One-sided Simultaneous Prediction Intervals for AR(1) and MA(1) Processes with Exponential Innovations

This paper is concerned with the determination of simultaneous confidence regions for some types of time series models. We derive recursive formulas which allow the determination of the probability for an AR(1) stationary process based on exponential inputs to lie under any sequence of constants during N steps. Also, probabilities of the same form are derived for an MA(1) process, based on an exponentially distributed white noise. Numerical results are obtained and comparison of prediction regions for different values of ϕ or θ is made. The results show how the use of the correlation structure of the models can reduce the confidence regions area. © 1997 by John Wiley & Sons, Ltd.     

On the existence of generalized inverse estimators in a singular system of equations

This paper deals with estimation problems in the context of singular systems of equations. It provides the necessary and sufficient conditions for the existence of restricted estimators as a routine extension of the standard theory of restricted least squares estimation. The paper also provides the means for carrying out tests of hypotheses on subsets of restrictions imposed on the system by explicitly providing an expression for the (appropriate) Lagrange multipliers.     

Modelling non-normal first-order autoregressive time series

We shall first review some non-normal stationary first-order autoregressive models. The models are constructed with a given marginal distribution (logistic, hyperbolic secant, exponential, Laplace, or gamma) and the requirement that the bivariate joint distribution of the generated process must be sufficiently simple so that the parameter estimation and forecasting problems of the models can be addressed. A model-building approach that consists of model identification, estimation, diagnostic checking, and forecasting is then discussed for this class of models.     

Recovering precision from seemingly redundant rounded data

Economic time series from the socialist economies of Eastern Europe and the Soviet Union are often not available. Instead, a matrix of rounded growth indexes for different base years is published. Using the constrained least squares and feasible GLS estimators developed in this paper, it is possible to predict the true matrix of growth indexes with greatly improved accuracy.     

On estimation and prediction procedures for ar(1) models with power transformation

The power transformation of Box and Cox (1964) has been shown to be quite useful in short-term forecasting for the linear regression model with AR(1) dependence structure (see, for example, Lee and Lu, 1987, 1989). It is crucial to have good estimates of the power transformation and serial. correlation parameters, because they form the basis for estimating other parameters and predicting future observations. The prediction of future observations is the main focus of this paper. We propose to estimate these two parameters by minimizing the mean squared prediction errors. These estimates and the corresponding predictions compare favourably, via revs and simulated data, with those obtained by the maximum likelihood method. Similar results are also demonstrated in the repeated measurements setting.     

Bushfire rate-of-spread forecasting: Deterministic and statistical approaches to fire modelling

The three basic modelling approaches used to explain forest fire behaviour are theoretically, laboratory or empirically based. Results of all three approaches are reviewed, but it is noted that only the laboratory- and empirically based models have led to forecasting techniques that are in widespread use. These are the Rothermel model and the McArthur meters, respectively. Field tests designed to test the performance of these operational models were carried out in tropical grasslands. A preliminary analysis indicated that the Rothermel model overpredicted spread rates while the McArthur model underpredicted. To improve the forecast of bushfire rate of spread available to operational firefighting crews it is suggested that a time-variable parameter (TYP) recursive least squares algorithm can be used to assign weights to the respective models, with the weights recursively updated as information on fire-front location becomes available. Results of this methodology when applied to US Grasslands fire experiment data indicate that the quality of the input combined with a priori knowledge of the performance of the candidate models plays an important role in the performance of the TVP algorithm. With high-quality input data, the Rothermel model on its own outperformed the TVP algorithm, but with slightly inferior data both approaches were comparable. Though the use of all available data in a multiple linear regression produces a lower sum of squared errors than the recursive, time-variable weighting approach, or that of any single model, the uncertainties of data input and consequent changes in weighting coefficients during operational conditions suggest the use of the TVP algorithm approach.     

A constrained least squares method for estimating the effects of an unknown monotonically intervening factor

A constrained least squares method is developed for the estimation of the effects of an unknown intervening causal factor in regression analysis, when the unknown factor shifts the regression hyperplane monotonically upwards (downwards) over time. As an illustration, we estimate the price elasticity of cigarettes in the USA and the systematic shifts of the demand curve for cigarettes during the time period 1964-86 (these shifts presumably reflecting the heightened awareness of the general public of the potential dangers of smoking).     

Structural Time-series Modelling of Monetary Aggregates: A Case Study for Eleven European Countries

Monetary aggregates for eleven European countries are analysed using the structural time-series methodology, paying special attention to unit root issues. Estimation of the parameters of the models is carried out by applying the asymptotic least squares (ALS) procedure. A comparison with the maximum likelihood estimates obtained via the Kalman filter shows that ALS is an alternative to Kalman filter estimation. The empirical results show that for only a small number of series the four variance parameters of the basic structural model are strictly positive. For the majority of the series the variance of the irregular component is equal to 0.©1997 John Wiley & Sons, Ltd.     

Bayes Inference for Technological Substitution Data with Data-based Transformation

Bayesian inference via Gibbs sampling is studied for forecasting technological substitutions. The Box–Cox transformation is applied to the time series AR(1) data to enhance the linear model fit. We compute Bayes point and interval estimates for each of the parameters from the Gibbs sampler. The unknown parameters are the regression coefficients, the power in the Box–Cox transformation, the serial correlation coefficient, and the variance of the disturbance terms. In addition, we forecast the future technological substitution rate and its interval. Model validation and model choice issues are also addressed. Two numerical examples with real data sets are given.©1997 John Wiley & Sons, Ltd.     

Observability of States in Non-Linear Systems

In this paper we define two new properties—linear observability and L-observability—to aid the study of overspecification and unidentifiability in quasi-linear and non-linear state space time series. Various equivalence results are proved and illustrated and some general properties of observable and unobservable processes are derived. The results are often proved by using graphical methods (influence diagrams) for manipulating conditional independence statements embedded in the new definitions. © 1997 John Wiley & Sons, Ltd.     

The usefulness of heuristic N(E)RLS algorithms for combining forecasts

There exists theoretical and empirical evidence on the efficiency and robustness of Non-negativity Restricted Least Squares combinations of forecasts. However, the computational complexity of the method hinders its widespread use in practice. We examine various optimizing and heuristic computational algorithms for estimating NRLS combination models and provide certain CPU-time reducing implementations. We empirically compare the combination weights identified by the alternative algorithms and their computational demands based on a total of more than 66,000 models estimated to combine the forecasts of 37 firm-specific accounting earnings series. The ex ante prediction accuracies of combined forecasts from the optimizing versus heuristic algorithms are compared. The effects of fit sample size, model specification, multicollinearity, correlations of forecast errors, and series and forecast variances on the relative accuracy of the optimizing versus heuristic algorithms are analysed. The results reveal that, in general, the computationally simple heuristic algorithms perform as well as the optimizing algorithms. No generalizable conclusions could be reached, however, about which algorithm should be used based on series and forecast characteristics. © 1997 John Wiley & Sons, Ltd.     

Distribution of HIV-1 in the genomes of AIDS patients

The localization of HIV-1 proviruses in compositional DNA fractions from 27 AIDS patients during the chronic phase of the disease with depletion of CD4+ and different levels of viremia showed the following. (1) At low viremia, proviruses are predominantly localized in the GC-richest isochores, which are characterized by an open chromatin structure; this result mimics findings on HIV-1 integration in early infected cells in culture. (2) At higher viremia, an increased distribution of proviruses in GC-poor isochores (which match the GC poorness of HIV-1) was found; this suggests a selection of cells in which the isopycnic localization leads to a higher expression of proviruses and, in turn, to higher viremia. (3) At the highest viremia, integrations in GC-rich isochores are often predominant again, but generally not at the same level as in (1); this may be the consequence of new integrations from the extremely abundant RNA copies.Received 21 November 2003; received after revision 13 January 2004: accepted 15 January 2004     

基于粗糙集与灰色动态预测模型在电力负荷短期预测的应用

针对电力系统多因素负荷预测问题的复杂性,结合粗糙集理论与GM（1,N）模型各自的优势,提出一种基于粗糙集理论的GM（1,N）预测模型.采取粗糙集理论对影响负荷预测因素进行简约,利用GM（1,N）建立简约后的因素变量和负荷之间的关系建立模型,并与GM（1,1）预测模型进行了比较,结果反映基于粗糙集理论的GM（1,N）预测模型的优越性,精准度达到94.055%.     

Inhibition of virus multiplication and alteration of cyclic AMP level in cell cultures by flavonoids

Summary The inhibitory effect of four flavonoid compounds on virus multiplication and their influence on the intracellular cyclic AMP (cAMP) level were studied in cell cultures. Quercetin and quercitrin reduced the yields ofHuman (alpha) herpesvirus 1 (HSV-1) andSuid (alpha) herpesvirus 1 (pseudorabies virus), but hesperidin and rutin had no effect. Further, quercetin and quercitrin elevated the intracellular level of cAMP, whereas hesperidin and rutin did not alter the cAMP level. Both antiviral activity and cAMP-enhancing effect were dependent on the concentrations of the flavonoids, and these effects turned out to be parallel.This study suggests that a relation exists between the antiviral effect and the cAMP-enhancing activity of flavonoids.