首页
学术科研
-
Adult children's education and elderly parents' energy poverty: Evidence from China
This paper investigates the intergenerational impact of adult children's education on elderly parents' energy poverty. Utilizing data from the China Family Panel Studies (CFPS), we find that higher levels of children's education significantly reduce parents' energy poverty. Our results are robust across various measures of energy poverty and children's education. To address the issue of endogeneity, we employ China's Compulsory Education Law as an instrumental variable for children's education. Heterogeneity tests show that the reduction effect is more pronounced in rural areas compared to urban areas, in families with male children relative to those with female children, in parents with lower levels of education, and in regions with higher educational quality. Intergenerational financial transfers, the digital divide, and social capital are identified as three potential underlying mechanisms through which children's education affects parents' energy poverty. These findings offer a new perspective on the driving forces behind energy poverty, underscoring the significance of intergenerational effects.
-
Do investors pay attention to the long-term sustainable company development?
This study investigates whether the green concerns expressed by retail investors on online platforms affect the environmental, social, and governance (ESG) performance of companies in China's A-share market. The implementation of China's dual carbon targets has received significant public support. Investors in the capital market are increasingly emphasizing the green initiatives of the companies they invest in. This creates external pressure on publicly listed firms. Results indicate that retail investors' attention to environmental sustainability significantly enhances companies' ESG performance, primarily by boosting their market reputation. The effect is more pronounced in companies from regions with lower marketization levels, those that are private enterprises, and those in the manufacturing sector.
-
Optimal starting point for time series forecasting
Recent advances on time series forecasting mainly focus on improving the forecasting models themselves. However, when the time series data suffer from potential structural breaks or concept drifts, the forecasting performance might be significantly reduced. In this paper, we introduce a novel approach called Optimal Starting Point Time Series Forecast (OSP-TSP) for optimal forecasting, which can be combined with existing time series forecasting models. By adjusting the sequence length via leveraging the XGBoost and LightGBM models, the proposed approach can determine the optimal starting point (OSP) of the time series and then enhance the prediction performances of the base forecasting models. To illustrate the effectiveness of the proposed approach, comprehensive empirical analysis have been conducted on the M4 dataset and other real world datasets. Empirical results indicate that predictions based on the OSP-TSP approach consistently outperform those using the complete time series dataset. Moreover, comparison results reveals that combining our approach with existing forecasting models can achieve better prediction accuracy, which also reflect the advantages of the proposed approach.
-
When structural break meets threshold effect: Factor analysis under structural instabilities
Structural instability has been one of the central research questions in economics and finance over many decades. This paper systematically investigates structural instabilities in high dimensional factor models, which portray both structural breaks and threshold effects simultaneously. The observed high dimensional time series are concatenated at an unknown number of break points, while they are described by multiple threshold factor models that are heterogeneous between any two consecutive subsamples. Both joint and sequential procedures for estimating the break points are developed based on the second moment of the pseudo factor estimates that fully ignore the structural instabilities. In each separated subsample, the group Lasso approach recently proposed by Ma and Tu (2023b) is adopted to efficiently identify the threshold factor structure. An information criterion is further proposed to determine the number of break points, which also serves the purpose to distinguish the two types of instabilities. Theoretical properties of the proposed estimators are established, and their finite sample performance is evaluated in Monte Carlo simulations. An empirical application to the U.S. financial market dataset demonstrates the consequences when structural break meets threshold effect in factor analysis.