The national accounts provide a coherent and exhaustive description of the current state of the economy, but are available at the quarterly frequency and are released with a nonignorable publication lag. The paper illustrates a method for nowcasting and forecasting the sixteen main components of Gross Domestic Product (GDP) by output and expenditure type at the monthly frequency, using a high-dimensional set of monthly economic indicators spanning the space of the common macroeconomic and financial factors. The projection on the common space is carried out by combining the individual nowcasts and forecasts arising from all possible bivariate models of the unobserved monthly GDP component and the observed monthly indicator. We discuss several pooling strategies and we select the one showing the best predictive performance according to a pseudo-real-time forecasting experiment. Monthly GDP can be indirectly estimated by the contemporaneous aggregation of the value added of the different industries and of the expenditure components. This enables the comparative assessment of the indirect nowcasts and forecasts vis-à-vis the direct approach and a growth accounting exercise. Our approach meets the challenges posed by the dimensionality, since it can handle a large number of time series with a complexity that increases linearly with the cross-sectional dimension, while retaining the essential heterogeneity of the information about the macroeconomy. An application to the Italian case leads to several interesting discoveries concerning the time-varying predictive content of the information carried by the monthly indicators.

国民账户提供了对当前经济状况的连贯而详尽的描述，但每季度提供一次，发布时有不可忽视的发布滞后。本文说明了一种方法，使用跨越常见宏观经济和金融因素空间的一组高维月度经济指标，按月频率按产出和支出类型对国内生产总值 (GDP) 的 16 个主要组成部分进行临近预测和预测。 . 对公共空间的预测是通过结合来自未观察到的月度 GDP 成分和观察到的月度指标的所有可能双变量模型的单个临近预报和预测来进行的。我们讨论了几种池化策略，并根据伪实时预测实验选择了一种显示最佳预测性能的策略。每月 GDP 可以通过不同行业的增加值和支出成分的同期汇总来间接估计。这使得可以对间接临近预报和预测与直接方法和增长核算活动进行比较评估。我们的方法解决了维度带来的挑战，因为它可以处理大量时间序列，其复杂度随横截面维度线性增加，同时保留了宏观经济信息的基本异质性。