Preliminary-Data Error and Monetary Aggregate Targeting

Preliminary monetary aggregate data are subject to subsequent revision because of errors from seasonal and nonseasonal sources that will only be known later. This article examines the characteristics of these revision errors and analyzes their possible effect on monetary policy. It is found that false signals, in the sense that the preliminary M1 growth rate falls within the tolerance band set monthly by the Federal Open Market Committee while the final revised figure lies outside the tolerance limits, or vice versa, occur about two-fifths of the time. It is shown that this proportion can be halved by the adoption of improved seasonal adjustment procedures.     

Comment on state space modeling of multiple time series

This paper raises some interpretative issues that arise from univariate trend–cycle decompositions with correlated disturbances. In particular, it discusses whether the interpretation of a negative correlation as providing evidence for the prominence of real, or supply, shocks, can be supported.

For this purpose it determines the conditions under which correlated components may originate from the underestimation of the cyclical component in an orthogonal decomposition; from the presence of a growth rate cycle, rather than a deviation cycle; or alternatively, as a consequence of the hysteresis phenomenon. Finally, it considers interpreting correlated components in terms of permanent–transitory decompositions, where the permanent component has richer dynamics than a pure random walk.

The consequences for smoothing and signal extraction are discussed: in particular, it is documented that a negative correlation implies that future observations carry most of the information needed to assess cyclical stance. As a result, the components will be subject to underestimation in real time and thus to high revisions. The overall conclusion is that the characterization of economic fluctuations in macroeconomic time series largely remains an open issue.     

A Unified View of Nonparametric Trend-Cycle Predictors Via Reproducing Kernel Hilbert Spaces

We provide a common approach for studying several nonparametric estimators used for smoothing functional time series data. Linear filters based on different building assumptions are transformed into kernel functions via reproducing kernel Hilbert spaces. For each estimator, we identify a density function or second order kernel, from which a hierarchy of higher order estimators is derived. These are shown to give excellent representations for the currently applied symmetric filters. In particular, we derive equivalent kernels of smoothing splines in Sobolev and polynomial spaces. The asymmetric weights are obtained by adapting the kernel functions to the length of the various filters, and a theoretical and empirical comparison is made with the classical estimators used in real time analysis. The former are shown to be superior in terms of signal passing, noise suppression and speed of convergence to the symmetric filter.     

关于修订全国科技进步监测指标体系的设想

本文针对全国科技进步统计监测指标体系,提出了进一步修订建议,如：增加教育投入强度和教育产出的指标,修改反映资本使用效率的指标,以及完善反映环境保护、社会生活信息化水平的指标等。     

Trend-Cycle Decompositions with Correlated Components

This paper raises some interpretative issues that arise from univariate trend-cycle decompositions with correlated disturbances. In particular, it discusses whether the interpretation of a negative correlation as providing evidence for the prominence of real, or supply, shocks, can be supported.

For this purpose it determines the conditions under which correlated components may originate from the underestimation of the cyclical component in an orthogonal decomposition; from the presence of a growth rate cycle, rather than a deviation cycle; or alternatively, as a consequence of the hysteresis phenomenon. Finally, it considers interpreting correlated components in terms of permanent-transitory decompositions, where the permanent component has richer dynamics than a pure random walk.

The consequences for smoothing and signal extraction are discussed: in particular, it is documented that a negative correlation implies that future observations carry most of the information needed to assess cyclical stance. As a result, the components will be subject to underestimation in real time and thus to high revisions. The overall conclusion is that the characterization of economic fluctuations in macroeconomic time series largely remains an open issue.