Does Information Help Intra‐Day Volatility Forecasts?

While much research related to forecasting return volatility does so in a univariate setting, this paper includes proxies for information flows to forecast intra‐day volatility for the IBEX 35 futures market. The belief is that volume or the number of transactions conveys important information about the market that may be useful in forecasting. Our results suggest that augmenting a variety of GARCH‐type models with these proxies lead to improved forecasts across a range of intra‐day frequencies. Furthermore, our results present an interesting picture whereby the PARCH model generally performs well at the highest frequencies and shorter forecasting horizons, whereas the component model performs well at lower frequencies and longer forecast horizons. Both models attempt to capture long memory; the PARCH model allows for exponential decay in the autocorrelation function, while the component model captures trend volatility, which dominates over a longer horizon. These characteristics are likely to explain the success of each model. Copyright © 2013 John Wiley & Sons, Ltd.     

AWGN信道中载波频偏影响下的PCC-OFDM系统性能分析

载波频率偏差（CFO）会破坏正交频分复用（OFDM）系统中子载波间的正交性，从而产生子载波间干扰（ICI）．基于多项式抑制编码（PCC）的OFDM系统（PCC—OFDM）可以增强其对CFO的鲁棒性，从两方面分析AWGN信道中CFO对PCC—OFDM系统的性能影响：一是反应信号功率损失的平均信号功率与ICI功率之BB（CIR）或者平均的ICI功率；二是CFO影响下的系统SER和BER性能．首先给出了平均CIR和ICI功率闭式解的表达式．结果表明，CIR和ICI功率只取决于归一化频偏而与系统子载波数无关．其次，研究了CFO影响下PCC—OFDM系统在采用BPSK，QPSK，8-PSK和16-QAM调制符号时的SER性能及其采用Gray映射时的BER性能．利用Beaulieu序列的性质，SER和BER性能都可以根据ICI的特征函数精确地表达成无穷级数的和．     

Forecasting the Antwerp maritime steel traffic flow: A case study

This paper uses multivariate time series models to specify the maritime steel traffic flow in the port of Antwerp. The time series considered are the total outgoing and total incoming maritime steel traffic and the total steel production in the EEC. The obtained time series models provide useful insight into the general behaviour of the maritime steel traffic flow during the period 1971–82. In particular, they provide a quantitative interpretation of important changes which took place in the European steel industry during that period. The multivariate time series models produce forecasts which are a substantial improvement over those obtained by univariate time series models. This is especially the case for the series of total incoming maritime steel traffic in the port of Antwerp, when differencing and transformation of the original data are applied.     

Creating high‐frequency national accounts with state‐space modelling: a Monte Carlo experiment

This paper assesses a new technique for producing high‐frequency data from lower frequency measurements subject to the full set of identities within the data all holding. The technique is assessed through a set of Monte Carlo experiments. The example used here is gross domestic product (GDP) which is observed at quarterly intervals in the United States and it is a flow economic variable rather than a stock. The problem of constructing an unobserved monthly GDP variable can be handled using state space modelling. The solution of the problem lies in finding a suitable state space representation. A Monte Carlo experiment is conducted to illustrate this concept and to identify which variant of the model gives the best monthly estimates. The results demonstrate that the more simple models do almost as well as more complex ones and hence there may be little gain in return for the extra work of using a complex model. Copyright © 2001 John Wiley & Sons, Ltd.     

基于 ZigBee的新型无线楼层呼叫系统设计

针对当前建筑工地所使用施工升降机的应用要求,利用新兴的 ZigBee无线技术设计了一种新型楼层呼叫系统.该系统使用 CC2430无线单片机,并对2.4GHz高频天线和 PCB制板进行了良好的设计,可应用于恶劣的工业环境中,同时减少了布线带来的困难.通过现场试验,得出该无线楼层呼叫系统能有效实现复杂网络组建和数据通信功能,具有安装方便等特点     

Forecasting exchange rates using cointegration models and intra‐day data

We present a cointegration analysis on the triangle (USD–DEM, USD–JPY, DEM–JPY) of foreign exchange rates using intra‐day data. A vector autoregressive model is estimated and evaluated in terms of out‐of‐sample forecast accuracy measures. Its economic value is measured on the basis of trading strategies that account for transaction costs. We show that the typical seasonal volatility in high‐frequency data can be accounted for by transforming the underlying time scale. Results are presented for the original and the modified time scales. We find that utilizing the cointegration relation among the exchange rates and the time scale transformation improves forecasting results. Copyright © 2002 John Wiley & Sons, Ltd.     

Forecasts of inflation from var models

Why are forecasts of inflation from VAR models so much worse than their forecasts of real variables? This paper documents that relatively poor performance, and finds that the price equation of a VAR model fitted to US post-war data is poorly specified. Statistical work by other authors has found that coefficients in such price equations may not be constant. Based on specific monetary actions, two changes in monetary policy regimes are proposed. Accounting for those two shifts yields significantly more accurate forecasts and lessens the evidence of misspecification.     

Merging monthly and quarterly forecasts: Experience with mqem

Forecasts from quarterly econometric models are typically revised on a monthly basis to reflect the information in current economic data. The revision process usually involves setting targets for the quarterly values of endogenous variables for which monthly observations are available and then altering the intercept terms in the quarterly forecasting model to achieve the target values. A formal statistical approach to the use of monthly data to update quarterly forecasts is described and the procedure is applied to the Michigan Quarterly Econometric Model of the US Economy. The procedure is evaluated in terms of both ex post and ex ante forecasting performance. The ex ante results for 1986 and 1987 indicate that the method is quite promising. With a few notable exceptions, the formal procedure produces forecasts of GNP growth that are very close to the published ex ante forecasts.