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We propose composite quantile regression for dependent data, in which the errors are from short‐range dependent and strictly stationary linear processes. Under some regularity conditions, we show that composite quantile estimator enjoys root‐n consistency and asymptotic normality. We investigate the asymptotic relative efficiency of composite quantile estimator to both single‐level quantile regression and least‐squares regression. When the errors have finite variance, the relative efficiency of composite quantile estimator with respect to the least‐squares estimator has a universal lower bound. Under some regularity conditions, the adaptive least absolute shrinkage and selection operator penalty leads to consistent variable selection, and the asymptotic distribution of the non‐zero coefficient is the same as that of the counterparts obtained when the true model is known. We conduct a simulation study and a real data analysis to evaluate the performance of the proposed approach. 相似文献
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[目的]农村居民是乡村振兴的主体,其食物消费和膳食结构直接影响乡村振兴战略的顺利实施。四川省是我国西部农业大省,其农村居民的食物消费和膳食结构在中国西部地区具有一定的代表性。分析四川农村居民食物消费支出及其影响因素,对于改善农村居民食物消费和推进国家乡村振兴战略具有重要意义。[方法]根据经济发展水平进行分层抽样,选取四川省3市3县(区)156个农户,开展食物消费支出及营养认知问卷调研,采用LASSO方法对调研结果进行回归,筛选出影响农村居民食物消费支出的关键因素。[结果]在影响农村居民食物消费支出的主要因素中,家庭食物营养决策人的营养态度、家庭收入和决策人年龄与家庭食物消费支出都存在显著正相关;其中,决策人的营养态度相关系数最大,达到0.886,家庭收入和决策人年龄的相关系数分别为0.043和0.011;而在家就餐人数与食物消费之间存在显著负相关,相关系数为-0.020。[结论]影响四川农村居民食物消费支出的关键性影响因素主要有决策人的营养态度、家庭收入、在家就餐人数和决策人年龄;其中,决策人的营养态度是四川省农村居民食物消费的主要因素,且存在正相关,即决策人营养认知水平高对农村居民膳食结构优化具有重要的促进作用,这也表明营养知识宣传及消费引导的重要性。 相似文献
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基于2001—2016年江西省高校R&D创新绩效的面板数据,利用LASSO方法筛选,探究江西省高校R&D创新绩效的影响因素。实证研究结果显示,R&D全时人员、R&D投入强度、举办学术交流活动次数、省人均GDP、技术转让合同数以及专利出售合同数6个因素对江西省高校R&D创新绩效影响显著。进一步提出江西省高等院校加强对自身R&D活动的重视和投入等对策建议。 相似文献
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《International Journal of Forecasting》2020,36(3):1149-1162
We analyze the quantile combination approach (QCA) of Lima and Meng (2017) in situations with mixed-frequency data. The estimation of quantile regressions with mixed-frequency data leads to a parameter proliferation problem, which can be addressed through extensions of the MIDAS and soft (hard) thresholding methods towards quantile regression. We use the proposed approach to forecast the growth rate of the industrial production index, and our results show that including high-frequency information in the QCA achieves substantial gains in terms of forecasting accuracy. 相似文献
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Boriss Siliverstovs 《Applied economics》2017,49(13):1326-1343
In this article, we extend the targeted-regressor approach suggested in Bai and Ng (2008) for variables sampled at the same frequency to mixed-frequency data. Our MIDASSO approach is a combination of the unrestricted MIxed-frequency DAta-Sampling approach (U-MIDAS) (see Foroni et al. 2015; Castle et al. 2009; Bec and Mogliani 2013), and the LASSO-type penalized regression used in Bai and Ng (2008), called the elastic net (Zou and Hastie 2005). We illustrate our approach by forecasting the quarterly real GDP growth rate in Switzerland. 相似文献
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We investigate the relative importance of various bankruptcy predictors commonly used in the existing literature by applying a variable selection technique, the least absolute shrinkage and selection operator (LASSO), to a comprehensive bankruptcy database. Over the 1980–2009 period, LASSO admits the majority of Campbell et al. (2008) predictive variables into the bankruptcy forecast model. Interestingly, by contrast with recent studies, some financial ratios constructed from only accounting data also contain significant incremental information about future default risk, and their importance relative to that of market-based variables in bankruptcy forecasts increases with prediction horizons. Moreover, LASSO-selected variables have superior out-of-sample predictive power and outperform (1) those advocated by Campbell et al. (2008) and (2) the distance to default from Merton’s (1974) structural model. 相似文献
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《International Journal of Forecasting》2019,35(1):45-66
Interest in the use of “big data” when it comes to forecasting macroeconomic time series such as private consumption or unemployment has increased; however, applications to the forecasting of GDP remain rather rare. This paper incorporates Google search data into a bridge equation model, a version of which usually belongs to the suite of forecasting models at central banks. We show how such big data information can be integrated, with an emphasis on the appeal of the underlying model in this respect. As the decision as to which Google search terms should be added to which equation is crucial —- both for the forecasting performance itself and for the economic consistency of the implied relationships —- we compare different (ad-hoc, factor and shrinkage) approaches in terms of their pseudo real time out-of-sample forecast performances for GDP, various GDP components and monthly activity indicators. We find that sizeable gains can indeed be obtained by using Google search data, where the best-performing Google variable selection approach varies according to the target variable. Thus, assigning the selection methods flexibly to the targets leads to the most robust outcomes overall in all layers of the system. 相似文献
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In this article, we develop an empirical framework to show the importance of money during the Great Moderation, while accounting for the fact that monetary policy was exclusively conducted through interest rates. We estimate the impulse response functions and forecast error variance decomposition derived from a structural VAR with a least absolute shrinkage and selection operator–based lag selection. The variance decomposition suggests that a substantial component of macroeconomic variation has been driven by shocks to the money market, which were not only unintended by the Federal Reserve, but worse passed unnoticed allowing those shocks to accumulate over time. 相似文献
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This article investigates if cryptocurrencies returns' are similarly affected by a selection of demand- and supply-side determinants. Homogeneity among cryptocurrencies is tested via a least absolute shrinkage and selection operator (LASSO) model where determinants of Bitcoin returns are applied to a sample of 12 cryptocurrencies. The analysis goes beyond existing research by simultaneously covering different periods and design choices of cryptocurrencies. The results show that cryptocurrencies are heterogeneous, apart from some similarities in the impact of technical determinants and cybercrime. The cryptocurrency market displays evidence of substitution effects, and design choices related explain the impact of the determinants of return. 相似文献