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.     

Lasso-based index tracking and statistical arbitrage long-short strategies

In this paper, we apply the lasso-type regression to solve the index tracking (IT) and the long-short investing strategies. In both cases, our objective is to exploit the mean-reverting properties of prices as reported in the literature. This method is an interesting technique for portfolio selection due to its capacity to perform variable selection in linear regression and to solve high-dimensional problems (which is the case if we consider broader indexes such as the S&P 500 or the Russell 1000). We use lasso to solve IT and long-short with three market benchmarks (S&P 100 and Russell 1000 – US stock market; and Ibovespa – Brazilian market), comprising data from 2010 to 2017. Also, we formed IT portfolios using cointegration (a method widely used for index tracking) to have a basis for comparison of the results using lasso. The findings for IT showed similar overall performance between portfolios using lasso and cointegration, with a slight advantage to cointegration in some cases. Nonetheless, lasso-based IT portfolios presented average monthly turnover at least 40% smaller, indicating that lasso generated portfolios that had not only a consistent tracking performance but also a considerable advantage in terms of transaction costs (represented by the average turnover).     

On the Practice of Rescaling Covariates

Whether doing parametric or nonparametric regression with shrinkage, thresholding, penalized likelihood, Bayesian posterior estimators (e.g., ridge regression, lasso, principal component regression, waveshrink or Markov random field ), it is common practice to rescale covariates by dividing by their respective standard errors ρ. The stated goal of this operation is to provide unitless covariates to compare like with like, especially when penalized likelihood or prior distributions are used. We contend that this vision is too simplistic. Instead, we propose to take into account a more essential component of the structure of the regression matrix by rescaling the covariates based on the diagonal elements of the covariance matrix Σ of the maximum-likelihood estimator. We illustrate the differences between the standard ρ- and proposed Σ-rescalings with various estimators and data sets.     

EVA 考核下科技型央企价值创造能力影响因素研究——基于 Elastic Net 的实证研究

本文基于EVA视角以科技型央企为样本对象,收集其2011~2015年的面板数据为研究样本,选出影响央企价值创造能力的30个指标,运用弹性网的模型对其进行实证研究,并与Lasso模型及自适应Lasso模型进行对比,最终筛选出18个对EVA率产生关键影响的指标,并得到其回归系数,最后又将三种模型的准确率进行检测,得出资产报酬率和净资产利润率对央企价值创造能力的正向作用最为显著的结果,企业价值创造能力应着重从以上的方面进行评估。此外,本文从实证结果中得出结论：科技型央企应积极提高自身资产利用效率,谨慎使用权益资本进行投资,才能进一步提升自身价值创造能力。     

Incorporating biometric data in models of consumer choice

The use of neuro-physiological data in models of consumer choice is gaining popularity. This article presents some of the benefits of using psycho-physiological data in analyzing consumer valuation and choice. Eye-tracking, facial expressions, and electroencephalography (EEG) data were used to construct three non-conventional choice models, namely, eye-tracking, emotion and brain model. The predictive performance of the non-conventional models was compared to a baseline model, which was based entirely on conventional data. While the emotion and brain models proved to be as good as conventional data in explaining and predicting consumer choice, the eye-tracking model generated superior predictions. Moreover, we document a significant increase in predictive power when biometric data from different sources were combined into a mixed model. Finally, we utilize a machine learning technique to sparse the data and enhance out-of-sample prediction, thus showcasing the compatibility of biometric data with well-established statistical and econometric methods.     

基于Lasso和BP神经网络的组合预测及其应用——以居民消费支出预测为例

在变量选择的基础上,构建基于 Lasso 方法和 BP 神经网络的预测模型,并对我国城乡居民的消费支出进行预测,结果显示：基于 Lasso 方法和 BP 神经网络的组合预测精度要明显高于 BP 神经网络、Lasso方法的预测精度;在2014~2020年,我国农村居民消费增长率有所提升,城镇居民消费增长率减缓,城乡居民消费增长率之间的差距呈下降趋势,但短期内城乡居民消费差距依然难以缓和。