ALICE: Composite leading indicators for euro area inflation cycles

This paper develops Area-wide Leading Inflation CyclE (ALICE) indicators with the aim to provide early signals for turning points in the euro area headline and core inflation cycles. The headline (core) ALICE leads the reference cycle by three (four) months and the lead times extend to five (nine) months based on longer-leading series. Both ALICE indicators signalled turning points in the inflation cycles ex post and also performed well in a simulated real-time exercise. Moreover, in particular the headline ALICE also appeared to be useful for quantitative forecasting of the direction and level of inflation.     

Data revisions to German national accounts: Are initial releases good nowcasts?

Data revisions to national accounts pose a serious challenge to policy decision making. Well-behaved revisions should be unbiased, small, and unpredictable. This article shows that revisions to German national accounts are biased, large, and predictable. Moreover, with use of filtering techniques designed to process data subject to revisions, the real-time forecasting performance of initial releases can be increased by up to 23%. For total real GDP growth, however, the initial release is an optimal forecast. Yet, given the results for disaggregated variables, the averaging out of biases and inefficiencies at the aggregate GDP level appears to be good luck rather than good forecasting.     

利用现场可编程门阵列FPGA开发专用实时图像处理系统芯片

本文介绍了一个实时图像处理系统中，利用ＦＰＧＡ开发专用算法模块的范例，使人 ＦＰＧＡ的应用方法有一个较完整的认识。     

基于G4平台的嵌入式VxWorks系统在雷达信号处理中的应用

G4平台能快速处理板内节点间以及板间的大数据流通信,本文主要介绍通用G4硬件平台的基本结构特点和相关软硬件,分析基于抢占式多任务VxW orks实时操作系统的优点;充分利用VxW orks操作系统和G4硬件平台特点,完成了雷达数字信号处理中的管理与控制,成功实现了某雷达系统的实时数字信号处理。     

实时数据与货币政策分析和评价——基于产出缺口的一个说明

本文基于1994Q1-2008Q4的数据并分别利用三次趋势和HP滤波两种模型方法估计了我国的实时、准实时和最终产出缺口,分析表明,这一时期我国的产出缺口遭受了较大而且高度持续的修正,说明我国的实时产出缺口和基于事后修正数据估计的产出缺口有很大不同。由于产出缺口是货币政策决策的重要依据,而货币政策决策总发生在实时,不能等待后来产出缺口等数据信息的修正,因此,区分实时数据和事后修正数据对政策分析和评价而言可能就十分重要。     

Validating an innovative real-time Delphi approach - A methodological comparison between real-time and conventional Delphi studies

A novel and innovative real-time Delphi technique is introduced in order to address previously identified weaknesses of the conventional Delphi method, such as complicated facilitator tasks, lack of real-time presentation of results, and difficulties in tracking progress over time. We demonstrate how the real-time (computer-based) method increases the efficiency of the process, accommodates expert availability, and reduces drop-out-rates. Modifications in the Delphi procedure (e.g. change of iteration principle) not only increase efficiency but also change the nature and process of the survey technique itself. By identifying and analysing three individual effects (initial condition effect, feedback effect, and iteration effect) we examine whether the modifications in the survey process cause deviations to the survey results. Empirical data obtained from both conventional as well as real-time Delphi studies is analysed based on multiple statistical analyses. The research findings indicate that significant differences between the two Delphi survey formats do not exist and final survey results are not affected by changes in the survey procedure.     

Studying innovation processes in real-time: The promises and challenges of ethnography

This paper discusses the promises and challenges of innovation ethnographies. We depart from the notion that innovation processes are highly contingent, messy and non-linear and examine ways in which these processes have been studied. Our focus is on the challenges posed by the use of ethnographic methods to study innovation in-the-making. Our discussion is illustrated by an example culled from a longitudinal, real-time study of an innovation process in the food industry, inspired by actor-network theory (ANT) and its injunctions to focus on controversies and follow the actors. We conclude that although innovation ethnographies pose plenty of theoretical, methodological and practical challenges, they remain a promising and powerful method to map out the complex and tortuous paths of these processes.     

FRED-SD: A real-time database for state-level data with forecasting applications

We construct a real-time dataset (FRED-SD) with vintage data for the U.S. states that can be used to forecast both state-level and national-level variables. Our dataset includes approximately 28 variables per state, including labor-market, production, and housing variables. We conduct two sets of real-time forecasting exercises. The first forecasts state-level labor-market variables using five different models and different levels of industrially disaggregated data. The second forecasts a national-level variable exploiting the cross-section of state data. The state-forecasting experiments suggest that large models with industrially disaggregated data tend to have higher predictive ability for industrially diversified states. For national-level data, we find that forecasting and aggregating state-level data can outperform a random walk but not an autoregression. We compare these real-time data experiments with forecasting experiments using final-vintage data and find very different results. Because these final-vintage results are obtained with revised data that would not have been available at the time the forecasts would have been made, we conclude that the use of real-time data is essential for drawing proper conclusions about state-level forecasting models.     

Predicting benchmarked US state employment data in real time

US payroll employment data come from a survey and are subject to revisions. While revisions are generally small at the national level, they can be large enough at the state level to alter assessments of current economic conditions. Users must therefore exercise caution in interpreting state employment data until they are “benchmarked” against administrative data 5–16 months after the reference period. This article develops a state-space model that predicts benchmarked state employment data in real time. The model has two distinct features: (1) an explicit model of the data revision process and (2) a dynamic factor model that incorporates real-time information from other state-level labor market indicators. We find that the model reduces the average size of benchmark revisions by about 11 percent. When we optimally average the model’s predictions with those of existing models, the model reduces the average size of the revisions by about 14 percent.     

Bayesian forecasting of federal funds target rate decisions

In this paper we examine which macroeconomic and financial variables have most predictive ability for the federal funds target rate decisions made by the Federal Open Market Committee (FOMC). We conduct the analysis for the 157 FOMC decisions during the period January 1990–June 2008, using dynamic ordered probit models with a Bayesian endogenous variable selection methodology and real-time data for a set of 33 candidate predictor variables. We find that indicators of economic activity and forward-looking term structure variables, as well as survey measures are most informative from a forecasting perspective. For the full sample period, in-sample probability forecasts achieve a hit rate of 90%. Based on out-of-sample forecasts for the period January 2001–June 2008, 82% of the FOMC decisions are predicted correctly.