Does children’s marriage matter for parents' mental health？Evidence from China

How to effectively alleviate mental disorders among elderly individuals is an important issue. Children are important financial and spiritual supporters of parents. However, whether there are upward spillovers from children to parents remains understudied. Using the instrumental variable (IV) method and data from the China Health and Retirement Longitudinal Study, this paper estimates the causal effect of children's marriage on the mental health of older parents. The IV estimation results demonstrate that having unmarried children is associated with a significant deterioration in parental mental health, especially in older, less educated, poor and male groups. Further evidence suggests that having unmarried children leads to significant changes in parents' economic behaviors, including labor supply, consumption, and savings; this indicates that parents are more likely to actively respond to their children’s unmarried status by increasing labor supply, reducing consumption and increasing savings rather than by engaging in negative behaviors.     

Forecasting crude oil market volatility using variable selection and common factor

This paper aims to improve the predictability of aggregate oil market volatility with a substantially large macroeconomic database, including 127 macro variables. To this end, we use machine learning from both the variable selection (VS) and common factor (i.e., dimension reduction) perspectives. We first use the lasso, elastic net (ENet), and two conventional supervised learning approaches based on the significance level of predictors’ regression coefficients and the incremental R-square to select useful predictors relevant to forecasting oil market volatility. We then rely on the principal component analysis (PCA) to extract a common factor from the selected predictors. Finally, we augment the autoregression (AR) benchmark model by including the supervised PCA common index. Our empirical results show that the supervised PCA regression model can successfully predict oil market volatility both in-sample and out-of-sample. Also, the recommended models can yield forecasting gains in both statistical and economic perspectives. We further shed light on the nature of VS over time. In particular, option-implied volatility is always the most powerful predictor.     

Sustainable consumption and plastic packaging: Relationships among product involvement,perceived marketplace influence and choice behavior

This study examines the relationships among human value connotations (instrumental and terminal), product involvement, perceived marketplace influence, and choice behavior in the context of sustainable consumption. Data was collected from 612 urban Indian residents who regularly consume sustainable products, e.g., non-plastic packaging bags. The study operationalizes the partial least square structural equation modeling method in consort with the covariance-based structural equation modeling. The research demonstrates the direct impact of instrumental and terminal values on product involvement and the direct influence of product involvement on perceived marketplace influence towards plastic packaging-related choice behavior. It is pertinent to focus on both instrumental and terminal connotations of human values to augment product involvement for plastic packaging. Limited studies have examined the possible relationships between two distinct yet interconnected connotations of human values on product involvement and perceived marketplace influence in the context of sustainable consumption related to plastic packaging.     

Does religion belief matter to self-employment of rural elderly? Evidence from China

There is a growing interest in understanding the economic consequences of religious belief. Using a unique dataset collected from thousands of villages, this research investigates the effect of religious beliefs on rural elderlies’ self-employment choice in China. The empirical results show that, self-employment is positively associated with religious beliefs after controlling for village characteristics, which implying that religion plays a significantly positive role in promoting self-employment of rural elderly. We also find that, the self-employment promoting effects are more significant in Christians and Buddhists. This result is consistent and robust after using instrumental variable approach, propensity score matching approach and controlling for the alternative hypothesis variables. Furthermore, elderly religious believers who are female, over 70 years old, living in a village where the secondary industry is dominant and soybeans/potatoes the main crops are more likely to became self-employees. Mechanism analysis shows economic rationality rather than value rationality that religious belief influence self-employment which confirms economic compensation channel. Given the few studies and limited dataset resources in the context of rural revitalization in China, this paper put effort to provides new evidences of the relationship between religious belief and self-employment.     

Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data

We propose an out-of-sample prediction approach that combines unrestricted mixed-data sampling with machine learning (mixed-frequency machine learning, MFML). We use the MFML approach to generate a sequence of nowcasts and backcasts of weekly unemployment insurance initial claims based on a rich trove of daily Google Trends search volume data for terms related to unemployment. The predictions are based on linear models estimated via the LASSO and elastic net, nonlinear models based on artificial neural networks, and ensembles of linear and nonlinear models. Nowcasts and backcasts of weekly initial claims based on models that incorporate the information in the daily Google Trends search volume data substantially outperform those based on models that ignore the information. Predictive accuracy increases as the nowcasts and backcasts include more recent daily Google Trends data. The relevance of daily Google Trends data for predicting weekly initial claims is strongly linked to the COVID-19 crisis.     

A kind of new time-weighted nonnegative lasso index-tracking model and its application

This study describes improved index-tracking methods to replicate the target index’s market performance in a high-dimensional sparse linear regression with nonnegative constraints on the coefficients. The main objective of this study is to construct a sparse portfolio with a better prediction effect and robustness. Considering the influence of time factors on index tracking, we propose a time-weighted nonnegative lasso index tracking model under different market constraints and define two new time-weighted construction methods. This index tracking model is an extension of Lasso and has variable selection consistency and estimation consistency under time-weighted nonnegative irrepresentable conditions similar to the irrepresentable condition in Lasso. We use the multiplicative updates algorithm to obtain the model’s solution since it is faster and simpler. The constrained index tracking problem in the stock market without short sales is studied in the latter part. The empirical results indicate that the optimized time-weighted nonnegative lasso index tracking model can obtain a smaller out-of-sample tracking error. The constructed portfolio has a better prediction effect and robustness, and we find that the exponential time-weighted method is better than the linear time-weighted method in capturing time information.     

The effects of inter-municipal cooperation and central grant allocation on the size of the French local public sector

The analysis of French municipalities’ public personnel expenditures allows us to study the issue of the size of the local public sector. We concentrate on two paths that French authorities have followed to limit it, i.e., the promotion of inter-municipal cooperation (IMC) and the cut in grants received by municipalities. Our objective is to evaluate their respective role in the evolution of public personnel expenditures at the municipal level, in a context where local politics comes into play. We consider a large panel dataset of municipalities embedded in IMC structures between 2011 and 2018. Our main results, obtained using an original identification strategy, are threefold. We first find evidence that a substitution effect between municipal and IMC personnel expenditures is at work. Second, we find a partisan distorsion through the grant allocation: despite its formula-based definition, aligned and unaligned municipalities are treated differently by the central government. Third, we show that cuts in grants lead to cuts in municipalities’ public wage bills, while partisanship hinders such cuts.     

Physical activity,leisure-time,cognition and academic grades: Connections and causal effects in Chinese students

Academic achievement and positive leisure activities are traditionally considered significant determinants of economic growth and human capital accumulation. This paper estimates the impact of physical activity on academic outcome and time allocation to 25 different types of leisure activity by Chinese adolescents. We use structural equation models (SEM) to explore the channels of this transmission. Our results suggest that physical exercise not only exerts a positive direct effect on academic outcome but also increases (decreases) students' time devoted to activities that are positively (negatively) correlated with academic outcome. All the effects are statistically significant but modest at the individual level. Our findings are robust to different exercise frequencies and academic outcome indicators based on students' self-assessment, academic scores, and cognitive tests.     

Targeting predictors in random forest regression

Random forest (RF) regression is an extremely popular tool for analyzing high-dimensional data. Nonetheless, its benefits may be lessened in sparse settings due to weak predictors, and a pre-estimation dimension reduction (targeting) step is required. We show that proper targeting controls the probability of placing splits along strong predictors, thus providing an important complement to RF’s feature sampling. This is supported by simulations using finite representative samples. Moreover, we quantify the immediate gain from targeting in terms of the increased strength of individual trees. Macroeconomic and financial applications show that the bias–variance trade-off implied by targeting, due to increased correlation among trees in the forest, is balanced at a medium degree of targeting, selecting the best 5%–30% of commonly applied predictors. Improvements in the predictive accuracy of targeted RF relative to ordinary RF are considerable, up to 21%, occurring both in recessions and expansions, particularly at long horizons.     

Italy: Immigration and the evolution of populism

We study the effect of immigration on the upsurge of right-wing populism in Italy. Our data considers electoral results at the municipality level of the Senate of the Italian Republic and the Chamber of Deputies over the period 2006–2018. Using an IV strategy based on the shift–share instrument, we find that immigration generates a sizable causal increase in votes for the right-wing populist party Lega. Immigration also works as a major catalyst for the electoral distance between Lega and its most direct competitors. We explore how different levels of tax autonomy impact the results, as well as how the re-branding of Lega as a national movement affects the relation between immigration and support for the party.