Adaptive LASSO for selecting Fourier coefficients in a functional smooth time-varying cointegrating regression: An application to the Feldstein–Horioka puzzle

Neto, David

doi:10.1016/j.matcom.2020.08.011

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…To adjust for skewing commonly seen with count data, we followed Ma et al [36] in using Chebyshev polynomials transformation to normalize the distribution of all LENA-generated measures [115]. We chose the final Chebyshev polynomials model used to transform the data by using least absolute shrinkage and selection operator (LASSO) regression models [116]. We then predicted residuals using the final Chebyshev polynomials model.…”

Section: Plos Onementioning

confidence: 99%

Factors of parental investment in the home language environment in peri-urban China: A mixed methods study

Feng,

Guo,

Dill

et al. 2023

PLoS ONE

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The home language environment is a critical point of investment in early language skills. However, few studies have quantitatively measured the home language environment of low-socioeconomic-status households in non-western settings. This mixed methods study describes the home language environment and early child language skills among households in a low-socioeconomic-status, peri-urban district of Chengdu, China, and identifies factors influencing parental investment in the home language environment. Audio recordings were collected from 81 peri-urban households with children ages 18–24 months and analysed using the Language Environment Analysis (LENATM) system. The Mandarin version of the MacArthur-Bates Communicative Development Inventory was administered to each child’s primary caregiver. The quantitative results revealed large variation in home language environments and child language skills among the sample, with relatively low average scores when compared to other Chinese samples. Qualitative interviews with a subset of 31 caregivers revealed that many caregivers face constraints on their knowledge of interactive parenting, compounded, in some households, by time constraints due to work or household responsibilities. The findings indicate a need for increased sources of credible parenting information for peri-urban caregivers of young children to promote investment in the home language environment.

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Section: Plos Onementioning

confidence: 99%

Factors of parental investment in the home language environment in peri-urban China: A mixed methods study

Feng,

Guo,

Dill

et al. 2023

PLoS ONE

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“…vanced econometric techniques: the fully modified ordinary least squares and th namic ordinary least squares. The application of fully modified ordinary least squ supported by the findings of Gold et al [139], Young [140], and Maydeu-Olivares [141], while dynamic ordinary least squares is backed by the research of González res and Guizar [142] and Neto [143]. These methods are sophisticated adaptations classic least squares technique, devised to effectively tackle the endogeneity that arise in regressors from cointegration links and also to manage the impact of serial lation.…”

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confidence: 98%

Unraveling Korea’s Energy Challenge: The Consequences of Carbon Dioxide Emissions and Energy Use on Economic Sustainability

Li,

2024

Sustainability

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This research delves into the effects of carbon dioxide emissions and energy consumption on economic growth in Korea from 1980 to 2022, employing a sophisticated nonlinear autoregressive distributed lag model. The study unveils pivotal findings, most notably the positive association between carbon dioxide emissions and economic growth, suggesting that periods of economic expansion in Korea have been accompanied by surges in emissions. Furthermore, the investigation highlights a significant, albeit asymmetrical, positive impact of primary energy consumption on economic growth, illuminating the critical role of energy in the nation’s economic trajectory. The analysis also identifies essential economic determinants—namely, the labor force, gross fixed capital formation, and net inflows of foreign direct investment—underscoring their pivotal contributions to economic proliferation. The reliability of these insights is corroborated through advanced econometric techniques, including fully modified ordinary least squares and dynamic ordinary least squares methods. A noteworthy discovery emerges from the Toda–Yamamoto causality test, revealing bidirectional Granger causality between carbon dioxide emissions and economic growth, as well as between energy consumption and economic growth. Moreover, it uncovers a unidirectional causality flowing from labor, capital formation, and foreign direct investment towards economic growth. These findings elucidate the complex interplay between environmental and economic elements, highlighting the critical need for sustainable energy policies and proactive environmental stewardship in Korea. By advocating for a synthesis of economic advancement and environmental sustainability, this study presents indispensable insights for policymakers. It calls for a judicious approach to balancing Korea’s economic ambitions with its ecological responsibilities, thereby charting a sustainable path forward for the nation. Through its comprehensive analysis, this research contributes valuable perspectives to the discourse on economic development and environmental sustainability, offering guidance for Korea’s strategic planning and policy formulation.

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Multi-target regression via self-parameterized Lasso and refactored target space

Xiao

2021

Appl Intell

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Adaptive LASSO for selecting Fourier coefficients in a functional smooth time-varying cointegrating regression: An application to the Feldstein–Horioka puzzle

Cited by 8 publications

References 31 publications

Factors of parental investment in the home language environment in peri-urban China: A mixed methods study

Factors of parental investment in the home language environment in peri-urban China: A mixed methods study

Unraveling Korea’s Energy Challenge: The Consequences of Carbon Dioxide Emissions and Energy Use on Economic Sustainability

Multi-target regression via self-parameterized Lasso and refactored target space

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