Exploring Survey‐Based Inflation Forecasts

This paper first shows that survey‐based expectations (SBE) outperform standard time series models in US quarterly inflation out‐of‐sample prediction and that the term structure of survey‐based inflation forecasts has predictive power over the path of future inflation changes. It then proposes some empirical explanations for the forecasting success of survey‐based inflation expectations. We show that SBE pool a large amount of heterogeneous information on inflation expectations and react more flexibly and accurately to macro conditions both contemporaneously and dynamically. We illustrate the flexibility of SBE forecasts in the context of the 2008 financial crisis. Copyright © 2011 John Wiley & Sons, Ltd.     

Related‐variables selection in temporal disaggregation

Two related‐variables selection methods for temporal disaggregation are proposed. In the first method, the hypothesis tests for a common feature (cointegration or serial correlation) are first performed. If there is a common feature between observed aggregated series and related variables, the conventional Chow–Lin procedure is applied. In the second method, alternative Chow–Lin disaggregating models with and without related variables are first estimated and the corresponding values of the Bayesian information criterion (BIC) are stored. It is determined on the basis of the selected model whether related variables should be included in the Chow–Lin model. The efficacy of these methods is examined via simulations and empirical applications. Copyright © 2008 John Wiley & Sons, Ltd.     

Disaggregation of annual flow data with multiplicative trends

Various methods based on smoothing or statistical criteria have been used for constructing disaggregated values compatible with observed annual totals. The present method is based on a time‐series model in a state space form and allows for a prescribed multiplicative trend. It is applied to US GNP data which have been used for comparing methods suggested for this purpose. The model can be extended to include quarterly series, related to the unknown disaggregated values. But as the estimation criteria are based on prediction errors of the aggregated values, the estimated form may not be optimal for reproducing high‐frequency variations of the disaggregated values. Copyright © 1999 John Wiley & Sons, Ltd.     

Monthly Employment Indicators of the Euro Area and Larger Member States: Real‐Time Analysis of Indirect Estimates

The paper presents a comparative real‐time analysis of alternative indirect estimates relative to monthly euro area employment. In the experiment quarterly employment is temporally disaggregated using monthly unemployment as related series. The strategies under comparison make use of the contribution of sectoral data of the euro area and its six larger member states. The comparison is carried out among univariate temporal disaggregations of the Chow and Lin type and multivariate structural time series models of small and medium size. Specifications in logarithms are also systematically assessed. All multivariate set‐ups, up to 49 series modelled simultaneously, are estimated via the EM algorithm. Main conclusions are that mean revision errors of disaggregated estimates are overall small, a gain is obtained when the model strategy takes into account the information by both sector and member state and that larger multivariate set‐ups perform very well, with several advantages with respect to simpler models.Copyright © 2014 John Wiley & Sons, Ltd.     

Proinsulin C-peptide elicits disaggregation of insulin resulting in enhanced physiological insulin effects

Using surface plasmon resonance (SPR) and electrospray mass spectrometry (ESI-MS), proinsulin C-peptide was found to influence insulin-insulin interactions. In SPR with chip-bound insulin, C-peptide mixed with analyte insulin increased the binding, while alone C-peptide did not. A control peptide with the same residues in random sequence had little effect. In ESI-MS, C-peptide lowered the presence of insulin hexamer. The data suggest that C-peptide promotes insulin disaggregation. Insulin/insulin oligomer μM dissociation constants were determined. Compatible with these findings, type 1 diabetic patients receiving insulin and C-peptide developed 66% more stimulation of glucose metabolism than when given insulin alone. A role of C-peptide in promoting insulin disaggregation may be important physiologically during exocytosis of pancreatic β-cell secretory granulae and pharmacologically at insulin injection sites. It is compatible with the normal co-release of C-peptide and insulin and may contribute to the beneficial effect of C-peptide and insulin replacement in type 1 diabetics. Received 3 May 2006; received after revision 9 June 2006; accepted 12 June 2006 Free Online Access     

Disaggregation of annual time-series data to quarterly figures: A comparative study

In this paper we consider some of the prominent methods that are available in the literature for the problem of disaggregating annual time-series data to quarterly figures. The procedures are briefly described and illustrated through a real data set. The performances of the methods are compared in a Monte Carlo study. The results indicate that the complicated model-based procedure is usually superior to other non-model-based alternatives in the large sample situations. Based on the simulation results, we make some recommendations regarding the use of these methods.     

一种新的洪水随机模拟模型的应用

将俄罗斯水文学者Khristoforov等建立的一种能反映洪水涨快落慢特性的随机模拟模型应用于钱塘江流域衢江衢县站汛期洪水流量过程模拟,并提出了模型参数的提取方法,同时对参数的分布线型进行了统计检验,对模拟结果分别进行了长、短序列不同时段洪峰洪量统计特性的实用性检验.结果表明:该模型能够通过实用性检验;模拟出的汛期洪水流量过程线能较好地反映实际洪水变化特性;与传统且常用的相关解集模型及季节性AR(1)模型相比,该模型所用参数大为减少,而且模拟效果也优于以上2种模型.     

聚合级实体仿真中任务的分解与解聚

提出了聚合级实体任务分解和任务解聚的概念。任务分解是任务空间概念模型(CMMS)的核心问题,提出了任务分解的一般指导原则和基本方法。任务解聚是多分辨率仿真中不同分辨率实体保持任务一致性的必然要求,提出了聚合级实体发生解聚时其指挥任务的解聚方法。给出了装甲营进攻过程中任务分解与解聚的示例。     

Ex-post and ex-ante prediction of unobserved economic time series: a case study

In several countries, some macro-economic variables are not observed frequently (e.g. quarterly) and economic authorities need estimates of these high-frequency figures to make econometric analyses or to follow closely the country's economic growth. Two problems are involved in this context. The first is to make these estimates after observing low-frequency values and some related indicators, and the second is to obtain predictions using just the observed indicators, i.e. before observing a new low-frequency figure. This paper gives a new optimal solution to the first problem, and solves the second using a recursive optimal approach. In the second situation, additionally, statistical tests are developed for detecting structural changes at current periods in the macro-economic variable involved. © 1998 John Wiley & Sons, Ltd.     

Linear combination of restrictions and forecasts in time series analysis

An important tool in time series analysis is that of combining information in an optimal way. Here we establish a basic combining rule of linear predictors and show that such problems as forecast updating, missing value estimation, restricted forecasting with binding constraints, analysis of outliers and temporal disaggregation can be viewed as problems of optimal linear combination of restrictions and forecasts. A compatibility test statistic is also provided as a companion tool to check that the linear restrictions are compatible with the forecasts generated from the historical data. Copyright © 2000 John Wiley & Sons, Ltd.