摘要翻译：
本文旨在研究在国家统计机构正式公布数据之前，使用稀疏方法预测美国和欧盟国内生产总值的实际支出构成部分。我们通过将季度数据与可用的月度信息连接起来，以更小的延迟来估计当前季度的预测以及1季度和2季度的预测。我们通过假设先行指标的稀疏结构，解决了月度数据集的高维问题，能够充分解释分析数据的动态。对于预测的变量选择和估计，我们使用稀疏方法-LASSO及其最近的改进。我们提出了一种将LASSO案例与主成分分析相结合的调整，认为这可以提高预测性能。我们以2005-2019年为样本，对固定资本形成总额、私人消费、进口和出口进行伪实时实验，并与基准ARMA和因子模型进行比较，评估预测绩效。主要结果表明，稀疏方法可以优于基准，并识别合理的解释变量子集。提出的LASSO-PC改进方案进一步提高了预报精度。
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英文标题：
《Sparse structures with LASSO through Principal Components: forecasting
  GDP components in the short-run》
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作者：
Saulius Jokubaitis and Dmitrij Celov and Remigijus Leipus
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  This paper aims to examine 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 the national institutions of statistics officially release the data. We estimate current quarter nowcasts along with 1- and 2-quarter forecasts by bridging quarterly data with available monthly information announced with a much smaller delay. We solve the high-dimensionality problem of the monthly dataset by assuming sparse structures of leading indicators, capable of adequately explaining the dynamics of analyzed data. For variable selection and estimation of the forecasts, we use the sparse methods - LASSO together with its recent modifications. We propose an adjustment that combines LASSO cases with principal components analysis that deemed to improve the forecasting performance. We evaluate forecasting performance conducting pseudo-real-time experiments for gross fixed capital formation, private consumption, imports and exports over the sample of 2005-2019, compared with benchmark ARMA and factor models. The main results suggest that sparse methods can outperform the benchmarks and to identify reasonable subsets of explanatory variables. The proposed LASSO-PC modification show further improvement in forecast accuracy.
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PDF链接：
https://arxiv.org/pdf/1906.07992