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引用次数: 48
摘要
本文记录了国内生产总值(GDP)增长的“临近预测”模型GDPNow,该模型综合了将GDP子成分与月度源数据联系起来的“桥式方程”方法和Giannone、Reichlin和Small(2008)使用的因子模型方法。GDPNow模型采用美国经济分析局(Bureau of Economic Analysis)使用的链式加权方法,将构成GDP的13个子成分相加,从而预测GDP增长。使用当前的古数据,发现样本外的GDPNow模型预测比2000年以来的一些统计基准更准确。使用2011年下半年以来的实时数据,发现GDPNow模型的预测仅略低于蓝筹经济指标的近期GDP预测。将GDPNow模型、蓝筹股和美联储工作人员绿皮书对GDP增长的预测误差方差分解为GDP各子成分对增长贡献的预测误差协方差之和。分解表明,“净出口”和“私人库存变化”是特别难以预测的子成分。
This paper documents GDPNow, a "nowcasting" model for gross domestic product (GDP) growth that synthesizes the "bridge equation" approach relating GDP subcomponents to monthly source data with the factor model approach used by Giannone, Reichlin, and Small (2008). The GDPNow model forecasts GDP growth by aggregating 13 subcomponents that make up GDP with the chain-weighting methodology used by the U.S. Bureau of Economic Analysis. Using current vintage data, out-of-sample GDPNow model forecasts are found to be more accurate than a number of statistical benchmarks since 2000. Using real-time data since the second-half of 2011, GDPNow model forecasts are found to be only slightly inferior to consensus near-term GDP forecasts from Blue Chip Economic Indicators. The forecast error variance of GDP growth for each of the GDPNow model, Blue Chip, and the Federal Reserve staff's Green Book is decomposed as the sum of the forecast error covariances for the contributions to growth of the subcomponents of GDP. The decompositions show that "net exports" and "change in private inventories" are particularly difficult subcomponents to nowcast.