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Relevant bibliographies by topics / Dynamic threshold panel

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Journal articles

Academic literature on the topic 'Dynamic threshold panel'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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Journal articles on the topic "Dynamic threshold panel"

1

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

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2

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 (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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3

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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4

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

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5

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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6

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 (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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7

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 (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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8

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

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9

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 (2012): 465–82. http://dx.doi.org/10.1016/j.jempfin.2012.04.004.

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10

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 (2017): 145–65. http://dx.doi.org/10.1080/21606544.2017.1382395.

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