Macroeconomic Forecasting and Variable Selection with a Very Large Number of Predictors: A Penalized Regression Approach
Macroeconomic Forecasting and Variable Selection with a Very Large Number of Predictors: A Penalized Regression Approach
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DOI：
10.2139/ssrn.2927876
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发表时间：
2015-08
期刊：
影响因子：
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通讯作者：
Yoshimasa Uematsu;Shinya Tanaka
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文献类型：
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作者：
Yoshimasa Uematsu;Shinya Tanaka
本文研究了具有大量预测变量的折迭凹惩罚回归模型的宏观经济预测和变量选择问题。惩罚回归方法导致回归系数的稀疏估计，并且即使模型的维数远大于样本大小也是适用的。本文的前半部分讨论了当模型具有时间序列依赖性时，折叠凹惩罚回归的理论问题。特别地，我们证明了超高维时间依赖回归的预言不等式和预言性质。论文的后半部分使用两个激励实证应用程序显示了惩罚回归的有效性。第一个模型使用MIDAS回归框架，使用FRED-MD数据预测美国GDP，其中有1000多个协变量，而样本量最多为200。第二个研究如何以及惩罚回归筛选隐藏的投资组合，约40只股票从1800多个潜在的股票使用纽约证券交易所的股票价格数据。这两个应用程序表明，惩罚回归提供了显着的结果，在预测性能和变量选择。
This paper studies macroeconomic forecasting and variable selection using a folded-concave penalized regression with a very large number of predictors. The penalized regression approach leads to sparse estimates of the regression coefficients, and is applicable even if the dimensionality of the model is much larger than the sample size. The first half of the paper discusses the theoretical aspects of a folded-concave penalized regression when the model exhibits time series dependence. Specifically, we show the oracle inequality and the oracle property for ultrahigh-dimensional time-dependent regressors. The latter half of the paper shows the validity of the penalized regression using two motivating empirical applications. The first forecasts U.S. GDP with the FRED-MD data using the MIDAS regression framework, where there are more than 1000 covariates, while the sample size is at most 200. The second examines how well the penalized regression screens the hidden portfolio with around 40 stocks from more than 1800 potential stocks using NYSE stock price data. Both applications reveal that the penalized regression provides remarkable results in terms of forecasting performance and variable selection.