Revisiting Targeted Factors

This paper proposes new methods for ‘targeting’ factors estimated from a big dataset. We suggest that forecasts of economic variables can be improved by tuning factor estimates: (i) so that they are both more relevant for a specific target variable; and (ii) so that variables with considerable idiosyncratic noise are down‐weighted prior to factor estimation. Existing targeted factor methodologies are limited to estimating the factors with only one of these two objectives in mind. We therefore combine these ideas by providing new weighted principal components analysis (PCA) procedures and a targeted generalized PCA (TGPCA) procedure. These methods offer a flexible combination of both types of targeting that is new to the literature. We illustrate this empirically by forecasting a range of US macroeconomic variables, finding that our combined approach yields important improvements over competing methods, consistently surviving elimination in the model confidence set procedure. Copyright © 2016 John Wiley & Sons, Ltd.     

基于LASSO方法的结构突变理论研究综述

结构突变是统计学、经济学、信号处理和生物信息学等学科领域中的研究热点之一.Z.Harchaoui 等提出了基于LASSO的结构突变点检测方法,是近几年结构突变问题的最新研究方法.为了在国内推行该方法,系统介绍了国外基于LASSO方法的几种变点模型中的变点检测问题,其核心是把变点检测问题转化成模型选择问题来解决,并阐述了相应的算法.最后探讨该方法在不同学科领域的应用和前景展望.     

相加模型中成分函数选择的新方法

作者提出一种新的相加模型中成分函数选择方法,并将Tibshirani 的LASSO方法推广到非参数的相加模型.数值模拟研究显示该方法可以同时估计与选择成分函数,计算简便.     

基于Generalized LASSO模型的长江寸滩站年均径流突变研究

为了区分水文时间序列的趋势和跳跃变异,将基于L_1范数正则化技术的generalized LASSO模型应用于水文序列变异识别。经过识别,发现长江寸滩水文站年平均流量序列在1969年发生了向下的均值跃变。此外,趋势分析表明无论是跃变前后的子序列还是剔除跳跃成分的整个序列,均未检出显著的趋势,这说明对寸滩水文站年平均流量序列的跳跃变异假设是合理的。基于generalized LASSO模型的结果与其他突变检测方法结果进行比较,所得结论是一致的。     

Forecasting Simultaneously High‐Dimensional Time Series: A Robust Model‐Based Clustering Approach

This paper considers the problem of forecasting high‐dimensional time series. It employs a robust clustering approach to perform classification of the component series. Each series within a cluster is assumed to follow the same model and the data are then pooled for estimation. The classification is model‐based and robust to outlier contamination. The robustness is achieved by using the intrinsic mode functions of the Hilbert–Huang transform at lower frequencies. These functions are found to be robust to outlier contamination. The paper also compares out‐of‐sample forecast performance of the proposed method with several methods available in the literature. The other forecasting methods considered include vector autoregressive models with ∕ without LASSO, group LASSO, principal component regression, and partial least squares. The proposed method is found to perform well in out‐of‐sample forecasting of the monthly unemployment rates of 50 US states. Copyright © 2013 John Wiley & Sons, Ltd.     

基于LASSO分位数回归的中期电力负荷概率密度预测方法

中期电力负荷预测过程中往往会受到多种外界因素(诸如温度、节假日、风力大小等)的不确定性干扰,并且影响中期电力负荷预测的因素复杂多变、规律各异,难以精准地进行预测.在大数据环境下,如何在种类繁多、数量庞大的影响因素中快速获取有价值信息成为了电力负荷预测问题的关键所在.提出的基于LASSO分位数回归概率密度预测方法,首先从影响电力负荷预测的多种外界因素中挑选出重要的影响因子,建立LASSO分位数回归模型.然后,使用triangular核函数,将LASSO分位数回归与核密度估计方法相结合,进行中期电力负荷概率密度预测.以中国东部某副省级市的历史负荷和外界影响因素(包括温度、节假日及风力大小)为算例,运用LASSO分位数回归方法进行中期电力负荷概率密度预测,得到的平均绝对误差在中位数和众数上分别为3.53%和3.69%,优于未考虑外界因素和考虑外界因素未进行变量选择的情况.为了进一步验证该方法的优越性,将其与非线性分位数回归和基于三角核的分位数回归神经网络概率密度预测方法进行对比分析,说明该方法能较好解决电力负荷预测中的高维数据问题,从而获得比较准确的电力负荷预测结果.     

The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach

The difficulty in modelling inflation and the significance in discovering the underlying data‐generating process of inflation is expressed in an extensive literature regarding inflation forecasting. In this paper we evaluate nonlinear machine learning and econometric methodologies in forecasting US inflation based on autoregressive and structural models of the term structure. We employ two nonlinear methodologies: the econometric least absolute shrinkage and selection operator (LASSO) and the machine‐learning support vector regression (SVR) method. The SVR has never been used before in inflation forecasting considering the term spread as a regressor. In doing so, we use a long monthly dataset spanning the period 1871:1–2015:3 that covers the entire history of inflation in the US economy. For comparison purposes we also use ordinary least squares regression models as a benchmark. In order to evaluate the contribution of the term spread in inflation forecasting in different time periods, we measure the out‐of‐sample forecasting performance of all models using rolling window regressions. Considering various forecasting horizons, the empirical evidence suggests that the structural models do not outperform the autoregressive ones, regardless of the model's method. Thus we conclude that the term spread models are not more accurate than autoregressive models in inflation forecasting. Copyright © 2016 John Wiley & Sons, Ltd.     

基于Post-LASSO方法的就医需求多控制变量选择

分析省级层面就医需求的政策变量和交互要素,并控制地区和时间效应的异质性,为精确估计医疗改革效应和医疗机构区域合理布局提供科学依据.以就医需求和就医供给的代理变量、区域特征控制变量建立指标体系,采用Post-double-selection-LASSO方法选择潜在变量及其函数形式.一阶差分、全控制变量和各省标准差集聚三个模型的比较结果显示,标准差集聚模型较好地控制时间趋势和初始差异,证实复杂就医需求的影响因素包括医院规模、医疗业务、医疗价格、区域特征,以及医疗收入和人口密度的交互作用.由此,应考虑不同工具变量的传递路径、不同区域特征及其初始差异和交互要素对就医需求的影响,以实现医疗资源空间均衡化.     

部分线性模型的LASSO估计及其渐近性

结合截面最小二乘估计思想,构造了LASSO惩罚截面最小二乘估计,并研究了惩罚参数和窗宽的选择问题。由于部分线性模型LASSO解仍为线性优化问题,因此容易实现。在一定条件下,本文还研究了参数估计量的相合性和渐近正态性。最后通过蒙特卡洛模拟研究了变量选择方法的小样本性质。     

对称阵稀疏主成分分析及其在充分降维问题中的应用

讨论了对称阵的稀疏主成分分析,并给出估计的渐近结果。基于蒙特卡洛分析的模拟实验展示了在充分降维中稀疏主成分的优势。