Model Averaging for Prediction With Fragmentary Data

<p>One main challenge for statistical prediction with data from multiple sources is that not all the associated covariate data are available for many sampled subjects. Consequently, we need new statistical methodology to handle this type of “fragmentary data” that has become more and more popular in recent years. In this article, we propose a novel method based on the frequentist model averaging that fits some candidate models using all available covariate data. The weights in model averaging are selected by delete-one cross-validation based on the data from complete cases. The optimality of the selected weights is rigorously proved under some conditions. The finite sample performance of the proposed method is confirmed by simulation studies. An example for personal income prediction based on real data from a leading e-community of wealth management in China is also presented for illustration.</p

Covariance Regression Analysis

<p>This article introduces covariance regression analysis for a <i>p</i>-dimensional response vector. The proposed method explores the regression relationship between the <i>p</i>-dimensional covariance matrix and auxiliary information. We study three types of estimators: maximum likelihood, ordinary least squares, and feasible generalized least squares estimators. Then, we demonstrate that these regression estimators are consistent and asymptotically normal. Furthermore, we obtain the high dimensional and large sample properties of the corresponding covariance matrix estimators. Simulation experiments are presented to demonstrate the performance of both regression and covariance matrix estimates. An example is analyzed from the Chinese stock market to illustrate the usefulness of the proposed covariance regression model. Supplementary materials for this article are available online.</p

Testing Alphas in Conditional Time-Varying Factor Models With High-Dimensional Assets

<p>For conditional time-varying factor models with high-dimensional assets, this article proposes a high-dimensional alpha (HDA) test to assess whether there exist abnormal returns on securities (or portfolios) over the theoretical expected returns. To employ this test effectively, a constant coefficient test is also introduced. It examines the validity of constant alphas and factor loadings. Simulation studies and an empirical example are presented to illustrate the finite sample performance and the usefulness of the proposed tests. Using the HDA test, the empirical example demonstrates that the FF three-factor model is better than CAPM in explaining the mean-variance efficiency of both the Chinese and U.S. stock markets. Furthermore, our results suggest that the U.S. stock market is more efficient in terms of mean-variance efficiency than the Chinese stock market. Supplementary materials for this article are available online.</p

Reduced Rank Spatio-Temporal Models

To simultaneously model the cross-sectional dependency and dynamic time dependency among n units, most research in spatial econometrics parameterizes the coefficient matrices among the n units as functions of known weights matrices. This modeling framework is over-simplified and faces the risk of misspecification when constructing the weights matrices. In this article, we propose a novel reduced-rank spatio-temporal model by assuming the coefficient matrices follow a reduced-rank structure. This specification avoids construction of the weights matrices and provides a good interpretation, especially for financial data. To estimate the unknown parameters, a quasi-maximum likelihood estimator (QMLE) is proposed and obtained via the Gradient descent algorithm with Armijo line search. We establish the asymptotic properties of QMLE when the number of units and the number of time periods both diverge to infinity. To determine the rank, we propose a ridge-type ratio estimator and demonstrate its rank selection consistency. The proposed methodology is illustrated via extensive simulation studies. Finally, a Chinese stock dataset is analyzed to investigate the cross-sectional and temporal spillover effects among stock returns.</p

Additional file 1 of A rare case of anti-DPPX encephalitis combined with neuroleptospirosis

Supplementary Material

The measurement model showing the factor loadings of items along with R-square values.

The measurement model showing the factor loadings of items along with R-square values.</p

Reliability statistics.

In today’s digitally interconnected world, social media emerges as a powerful tool, offering different opportunities for modern businesses. Not only do organizations use social media for marketing purposes, but they also endeavor to influence consumer psychology and behavior. Although prior studies indicate social media’s efficacy in disseminating corporate social responsibility (CSR) communications, there remains a dearth of research addressing the impact of CSR-related messaging from banks on consumers’ brand advocacy behavior (CBAB). Our study seeks to bridge this gap, exploring the CSR-CBAB relationship within the banking sector of an emerging economy. Additionally, we investigate the roles of consumers’ emotions and values in mediating and moderating their CBAB, introducing two mediating factors, consumer happiness (HP) and admiration (BRAD), and moderating variable altruistic values (ATVL). Data collection involved an adapted questionnaire targeting banking consumers. The structural analysis revealed a positive correlation between a bank’s CSR-related social media communications and CBAB. HP and BRAD were identified as mediators in this relationship, while ATVL emerged as a moderator. These findings hold significant theoretical and practical implications. For instance, our research highlights the indispensable role of social media in effectively conveying CSR-related information to banking consumers, subsequently enhancing their advocacy intentions.</div

Correlations and discriminant validity.

In today’s digitally interconnected world, social media emerges as a powerful tool, offering different opportunities for modern businesses. Not only do organizations use social media for marketing purposes, but they also endeavor to influence consumer psychology and behavior. Although prior studies indicate social media’s efficacy in disseminating corporate social responsibility (CSR) communications, there remains a dearth of research addressing the impact of CSR-related messaging from banks on consumers’ brand advocacy behavior (CBAB). Our study seeks to bridge this gap, exploring the CSR-CBAB relationship within the banking sector of an emerging economy. Additionally, we investigate the roles of consumers’ emotions and values in mediating and moderating their CBAB, introducing two mediating factors, consumer happiness (HP) and admiration (BRAD), and moderating variable altruistic values (ATVL). Data collection involved an adapted questionnaire targeting banking consumers. The structural analysis revealed a positive correlation between a bank’s CSR-related social media communications and CBAB. HP and BRAD were identified as mediators in this relationship, while ATVL emerged as a moderator. These findings hold significant theoretical and practical implications. For instance, our research highlights the indispensable role of social media in effectively conveying CSR-related information to banking consumers, subsequently enhancing their advocacy intentions.</div

Outer loadings and validity.

In today’s digitally interconnected world, social media emerges as a powerful tool, offering different opportunities for modern businesses. Not only do organizations use social media for marketing purposes, but they also endeavor to influence consumer psychology and behavior. Although prior studies indicate social media’s efficacy in disseminating corporate social responsibility (CSR) communications, there remains a dearth of research addressing the impact of CSR-related messaging from banks on consumers’ brand advocacy behavior (CBAB). Our study seeks to bridge this gap, exploring the CSR-CBAB relationship within the banking sector of an emerging economy. Additionally, we investigate the roles of consumers’ emotions and values in mediating and moderating their CBAB, introducing two mediating factors, consumer happiness (HP) and admiration (BRAD), and moderating variable altruistic values (ATVL). Data collection involved an adapted questionnaire targeting banking consumers. The structural analysis revealed a positive correlation between a bank’s CSR-related social media communications and CBAB. HP and BRAD were identified as mediators in this relationship, while ATVL emerged as a moderator. These findings hold significant theoretical and practical implications. For instance, our research highlights the indispensable role of social media in effectively conveying CSR-related information to banking consumers, subsequently enhancing their advocacy intentions.</div

The structural model showing the hypothesized relationships along with path coefficients.

The structural model showing the hypothesized relationships along with path coefficients.</p