Evaluating volatility and interval forecasts

A widely used approach to evaluating volatility forecasts uses a regression framework which measures the bias and variance of the forecast. We show that the associated test for bias is inappropriate before introducing a more suitable procedure which is based on the test for bias in a conditional mean forecast. Although volatility has been the most common measure of the variability in a financial time series, in many situations confidence interval forecasts are required. We consider the evaluation of interval forecasts and present a regression‐based procedure which uses quantile regression to assess quantile estimator bias and variance. We use exchange rate data to illustrate the proposal by evaluating seven quantile estimators, one of which is a new non‐parametric autoregressive conditional heteroscedasticity quantile estimator. The empirical analysis shows that the new evaluation procedure provides useful insight into the quality of quantile estimators. Copyright © 1999 John Wiley & Sons, Ltd.     

连续模态Pushover分析法在钢交错桁架结构中的应用及改进

连续模态Pushover分析(Consecutive Modal Pushover,简称CMP)是一种能够考虑结构高阶振型的Pushover分析方法.为研究该法是否适用于钢交错桁架结构的抗震性能评估,分别时10层、15层和20层钢交错桁架结构体系进行CMP分析和非线性时程分析,并通过比较由CMP分析和非线性时程分析所得结构的反应,对CMP分析法提出相应的改进方案.分析结果表明,改进后的CMP分析法对于铜交错桁架结构的抗震性能评估是一种行之有效的方法.     

Murine primitive neuroectodermal tumor: nucleolar isolation and RNA polymerase inhibition

Summary A nucleolar isolation procedure was developed for the murine primitive neuroectodermal tumor originally induced with methylcholanthrene. This procedure utilizes sonication for breakage of the nuclei at a magnesium concentration which disperses the extranucleolar chromatin. The nucleoli retain RNA synthetic activity which is inhibited to about 90% by actinomycin D and the chemotherapeutic drug adriamycin.Acknowledgments. This work was supported in part by USPHS Grant CM-67058. We wish to thank Dr William Brooks for his generous support of this project.     

RNA polymerase activities in the isolated perfused rat liver

Summary The routine procedure for the isolation of rat liver induces a significant fall in RNA polymerase I and II activities which are rapidly restored to the control levels during perfusion.     

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.     

Detection of regime switches between stationary and nonstationary processes and economic forecasting

It often occurs that no model may be exactly right, and that different portions of the data may favour different models. The purpose of this paper is to propose a new procedure for the detection of regime switches between stationary and nonstationary processes in economic time series and to show its usefulness in economic forecasting. In the proposed procedure, time series observations are divided into several segments, and a stationary or nonstationary autoregressive model is fitted to each segment. The goodness of fit of the global model composed of these local models is evaluated using the corresponding information criterion, and the division which minimizes the information criterion defines the best model. Simulation and forecasting results show the efficacy and limitations of the proposed procedure. Copyright © 2005 John Wiley & Sons, Ltd.     

Subset selection of autoregressive time series models

We propose a solution to select promising subsets of autoregressive time series models for further consideration which follows up on the idea of the stochastic search variable selection procedure in George and McCulloch (1993). It is based on a Bayesian approach which is unconditional on the initial terms. The autoregression stepup is in the form of a hierarchical normal mixture model, where latent variables are used to identify the subset choice. The framework of our procedure is utilized by the Gibbs sampler, a Markov chain Monte Carlo method. The advantage of the method presented is computational: it is an alternative way to search over a potentially large set of possible subsets. The proposed method is illustrated with a simulated data as well as a real data. Copyright © 1999 John Wiley & Sons, Ltd.     

Chloral hydrate induced haploidization inAspergillus nidulans

Summary This is the first report of induction of haploidization inAspergillus nidulans by chloral hydrate, which is an efficient polyploidizing agent for higher plants and a psychotropic drug for man. A new procedure has been described to isolate haploids from diploids with a very high frequency, as compared top-fluorophenylalanine, which is generally used for this purpose.     

Forecasting the conditional covariance matrix of a portfolio under long‐run temporal dependence

Long‐range persistence in volatility is widely modelled and forecast in terms of the so‐called fractional integrated models. These models are mostly applied in the univariate framework, since the extension to the multivariate context of assets portfolios, while relevant, is not straightforward. We discuss and apply a procedure which is able to forecast the multivariate volatility of a portfolio including assets with long memory. The main advantage of this model is that it is feasible enough to be applied on large‐scale portfolios, solving the problem of dealing with extremely complex likelihood functions which typically arises in this context. An application of this procedure to a portfolio of five daily exchange rate series shows that the out‐of‐sample forecasts for the multivariate volatility are improved under several loss functions when the long‐range dependence property of the portfolio assets is explicitly accounted for. Copyright © 2006 John Wiley & Sons, Ltd.     

Neural model identification,variable selection and model adequacy

In recent years an impressive array of publications has appeared claiming considerable successes of neural networks in modelling financial data but sceptical practitioners and statisticians are still raising the question of whether neural networks really are ‘a major breakthrough or just a passing fad’. A major reason for this is the lack of procedures for performing tests for misspecified models, and tests of statistical significance for the various parameters that have been estimated, which makes it difficult to assess the model's significance and the possibility that any short‐term successes that are reported might be due to ‘data mining’. In this paper we describe a methodology for neural model identification which facilitates hypothesis testing at two levels: model adequacy and variable significance. The methodology includes a model selection procedure to produce consistent estimators, a variable selection procedure based on statistical significance and a model adequacy procedure based on residuals analysis. We propose a novel, computationally efficient scheme for estimating sampling variability of arbitrarily complex statistics for neural models and apply it to variable selection. The approach is based on sampling from the asymptotic distribution of the neural model's parameters (‘parametric sampling’). Controlled simulations are used for the analysis and evaluation of our model identification methodology. A case study in tactical asset allocation is used to demonstrate how the methodology can be applied to real‐life problems in a way analogous to stepwise forward regression analysis. Neural models are contrasted to multiple linear regression. The results indicate the presence of non‐linear relationships in modelling the equity premium. Copyright © 1999 John Wiley & Sons, Ltd.     

A simple enzymatic method for isolation of hepatocytes

Summary A new method is described for isolating hepatocytes by an enzymatic procedure: 0.05% bacterial collagenase in calcium-free Hanks' solution is injected into the liver tissue which is removed aseptically. The time required to prepare culture and risk of bacterial and fungal contamination is greatly reduced.     

A reliable method for obtaining longitudinal sections of sperms for electron microscopy

Summary A method is presented by which it is possible to obtain a high percentage of longitudinal sections through sperms for electron microscopy. The procedure is described in detail in order to permit easy reproduction.The technical help of Miss A. K. Baer is gratefully acknowledged.     

Modeling and forecasting time series with a general non-normal distribution

We propose a model for time series with a general marginal distribution given by the Johnson family of distributions. We investigate for which Johnson distributions forecasting using the model is likely to be most effective compared to using a linear model. Monte Carlo simulation is used to assess the reliability of methods for determining which of the three Johnson forms is most appropriate for a given series. Finally, we give model fitting and forecasting results using the modeling procedure on a selection of simulated and real time series.     

A simplified method for silanization of doublebarrelled ion-sensitive microelectrodes

Summary A simplified procedure is described for silanization of one shank of a doublebarrelled microelectrode. this procedure makes it possible to make more functioning electrodes in a shorter time.     

A simple method for sealing surgical skin wounds in young or new-born mice

Summary To close skin incisions in albino mice, a solution of polymethyl methacrylate in chloroform is recommended to be smeared over the wound, a procedure which virtually eliminates the incidence of maternal cannibalism.     

Variable selection in STAR models with neighbourhood effects using genetic algorithms

In this paper we deal with the problem of variable selection in spatiotemporal autoregressive (STAR) models with neighbourhood effects. We propose a procedure to carry out the selection process, taking into account the uncertainty associated with estimation of the parameters and the predictive behaviour of the compared models, in order to give more realism to the analysis. We set up a multi‐objective programming problem that combines the use of different criteria to measure both these aspects. We use genetic algorithms which are very flexible and suitable for our multicriteria decision problem. In particular, the procedure allows us to estimate the number of spatial and temporal nearest neighbours as well as their relative effects. The methodology is illustrated through an application to the real estate market of Zaragoza. Copyright © 2010 John Wiley & Son, Ltd.     

Forecasting with balanced state space representations of multivariate distributed lag models

A procedure for estimating state space models for multivariate distributed lag processes is described. It involves singular value decomposition techniques and yields an internally balanced state space representation which has attractive properties. Following the specifications of a forecasting competition, the approach is applied to generate ex-post forecasts for US real GNP growth rates. The forecasts of the estimated state space model are compared to those of twelve econometric models and an ARIMA model.     

System identification and validation for output prediction of a dynamic hydrologic process

The paper presents an identification procedure for a dynamic model of am hydrologic process. The process involves solute transport in streams subject to aquifer interaction and unsteady flows and the intended use of the model is prediction. Detailed assumptions and results are provided to illustrate the level of comprehensive analysis required to assess model adequacy. The assessment procedure easily generalizes to any dynamic model which is linear-in-the-parameters. As a fundamental tool, instrumental variable algorithms can be adopted which have a number of attractive features. These algorithms make both model-order identification and specification among alternatives a straightforward task. They are known to be consistent estimators in the presence of a wide class of errors. It is seen that they can be made stable and robust in the presence of data outliers. Instrumental variable algorithms can also be used which are asymptotically efficient and provide a covariance matrix of parameter estimates. The paper shows how they aid the quantification of predictive uncertainty and investigates the validity of the underlying assumptions. Further, it illustrates that, when instrumental variable algorithms are used in recursive mode, they can be used not only as an additional tool to access model inadequacy but also as an aid to model improvements.     

A DNA extraction procedure practicable under field work conditions

Summary A DNA extraction procedure is described which is designed to be used in field work. A crude initial cell precipitate, which is easily obtained, may endure storage of months.Acknowledgment. The skilful technical assistance by MissB. Kunz is gratefully acknowledged.     

Combining forecasts based on multiple encompassing tests in a macroeconomic core system

This paper investigates whether and to what extent multiple encompassing tests may help determine weights for forecast averaging in a standard vector autoregressive setting. To this end we consider a new test‐based procedure, which assigns non‐zero weights to candidate models that add information not covered by other models. The potential benefits of this procedure are explored in extensive Monte Carlo simulations using realistic designs that are adapted to UK and to French macroeconomic data, to which trivariate vector autoregressions (VAR) are fitted. Thus simulations rely on potential data‐generating mechanisms for macroeconomic data rather than on simple but artificial designs. We run two types of forecast ‘competitions’. In the first one, one of the model classes is the trivariate VAR, such that it contains the generating mechanism. In the second specification, none of the competing models contains the true structure. The simulation results show that the performance of test‐based averaging is comparable to uniform weighting of individual models. In one of our role model economies, test‐based averaging achieves advantages in small samples. In larger samples, pure prediction models outperform forecast averages. Copyright © 2010 John Wiley & Sons, Ltd.