卫星通信地球站终端技术的发展

本文介绍了卫星通信地球站终端技术的发展过程,初步论述了新技术应用的环境,为今后研制卫星通信设备提供某些参考。     

A note on forecasting international tourism demand in Spain

This paper considers the extent to which price and income proxy variables help in forecasting tourist demand in Spain. Contrary to some recent studies, we found that the inputs' contribution in terms of fitting and forecasting is nil when compared with alternative univariate models. Whether these findings are the results of the restrictions embedded in building the proxy inputs or in a poor specification of the dynamics of these models remains to be seen. We also contend that when dealing with medium, long-term forecasting comparisons, the use of the traditional aggregate accuracy measures like RMSE and MAPE help very little in discriminating among competing models. In these situations, predicted annual growth rates may be a better alternative.     

Combining information for prediction in linear regression

This article deals with the prediction problem in linear regression where the measurements are obtained using k different devices or collected from k different independent sources. For the case of k=2, a Graybill-Deal type combined estimtor for the regression parameters is shown to dominate the individual least squares estimators under the covariance criterion. Two predictors ŷ _c and ŷ _p are proposed. ŷ _c is based on a combined estimator of the regression coefficient vector, and ŷ _p is obtained by combining the individual predictors from different models. Prediction mean square errors of both predictors are derived. It is shown that the predictor ŷ _p is better than the individual predictors for k≥2 and the predictor ŷ _c is better than the individual predictors for k=2. Numerical comparison between ŷ _c and ŷ _p shows that the former is superior to the latter for the case k=2.     

Forecasting residential burglary

Following the work of Dhiri et al. [Modelling and predicting property crime trends. Home Office Research Study 198 (1999). London: HMSO] at the Home Office predicting recorded burglary and theft for England and Wales to the year 2001, econometric and time series models were constructed for predicting recorded residential burglary to the same date. A comparison between the Home Office econometric predictions and the less alarming econometric predictions made in this paper identified the differences as stemming from the particular set of variables used in the models. However, the Home Office and one of our econometric models adopted an error correction form which appeared to be the main reason why these models predicted increases in burglary. To identify the role of error correction in these models, time series models were built for the purpose of comparison, all of which predicted substantially lower numbers of residential burglaries. The years 1998–2001 appeared to offer an opportunity to test the utility of error correction models in the analysis of criminal behaviour. Subsequent to the forecasting exercise carried out in 1999, recorded outcomes have materialised, which point to the superiority of time series models compared to error correction models for the short-run forecasting of property crime. This result calls into question the concept of a long-run equilibrium relationship for crime.     

Asymmetric volatility in the foreign exchange markets

We examine the presence or absence of asymmetric volatility in the exchange rates of Australian dollar (AUD), Euro (EUR), British pound (GBP) and Japanese yen (JPY), all against US dollar. Our investigation is based on a variant of the heterogeneous autoregressive realized volatility model, using daily realized variance and return series from 1996 to 2004. We find that a depreciation against USD leads to significantly greater volatility than an appreciation for AUD and GBP, whereas the opposite is true for JPY. Relative to volatility on days following a positive one-standard-deviation return, volatility on days following a negative one-standard-deviation return is higher by 6.6% for AUD, 6.1% for GBP, and 21.2% for JPY. The realized volatility of EUR appears to be symmetric. These results are robust to the removal of jump component from realized volatility and the sub-samplings defined by structural-changes. The asymmetry in AUD, GBP and JPY appears to be embedded in the continuous component of realized volatility rather than the jump component.     

Are Securitizations in Substance Sales or Secured Borrowings? Capital‐Market Evidence*

Two standard‐setting approaches have emerged globally to guide the choice of accounting for securitizations: the control and components approach (SFAS No. 125 and SFAS No. 140) and the risks and rewards transfer approach (IAS No. 39). A lack of consensus about derecognition accounting is a major impediment to achieving convergence in global standards that must be resolved. Thus, both SFAS No. 140 and IAS No. 39 will be reexamined, and evidence pertinent to the debate is timely and important. In this study, we present evidence consistent with the view of credit‐rating analysts, who view many securitizations as, in substance, secured borrowings. Specifically, for a sample of originators applying sale accounting guidance in SFAS No. 125 / 140 during the period 1997‐2003, we show that off‐balance‐sheet debt related to securitizations has, on average, the same risk‐relevance for explaining market measures of risk (that is, CAPM beta) as on‐balance‐sheet debt. We also find that, in a returns and earnings association framework, the pricing multiple on securitization gains declines as the amount of off‐balance‐sheet debt increases, implying that investors take off‐balance‐sheet debt into account when assessing the valuation‐relevance of such gains. For those who advocate the control and components approach to securitization accounting, our results suggest that, at least for frequent securitizers, the put option arising from implicit recourse is a “missing piece” that is not currently accounted for when calculating securitization gains. Our results challenge the extant measurement standards in SFAS No. 140.     

Demand for broad money and expenditure components in Japan: an empirical study

The main objective of the present study is to examine empirically the long-run relation of broad money demand and its determinants in Japan. In contrast with previous study, the present study considers various components of final expenditure demand as determinants that are final consumption goods, expenditure on investment goods and exports. Using quarterly data over the period 1973Q1–2000Q2, the results of the bounds test [J. Appl. Econ. 16 (2001) 289] indicate a stable long-run relationship between demand of real M2 and its determinants. The empirical results also highlight that different domestic demand components yields different effects on Japanese broad money demand behavior. The estimated unrestricted error-correction model appears to track the data well and the results have important policy implications.     

Analysis of cointegrated VARMA processes

VARMA (vector autoregressive moving average) processes are proposed for modelling cointegrated variables. For this purpose the echelon form is combined with the error correction form. Procedures for estimating the Kronecker indices which characterize the echelon form and for specifying the cointegration rank are discussed. The asymptotic distribution of the coefficient estimators is given. An example based o n US macroeconomic data illustrates the procedure and demonstrates its feasibility in practice.     

物流产业评价指标与方法的探讨

本文首先对物流产业的概念进行界定,提出物流产业的判断基准;然后根据物流产业的内涵进行评价指标的设计,主要包括物流产业规模、效益、结构、资源、市场潜力等五个大类指标;最后运用主成分分析法建立物流产业的评价模型.     

Macroeconomic forecasting using structural factor analysis

The use of a small number of underlying factors to summarize the information from a much larger set of information variables is one of the new frontiers in forecasting. In prior work, the estimated factors have not usually had a structural interpretation and the factors have not been chosen on a theoretical basis. In this paper we propose several variants of a general structural factor forecasting model, and use these to forecast certain key macroeconomic variables. We make the choice of factors more structurally meaningful by estimating factors from subsets of information variables, where these variables can be assigned to subsets on the basis of economic theory. We compare the forecasting performance of the structural factor forecasting model with that of a univariate AR model, a standard VAR model, and some non-structural factor forecasting models. The results suggest that our structural factor forecasting model performs significantly better in forecasting real activity variables, especially at short horizons.