approaching the threshold, will react to this pressure by fine-tuning of supplementary capital to increase its capital adequacy ratio.

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Hu, Jiangfeng, Zhao Wang, Qinghua Huang, and Xiaoqin Zhang. "Environmental Regulation Intensity, Foreign Direct Investment, and Green Technology Spillover—An Empirical Study." Sustainability 11, no. 10 (May 14, 2019): 2718. http://dx.doi.org/10.3390/su11102718.

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Many researchers have studied the relationships among heterogeneous foreign direct investment (FDI), environmental regulation, and green total factor productivity. However, no research has been done on how different types of FDI can result in green technology spillover under different levels of environmental regulation intensity. To address this research gap, in this paper, we build a static linear panel model, a static panel threshold model, and a dynamic panel threshold model to investigate the environmental regulatory threshold effect of labor-based FDI and capital-based FDI in terms of their green technology spillover. Based on the measurement of green total factor productivity (GTFP) of 36 industry sectors in China from 2003 to 2015, we first compare the threshold effects of environmental regulation on green technology spillover between labor-based FDI and capital-based FDI with a static linear model and a static threshold model. The results show that environmental regulation is unable to significantly promote the green technology spillover of labor-based FDI. However, intensifying environmental regulation can reduce the negative impact of labor-based FDI on GTFP. The effect of environmental regulation on green technology spillover of capital-based FDI is more complex. In the static linear model, environmental regulation can significantly promote the green technology spillover of capital-based FDI. In the static threshold model, the green technology spillover of capital-based FDI exists only when the environmental regulation intensity is sufficiently low or sufficiently high. Finally, the dynamic threshold model is adopted for robustness check. The results show when the environmental regulation intensity is higher than a threshold, both types of FDI can indeed result in green technology spillover. In short, our results prove that to ensure that FDI results in green technology spillover, it is necessary to continue to strengthen environmental regulation.

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Zhang, Lu, Renyan Mu, Nigatu Mengesha Fentaw, Yuanfang Zhan, Feng Zhang, and Jixin Zhang. "Industrial Coagglomeration, Green Innovation, and Manufacturing Carbon Emissions: Coagglomeration’s Dynamic Evolution Perspective." International Journal of Environmental Research and Public Health 19, no. 21 (October 27, 2022): 13989. http://dx.doi.org/10.3390/ijerph192113989.

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The achievement of China’s low-carbon development and carbon neutrality depends heavily on the decrease of manufacturing carbon emissions. From coagglomeration’s dynamic evolution perspective, by using panel-threshold-STIRPAT and mediation-STIRPAT models, this study examines the relationships among industrial coagglomeration, green innovation, and manufacturing carbon emissions and explores the direct and indirect function mechanisms. Panel data of China’s 30 provinces from 2010 to 2019 are employed. The results imply that, first, the impact of industrial coagglomeration on manufacturing carbon emissions is nonlinear and has significant threshold effects. Industrial coagglomeration negatively affects manufacturing carbon emissions, and as the coagglomeration level deepens, the negative effect has a diminishing trend in marginal utility. Once the coagglomeration degree exceeds a certain threshold, the negative impact becomes insignificant. At present, for 90% of China’s regions, an increase in industrial coagglomeration level can help reduce manufacturing carbon emissions. Second, green innovation is a vital intermediary between industrial coagglomeration and manufacturing carbon emissions. It is a partial intermediary when industrial coagglomeration is at a relatively lower-level stage and a complete intermediary when industrial coagglomeration is at a relatively higher-level stage. These findings reveal the significance of optimizing industrial coagglomeration and the level and efficiency of green innovation to decrease carbon emissions.

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Lu, Xinhai, Jiao Hou, Yifeng Tang, Ting Wang, Tianyi Li, and Xupeng Zhang. "Evaluating the Impact of the Highway Infrastructure Construction and the Threshold Effect on Cultivated Land Use Efficiency: Evidence from Chinese Provincial Panel Data." Land 11, no. 7 (July 9, 2022): 1044. http://dx.doi.org/10.3390/land11071044.

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Highway infrastructure construction is regarded as one of the effective policy tools used to promote the flow of production factors and upgrade the industrial structure in China, and it may also be an important precondition to improving Cultivated Land Use Efficiency (CLUE). This paper uses a slack-based model (SBM) based on provincial-level panel data from China from 2004 to 2017 to measure CLUE. Then a dynamic spatial Durbin model and a panel threshold regression model were established to analyze the spatial spillover effect and threshold effect of highway infrastructure construction on CLUE. The results showed that the CLUE of China has a fluctuating but overall rising trend. The dynamic spatial Durbin model demonstrated that the Chinese government’s policy of supporting highway infrastructure construction has played an important role in promoting CLUE by spatial spillover effects, and the driving effect of expressways and first–second highways on CLUE is particularly significant in this regard. More interestingly, the results of the panel threshold regression indicated that there is a single threshold effect in the influence of highway infrastructure construction on CLUE. This paper suggested that the spatial correlation between regions should be considered in the construction of regional highway infrastructure and land use planning to improve CLUE. Moreover, the planning of highway infrastructure construction should be balanced according to the actual demand of economic and agricultural development, so as to promote the full flow and reasonable allocation of cultivated land use factors among regions.

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Ueno, H., S. Amano, B. Merecka, and J. Kośmider. "Difference in the odor concentrations measured by the triangle odor bag method and dynamic olfactometry." Water Science and Technology 59, no. 7 (April 1, 2009): 1339–42. http://dx.doi.org/10.2166/wst.2009.112.

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‘The triangle odor bag method’, which has been adopted for the offensive odor control law in Japan, and the dynamic olfactometry defined by EN 13725 have been compared. The odor concentration measured by the triangle odor bag method tends to be higher than that of the dynamic olfactometry in the forced choice mode, while well agreed in the Yes/No mode olfactometry when the panel is the same. The difference can be minimized by applying the panel selection criterion of EN13725 to the triangle odor bag method. The European panel selection test is useful to negate the difference in the measurement equipments although the criteria seem to be strict considering the individual threshold data of n-butanol.

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Zhou, Bing, Meng Peng, Yingxue Tan, Sidai Guo, Shengzhong Huang, and Bing Xue. "Dynamic Panel Threshold Model-Based Analysis on Equity Restriction and Enterprise Performance in China." Sustainability 11, no. 22 (November 18, 2019): 6489. http://dx.doi.org/10.3390/su11226489.

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This paper takes China’s A-share listed companies of the mixed ownership of state-owned enterprises from 2007 to 2016 as a sample, and examines the impact of state-owned business mixed reform on corporate performance. Research shows that under different equity restriction ratios, there exists a difference in the connection between corporate performance and equity restriction ratio. Corporate performance reduces with the subjoin of equity restriction ratio, and they are negatively correlated when the stockholding ratio of the largest stockholder is less than 25%; on the condition that the stockholding ratio of the largest stockholder is in the range of 25 and 40% and 40 and 60%, it presents an “inverted U-shaped” connection between corporate performance and equity restriction ratio. At this time, the threshold value of the optimal equity restriction ratio is 1.1336 and 0.7297, respectively. On the condition that the stockholding ratio of the largest stockholder is equal to or more than 60%, there exists no threshold value for equity restriction ratio. However, the regression results present that corporate performance increases with the increase of equity restriction ratio, and the two are positively correlated.

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Turan, Taner, and Pelin Varol Iyi̇dogan. "Estimating Fiscal Reaction Functions for Developing and Developed Countries: A Dynamic Panel Threshold Analysis." Ekonomický časopis 70, no. 5 (September 5, 2022): 393–410. http://dx.doi.org/10.31577/ekoncas.2022.05.01.

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Yu, Shiwei, Xing Hu, and Juan Yang. "Housing prices and carbon emissions: a dynamic panel threshold model of 60 Chinese cities." Applied Economics Letters 28, no. 3 (April 6, 2020): 170–85. http://dx.doi.org/10.1080/13504851.2020.1739612.

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Wu, Haitao, Yu Hao, and Jia-Hsi Weng. "How does energy consumption affect China's urbanization? New evidence from dynamic threshold panel models." Energy Policy 127 (April 2019): 24–38. http://dx.doi.org/10.1016/j.enpol.2018.11.057.

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Lay, Sok Heng. "Bank credit and economic growth: Short-run evidence from a dynamic threshold panel model." Economics Letters 192 (July 2020): 109231. http://dx.doi.org/10.1016/j.econlet.2020.109231.

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Zhang, Ming, Xiaorong Zou, and Long Sha. "Social Security and Sustainable Economic Growth: Based on the Perspective of Human Capital." Sustainability 11, no. 3 (January 28, 2019): 662. http://dx.doi.org/10.3390/su11030662.

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China’s social security expenditure has rapidly grown during the past decade, and concerns about the impact of social security on productivity and sustained economic growth have attracted attention. Based on Chinese provincial panel data over the period 2007–2016, a threshold model analysis found that the impact of social security on productivity has a “double threshold” on human capital. Using dynamic panel data models and system General Moment Method estimators also found the existence of this threshold effect: When the human capital level is low or high, social security is favorable for sustained economic growth. However, if the human capital level is at the intermediate level, the function of social security is weak. The main conclusions were still valid after we examined the robustness of our results with several methods.

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Olaoye, Olumide, and Oluwatosin Aderajo. "Institutions and economic growth in ECOWAS: an investigation into the hierarchy of institution hypothesis (HIH)." International Journal of Social Economics 47, no. 9 (August 11, 2020): 1081–108. http://dx.doi.org/10.1108/ijse-10-2019-0630.

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PurposeThe purpose of this paper is to examine the relationship between the quality of different dimensions of institutional and economic growth in a panel of 15 member ECOWAS.Design/methodology/approachThe study adopts Driscoll and Kraay′s nonparametric covariance matrix estimator, and the spatial error model to account for cross-section dependency, cross-country heterogeneity and spatial dependence inherent in empirical modelling, which has largely been ignored in previous studies. This is because, the likelihood that corruption and human capital cluster in space is very high because factors that affect these phenomena disperse across borders. Similarly, to test the threshold effect, the study adopts the more refined and more appropriate dynamic panel data which models a nonlinear asymmetric dynamics and cross-sectional heterogeneity, simultaneously, in a dynamic threshold panel data framework.FindingsThe empirical evidence supports findings by previous researchers that better-quality political and economic institutions can have positive effects on economic growth. Similarly, our results support a nonlinear relationship between political institutions and economic institution, confirming the “hierarchy of institution hypothesis” in ECOWAS. Specifically, the findings show that economic institutions will only have the desired economic outcome in ECOWAS, only when political institution is above a certain threshold.Originality/valueUnlike previous studies which assume cross-sectional and spatial independence, the authors account for cross-section dependency and cross-country heterogeneity inherent in empirical modelling.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2019-0630

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Attia, Eman Fathi, Hamsa hany Ezz Eldeen, and Sameh said Daher. "Size-Threshold Effect in the Capital Structure–Firm Performance Nexus in the MENA Region: A Dynamic Panel Threshold Regression Model." Risks 11, no. 2 (January 17, 2023): 23. http://dx.doi.org/10.3390/risks11020023.

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This paper investigates the nonlinear relationship between capital structure and firm performance in the MENA region using a sample of 499 listed firms over the 2007–2020 period, or 6986 firm-year observations. Specifically, we examine the size-threshold effect in the capital structure–firm performance nexus. To do so, this study applies a dynamic panel threshold regression model (DPTR). The findings show that there is a nonlinear relationship between debt and firm performance (Tobin’s Q, ROA, and ROE). Specifically, the threshold values of firm size for the three models are estimated at 9.126 (about $1 million), 15.48 (about $5 million), and 16.816 (about $20 million), respectively, between the low- and the high-sized regimes. In the lower regime, the firm’s value (Q) increases when debt increases; however, in the higher regime, this value decreases when debt increases. Furthermore, in the lower regime, the performances (ROA and ROE) of small firms decrease when debt increases; however, in the upper regime, when debt increases, the performances of large firms increase. The results are several robustness tests. These results support the predictions of signal, pecking order, and trade-off theories. Managers of large (small) MENA firms should increase (decrease) the use of debt to improve performance.

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Chen, Jing, and Liyuan Hu. "Does Environmental Regulation Drive Economic Growth through Technological Innovation: Application of Nonlinear and Spatial Spillover Effect." Sustainability 14, no. 24 (December 8, 2022): 16455. http://dx.doi.org/10.3390/su142416455.

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Based on provincial dynamic panel data from 2010 to 2019, this paper constructs a panel threshold model and explores the nonlinear relationship between environmental regulation and economic growth through channels of technological innovation. In addition, a spatial panel Durbin model is constructed to test the spatial spillover effects of environmental regulation and technological innovation on economic growth. The results show that environmental regulation has a significant positive impact on economic growth through channels of technological innovation. The interaction between environmental regulation and technological innovation has a single threshold effect on economic growth. When the intensity of environmental regulation exceeds the critical value, environmental regulation will change from promoting economic growth to inhibiting economic growth through channels of technological innovation. Environmental regulation has negative spatial spillover effect on economic growth of neighboring regions, while technological innovation has positive spatial spillover effect on economic growth of neighboring regions.

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N’Guessan, C. F. J. "The Effects of Inflation on Employment in African Countries: A Non-dynamic Panel Threshold Approach." Studies in Economics and Econometrics 42, no. 1 (April 1, 2018): 135–50. http://dx.doi.org/10.1080/10800379.2018.12097330.

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Aydın, Celil, Ömer Esen, and Metin Bayrak. "Inflation and Economic Growth: A Dynamic Panel Threshold Analysis for Turkish Republics in Transition Process." Procedia - Social and Behavioral Sciences 229 (August 2016): 196–205. http://dx.doi.org/10.1016/j.sbspro.2016.07.129.

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Hung, Shiu-Wan, Chiao-Ming Li, and Joe-Ming Lee. "Firm growth, business risk, and corporate social responsibility in Taiwan’s food industry." Agricultural Economics (Zemědělská ekonomika) 65, No. 8 (August 26, 2019): 366–74. http://dx.doi.org/10.17221/339/2018-agricecon.

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This study applies the multiple-regime panel smooth transition regression (MR-PSTR) estimation to investigate the non-linear dynamic relationship between a firm’s corporate social responsibility (CSR) and firm growth in the food industry under the size threshold, using a panel of 30 Taiwanese food firms during 2007–2017. Our empirical results reveal that firm growth and firm size present a non-linear relationship, and that firm growth is different under the size threshold value and the control variables of firm age, CSR, R&D innovation, and finance policy. Corporate social responsibility helps reduce business risk. Regardless of the type and size, for many food industries, CSR is no longer an arbitrary choice, but an essential requirement.<br />

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Hou, Jian, Jiancheng Chen, Hongfeng Song, and Gang Wang. "Are Non-R&D Innovation Activities Actually Effective for Innovation Sustainability? Empirical Study from Chinese High-Tech Industry." Sustainability 11, no. 1 (December 31, 2018): 174. http://dx.doi.org/10.3390/su11010174.

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Compared with developed countries, the paper explores whether non-R&D innovation activities in China actually are effective and provides a guidance on how we can choose a sustainable innovation mode for non-R&D, especially considering the “threshold effect” of the heterogeneity of different regional innovation levels. The dynamic threshold panel models of the non-R&D (NRD) effect on the basis of the threshold of regional innovation level is constructed to verify the complex “threshold effect” characteristics between non-R&D and innovation performance. The empirical results are discussed according to the panel data for 30 provinces in China concerning the high-tech industry. Results argue that the mechanism of non-R&D innovation activities on innovation performance have a significantly different “threshold effect.” Interestingly, when the threshold of regional innovation keeps a low level, the negative effect of non-R&D innovation activities is much larger. When the threshold level of regional innovation increases, reaching the critical mass, the negative impact of non-R&D innovation activities on innovation performance becomes smaller. However, once the regional innovation level crosses the critical mass, the negative impact of non-R&D shows a significantly increasing tendency. Specifically, neither much higher nor much lower regional innovation level is conducive to the promoting effect of non-R&D activities. The negative impact of non-R&D on performance will decrease to the minimum only in the regions within the moderate threshold level (critical mass). The dynamic nonlinear mechanism between non-R&D activities and innovation performance is empirically studied to assist high-tech enterprises for innovation sustainability based on heterogeneity of different regional innovation levels.

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Chen, Heng, Jian Hou, and Wei Chen. "Threshold Effect of Knowledge Accumulation between Innovation Path and Innovation Performance: New Evidence from China’s High-tech Industry." Science, Technology and Society 23, no. 1 (January 8, 2018): 163–84. http://dx.doi.org/10.1177/0971721817744459.

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In the process of transitioning from closed to open innovation, regions in developing countries need to understand how to choose the most effective path within the complex innovation system while considering their own innovation factors. Based on provincial panel data from China’s high-tech industry and the improved dynamic threshold model, we introduce the threshold of knowledge accumulation (KLA) into the non-linear mechanism between innovation paths and innovation performance to compare the dynamic threshold effect and its heterogeneity. This research provides interesting insights into innovation paths, showing that the relationship between the innovation path and innovation performance is significantly influenced by the threshold effect of KLA. As the level of KLA strengthens, its effects on each innovation path change. Overall, this article shows how KLA affects the relationship between the innovation path and innovation performance. The article concludes with a discussion of the implications of these insights for innovation management and policy.

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Allegret, Jean-Pierre, and Sana Azzabi. "Finance, growth and threshold effects." Panoeconomicus 59, no. 5 (2012): 553–81. http://dx.doi.org/10.2298/pan1205553a.

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In this paper, we test the existence of financial development threshold effects, firstly, between financial development and long-term growth, and, secondly, between financial development and long-term GDP. We also ask whether such effects may explain the link financial development - convergence/ divergence to the advanced countries? growth. Our work builds on that of Aghion, Howitt, and Mayer-Foulkes (2004). It differs from previous work about assumptions and methodology. Estimates are performed with GMM dynamic panel data techniques for 112 emerging and developing countries from 1975 to 2007. The results show a positive but vanishing effect of financial development on steady-state GDP, from a critical (an average) level of financial development. They do not validate, however, the assumption that the marginal impact of financial development on the steady-state growth rate is more favorable than the degree of financial development is low. We support only partially the role that the financial development could play in the acceleration of the convergence of emerging and developing economies towards the world frontier growth.

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Sirag, Abdalla, Norashidah Nor, and Siong Law. "Does higher longevity harm economic growth?" Panoeconomicus 67, no. 1 (2020): 51–68. http://dx.doi.org/10.2298/pan150816015s.

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This study contributes to economic growth literature by providing new evidence on the relationship between life expectancy and economic growth utilising the recently developed dynamic panel threshold estimator. The sample of this study contains a total of 112 developed and developing countries covering the period from 1981 to 2010. The findings indicate the existence of a non-linear relationship between life expectancy and economic growth. In particular, life expectancy is useful for economic growth but only up to a certain threshold level; any further increase in longevity above the threshold would adversely affect growth. These findings emphasise the role of demographic transition in explaining the relationship between health and economic growth.

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Ogbaro, Eyitayo O. "Threshold Effects of Institutional Quality in the Infrastructure-Growth Nexux." Journal of Quantitative Methods 3, no. 2 (August 31, 2019): 45–61. http://dx.doi.org/10.29145/2019/jqm/030203.

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This study estimates the threshold level of institutional quality that will ensure the efficient use of infrastructure in stimulating growth in Sub-Saharan Africa based on panel data from 41 countries in the region between 1996 and 2015. It employs a dynamic panel threshold regression model which is derived from the New Institutional Economics theory and this is estimated using the first-differenced Generalized Method of Moments estimator. Results reveal that the relationship between infrastructure and growth is non-linear which provides support for the use of a threshold regression model, with institutional quality serving as the threshold variable. In terms of the threshold level, the findings show that the index of institutional quality that will ensure the efficient use of infrastructure in stimulating growth is 0.410. The study also finds that, on average, countries in the region operate below this threshold level, hence their poor growth. The conclusion that is drawn from the analysis is that poor or low institutional quality is one of the factors hampering the growth of countries in the SSA region. A major limitation of the study is that the estimator employed for the threshold analysis is developed for models with single threshold value only and so does not allow for multiple threshold values. Thus, it is recommended that governments in the region need to formulate and implement policies targeted at improving the level of institutional quality in their countries.

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Liu, Yong, Jun-liang Du, Ren-Shi Zhang, and Jeffrey Yi-Lin Forrest. "Three way decisions based grey incidence analysis clustering approach for panel data and its application." Kybernetes 48, no. 9 (October 7, 2019): 2117–37. http://dx.doi.org/10.1108/k-08-2018-0445.

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PurposeThis paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.Design/methodology/approachBecause of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.FindingsThe results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.Practical implicationsThe novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.Originality/valueThe proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.

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Bolarinwa, Segun Thompson, Richard Olaolu Olayeni, and Xuan Vinh Vo. "Is there a nonlinear relationship between nonperforming loans and bank profitability? Evidence from dynamic panel threshold." Managerial and Decision Economics 42, no. 3 (February 2, 2021): 649–61. http://dx.doi.org/10.1002/mde.3262.

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Nouira, Ridha, and Mohamed Kouni. "Optimal government size and economic growth in developing and MENA countries: A dynamic panel threshold analysis." Middle East Development Journal 13, no. 1 (January 2, 2021): 59–77. http://dx.doi.org/10.1080/17938120.2021.1898231.

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Okunade, Solomon Oluwaseun. "Institutional threshold in the nexus between financial openness and TFP in Africa: A dynamic panel analysis." Social Sciences & Humanities Open 5, no. 1 (2022): 100245. http://dx.doi.org/10.1016/j.ssaho.2021.100245.

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Journal articles on the topic 'Dynamic threshold panel'

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