Sparse structures with LASSO through principal components: Forecasting GDP components in the short-run

This paper examines the use of sparse methods to forecast the real (in the chain-linked volume sense) expenditure components of the US and EU GDP in the short-run sooner than national statistics institutions officially release the data. We estimate current-quarter nowcasts, along with one- and two-quarter forecasts, by bridging quarterly data with available monthly information announced with a much smaller delay. We solve the high-dimensionality problem of monthly datasets by assuming sparse structures of leading indicators capable of adequately explaining the dynamics of the analyzed data. For variable selection and estimation of the forecasts, we use LASSO together with its recent modifications. We propose an adjustment that combines LASSO cases with principal components analysis to improve the forecasting performance. We evaluated the forecasting performance by conducting pseudo-real-time experiments for gross fixed capital formation, private consumption, imports, and exports over a sample from 2005–2019, compared with benchmark ARMA and factor models. The main results suggest that sparse methods can outperform the benchmarks and identify reasonable subsets of explanatory variables. The proposed combination of LASSO and principal components further improves the forecast accuracy.     

基于LASSO方法的四川农村居民食物消费支出影响因素探究

[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。     

Variable selection and corporate bankruptcy forecasts

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.     

Quantile forecasting with mixed-frequency data

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.     

Google data in bridge equation models for German GDP

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.     

Short-term forecasting with mixed-frequency data: a MIDASSO approach

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.     

基于LASSO回归的江西省高校R&D投入强度的影响因素分析

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。     

Predicting failure risk using financial ratios: Quantile hazard model approach

This study examines the role of financial ratios in predicting companies’ default risk using the quantile hazard model (QHM) approach and compares its results to the discrete hazard model (DHM). We adopt the LASSO method to select essential predictors among the variables mentioned in the literature. We show the preeminence of our proposed QHM through the fact that it presents a different degree of financial ratios’ effect over various quantile levels. While DHM only confirms the aftermaths of “stock return volatilities” and “total liabilities” and the positive effects of “stock price”, “stock excess return”, and “profitability” on businesses, under high quantile levels QHM is able to supplement “cash and short-term investment to total assets”, “market capitalization”, and “current liabilities ratio” into the list of factors that influence a default. More interestingly, “cash and short-term investment to total assets” and “market capitalization” switch signs in high quantile levels, showing their different influence on companies with different risk levels. We also discover evidence for the distinction of default probability among different industrial sectors. Lastly, our proposed QHM empirically demonstrates improved out-of-sample forecasting performance.     

Testing the differentiated impact of the COVID-19 pandemic on air travel demand considering social inclusion

The economic downturn and the air travel crisis triggered by the recent coronavirus pandemic pose a substantial threat to the new consumer class of many emerging economies. In Brazil, considerable improvements in social inclusion have fostered the emergence of hundreds of thousands of first-time fliers over the past decades. We apply a two-step regression methodology in which the first step consists of identifying air transport markets characterized by greater social inclusion, using indicators of the local economies’ income distribution, credit availability, and access to the Internet. In the second step, we inspect the drivers of the plunge in air travel demand since the pandemic began, differentiating markets by their predicted social inclusion intensity. After controlling for potential endogeneity stemming from the spread of COVID-19 through air travel, our results suggest that short and low-density routes are among the most impacted airline markets and that business-oriented routes are more impacted than leisure ones. Finally, we estimate that a market with 1% higher social inclusion is associated with a 0.153%–0.166% more pronounced decline in demand during the pandemic. Therefore, markets that have benefited from greater social inclusion in the country may be the most vulnerable to the current crisis.     

Forecasting Chinese Corporate Bond Defaults: Comparative Study of Market- vs. Accounting-Based Models

This paper provides the first empirical study on bond defaults in the Chinese market. It overcomes the deficiencies of existing methods, which suffer from lack of actual default data for back testing. With newly available bond default data, we analyze the roles of market variables against accounting variables under various models. While we find that Merton’s market-based structural model and KMV’s Distance to Default exhibit languid discriminating power compared with hazard models that have carefully constructed predictors, other market variables carry significant information about bond defaults and could help improve on models with only the accounting variables. This implies that the collective intelligence of the market could somehow mitigate the distortion caused by misreported accounting information. Further, model performance can be significantly improved by adding predicting variables that link an individual financial measure to the broader market performance, such as the relative margin—a business environment proxy introduced in this study. We not only shed light on the default behavior of the Chinese bond market, but also provide a promising approach to improve the variable selection process.