Financial literacy amongst elderly Australians

Financial illiteracy is widespread amongst the elderly. Financially illiterate people are more likely to experience asset loss and outlive their savings after retirement. This paper measures financial literacy of elderly Australians using Item Responses Theory. Using a Lasso regression, we find that younger, married males with higher income and greater net wealth are more likely to be financially literate. Better financial literacy is also associated with good health, higher educational attainment, better occupation and outright home ownership. Our findings suggest policy‐makers take action and we make informed and practicable policy recommendations.     

一种基于Lasso回归与SVD融合的深度学习模型压缩方法

针对深度学习模型所需的海量参数及强大的计算资源而导致其不能很便捷地应用于嵌入式设备或移动端的问题，在Lasso（Least Absolute Shrinkage and Selection Operator）回归通道挑选法的基础上，提出了Lasso+奇异值分解（Singular Value Decomposition，SVD）的融合压缩法。使用VGG-16为初始模型，分别在不同的小型数据集上进行迁移学习，使用迁移学习后的模型在不同的加速率下进行测试。实验结果表明，相对于传统的模型压缩算法，Lasso+SVD的融合压缩法实现了在加速和参数压缩两方面的优势，进而以目标检测为应用方向，在保证准确率的同时不仅降低了模型存储需求，而且也较大提升了模型的实时性。     

Regional heterogeneity and U.S. presidential elections: Real-time 2020 forecasts and evaluation

This paper exploits cross-sectional variation at the level of U.S. counties to generate real-time forecasts for the 2020 U.S. presidential election. The forecasting models are trained on data covering the period 2000–2016, using high-dimensional variable selection techniques. Our county-based approach contrasts the literature that focuses on national and state level data but uses longer time periods to train their models. The paper reports forecasts of popular and electoral college vote outcomes and provides a detailed ex-post evaluation of the forecasts released in real time before the election. It is shown that all of these forecasts outperform autoregressive benchmarks. A pooled national model using One-Covariate-at-a-time-Multiple-Testing (OCMT) variable selection significantly outperformed all models in forecasting the U.S. mainland national vote share and electoral college outcomes (forecasting 236 electoral votes for the Republican party compared to 232 realized). This paper also shows that key determinants of voting outcomes at the county level include incumbency effects, unemployment, poverty, educational attainment, house price changes, and international competitiveness. The results are also supportive of myopic voting: economic fluctuations realized a few months before the election tend to be more powerful predictors of voting outcomes than their long-horizon analogs.     

Catching Growth Determinants with the Adaptive Lasso

Abstract. This paper uses the adaptive Lasso estimator to determine variables important for economic growth. The adaptive Lasso estimator is a computationally very efficient procedure that simultaneously performs model selection and parameter estimation. The computational cost of this method is negligibly small compared with standard approaches in the growth regressions literature. We apply this method for a regional dataset for the European Union covering the 255 NUTS2 regions in the 27 member states over the period 1995–2005. The results suggest that initial GDP per capita (with an implied convergence speed of about 1.5% per annum), human capital (proxied by the shares of highly and medium educated in the working age population), structural labor market characteristics (the initial unemployment rate and the initial activity rate of the low educated) as well as being a capital region are important for economic growth.     

基于双自适应Lasso惩罚的随机效应分位回归模型研究

研究目标：解决随机效应分位回归模型中固定效应和随机效应系数同时估计和选择问题。研究方法：对固定效应和随机效应系数同时实施自适应Lasso惩罚,并为参数估计设计交替迭代算法。研究发现：新方法不仅对随机误差分布具有较强的稳健性,而且在不同稀疏度模型下均有着良好的表现,尤其是在高维情形时。研究创新：本文提出的方法在对模型中重要自变量进行选择的同时能够充分考虑随机效应的影响;交替迭代算法不仅有效解决了需要选择两个惩罚参数的困境,而且收敛速度快。研究价值：为实际工作者对面板数据和纵向数据的分析提供了有效的建模方法。     

High-dimensional index tracking based on the adaptive elastic net

When a portfolio consists of a large number of assets, it generally incorporates too many small and illiquid positions and needs a large amount of rebalancing, which can involve large transaction costs. For financial index tracking, it is desirable to avoid such atomized, unstable portfolios, which are difficult to realize and manage. A natural way of achieving this goal is to build a tracking portfolio that is sparse with only a small number of assets in practice. The cardinality constraint approach, by directly restricting the number of assets held in the tracking portfolio, is a natural idea. However, it requires the pre-specification of the maximum number of assets selected, which is rarely practicable. Moreover, the cardinality constrained optimization problem is shown to be NP-hard. Solving such a problem will be computationally expensive, especially in high-dimensional settings. Motivated by this, this paper employs a regularization approach based on the adaptive elastic-net (Aenet) model for high-dimensional index tracking. The proposed method represents a family of convex regularization methods, which nests the traditional Lasso, adaptive Lasso (Alasso), and elastic-net (Enet) as special cases. To make the formulation more practical and general, we also take the full investment condition and turnover restrictions (or transaction costs) into account. An efficient algorithm based on coordinate descent with closed-form updates is derived to tackle the resulting optimization problem. Empirical results show that the proposed method is computationally efficient and has competitive out-of-sample performance, especially in high-dimensional settings.     

Deconfounding and Causal Regularisation for Stability and External Validity

We review some recent works on removing hidden confounding and causal regularisation from a unified viewpoint. We describe how simple and user-friendly techniques improve stability, replicability and distributional robustness in heterogeneous data. In this sense, we provide additional thoughts on the issue of concept drift, raised recently by Efron, when the data generating distribution is changing.     

Variable selection in international diffusion models

Prior research comes to different conclusions as to what country characteristics drive diffusion patterns. One prime difficulty that may partially explain this divergence between studies is the sparseness of the data, in terms of the periodicity as well as the number of products and countries, in combination with the large number of potentially influential country characteristics. In face of such sparse data, scholars have used nested models, bivariate models and factor models to explore the role of country covariates. This paper uses Bayesian Lasso and Bayesian Elastic Net variable selection procedures as powerful approaches to identify the most important drivers of differences in Bass diffusion parameters across countries. We find that socio-economic and demographic country covariates (most pronouncedly so, economic wealth and education) have the strongest effect on all diffusion metrics we study. Our findings are a call for marketing scientists to devote greater attention to country covariate selection in international diffusion models, as well as to variable selection in marketing models at large.     

Macroeconomic forecasting using penalized regression methods

We study the suitability of applying lasso-type penalized regression techniques to macroe-conomic forecasting with high-dimensional datasets. We consider the performances of lasso-type methods when the true DGP is a factor model, contradicting the sparsity assumptionthat underlies penalized regression methods. We also investigate how the methods handle unit roots and cointegration in the data. In an extensive simulation study we find that penalized regression methods are more robust to mis-specification than factor models, even if the underlying DGP possesses a factor structure. Furthermore, the penalized regression methods can be demonstrated to deliver forecast improvements over traditional approaches when applied to non-stationary data that contain cointegrated variables, despite a deterioration in their selective capabilities. Finally, we also consider an empirical applicationto a large macroeconomic U.S. dataset and demonstrate the competitive performance of penalized regression methods.