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引用次数: 5
摘要
本文研究了从大量数据中提取潜在信息的因子模型在预测韩国宏观经济变量方面的有效性。除了众所周知的主成分分析(PCA)之外,我们运用稀疏主成分分析(SPCA)构建了一个简约模型,并将估计的因素与各种收缩方法结合起来,继Stock and Watson(2012)和Kim and Swanson (2013a)之后,预测了2003:01至2012:12期间韩国的CPI通胀、GDP增长、出口、消费和总资本形成(GCF)。我们的主要发现是,在预测增长率方面,各种混合模型优于基准模型,包括自回归模型,并且随着预测范围的延长,这一结果变得更加清晰。具体来说,在预测2008-09年全球金融危机等波动更大的时期时,各种混合模型比AR模型更能预测拐点。辅助结论是,SPCA指出的韩国宏观经济变量的主要成分包括利率、接到的工程订单、雇佣变量。令人惊讶的是,在我们的实验中,货币总量或价格变量从未被发现对主成分有贡献。
Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea
This paper investigates the usefulness of the factor model, which extracts latent information from a large set of data, in forecasting Korean macroeconomic variables. In addition to the well-known principal component analysis (PCA), we apply sparse principal component analysis (SPCA) to build a parsimonious model, and combine the estimated factors with various shrinkage methods, following Stock and Watson (2012) and Kim and Swanson (2013a), to forecast CPI inflation, GDP growth, exports, consumption and gross capital formation (GCF) of Korea from 2003:01 to 2012:12. Our major findings are that, in predicting growth rates, various hybrid models outperform benchmark models including an autoregressive model, and that this result becomes clearer as the forecast horizons lengthens. Specifically, in forecasting for more volatile periods like the global financial crisis during 2008-09, various hybrid models predict the inflection point better than AR model does. The auxiliary finding is that the main ingredients of Korean macroeconomic variables as indicated by SPCA include interest rates, construction orders received, and employment variables. Surprisingly, the monetary aggregates or price variables are never found to contribute to the principal components in our experiment.