Consistent forecast intervals when the forecast-period exogenous variables are stochastic

Derivation of prediction intervals in the k-variable regression model is problematic when future-period values of exogenous variables are not known with certainty. Even in the most favourable case when the forecasts of the exogenous variables are jointly normal, the distribution of the forecast error is non-normal, and thus traditional asymptotic normal theory does not apply. This paper presents an alternative bootstrap method. In contrast to the traditional predictor of the future value of the endogenous variable, which is known to be inconsistent, the bootstrap predictor converges weakly to the true value. Monte Carlo results show that the bootstrap prediction intervals can achieve approximately nominal coverage.     

Asymptotic normal and bootstrap inference in structural VAR analysis

The aim of the paper is to examine the performance of bootstrap and asymptotic parametric inference methods in structural VAR analysis. The results obtained through a Monte Carlo experiment suggest that the two approaches are largely equivalent in most, but not all, cases. While the asymptotic method turns out to be surprisingly robust with respect to the distribution of the errors, the bootstrap does deliver results superior in terms of both length of the confidence interval and coverage when highly non-linear statistics (such as the components of the variance of the forecast error) are considered.     

Does geographic location matter to stock return predictability?

Building on recent and growing evidence that geographic location influences information diffusion, this paper examines the relation between firm's location and the predictability of stock returns. We hypothesize that returns on a portfolio composed of firms located in central areas are more likely to follow a random walk than returns on a portfolio composed of firms located in remote areas. Using a battery of variance ratio tests, we find strong and robust support for our prediction. In particular, we show that the returns on a portfolio composed of the 500 largest urban firms follow a random walk; however, all variance ratio tests reject the random walk hypothesis for a portfolio that includes the 500 largest rural firms. Our results are robust to alternative definitions of firm's location and portfolio formation.     

Forecasting an aggregate of cointegrated disaggregates

This study examines the problem of forecasting an aggregate of cointegrated disaggregates. It first establishes conditions under which forecasts of an aggregate variable obtained from a disaggregate VECM will be equal to those from an aggregate, univariate time series model, and develops a simple procedure for testing those conditions. The paper then uses Monte Carlo simulations to show, for a finite sample, that the proposed test has good size and power properties and that whether a model satisfies the aggregation conditions is closely related to out‐of‐sample forecast performance. The paper then shows that ignoring cointegration and specifying the disaggregate model as a VAR in differences can significantly affect analyses of aggregation, with the VAR‐based test for aggregation possibly leading to faulty inference and the differenced VAR forecasts potentially understating the benefits of disaggregate information. Finally, analysis of an empirical problem confirms the basic results. Copyright © 2000 John Wiley & Sons, Ltd.     

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.     

The forecasting attributes of trend- and difference-stationary representations for macroeconomic time series

We analyse the forecasting attributes of trenc and diffence-stationary representations of the U.S. macroeconomic time series sudied by Nelson and Plosser (1982). Predictive densities based on models estimated for these series (which terminate in 1970) are compared with subsequent realizations compiled by Schotman and van Dijk (1991) which terminate in (1988). Predictive densities obtained using the, extended series are also derived to assess the impact of the subsequent realization on long-range forecasts. Of particular interest are comparisons of the average intervals of predictive densities corresponding to the competing specifications In general, we find that coverage intervals based on diference-stationary specifications are far wider than those based or. trend-stationary specifications for the real series, and slightly wider for the nominal series. This additional width is often a virtue in forecasting nuninal series over the 1971-1988 period, as the inflation experienced durnig this time was unprecedented in the 1900s. However, the evolution of the real series has been relatively stable in the 1900s, hence the uncertainty associated with difference-stationary specifications generally seems excessive for these data.     

Estimating and Forecasting APARCH‐Skew‐t Model by Wavelet Support Vector Machines

This paper concentrates on comparing estimation and forecasting ability of quasi‐maximum likelihood (QML) and support vector machines (SVM) for financial data. The financial series are fitted into a family of asymmetric power ARCH (APARCH) models. As the skewness and kurtosis are common characteristics of the financial series, a skew‐t distributed innovation is assumed to model the fat tail and asymmetry. Prior research indicates that the QML estimator for the APARCH model is inefficient when the data distribution shows departure from normality, so the current paper utilizes the semi‐parametric‐based SVM method and shows that it is more efficient than the QML under the skewed Student's‐t distributed error. As the SVM is a kernel‐based technique, we further investigate its performance by applying separately a Gaussian kernel and a wavelet kernel. The results suggest that the SVM‐based method generally performs better than QML for both in‐sample and out‐of‐sample data. The outcomes also highlight the fact that the wavelet kernel outperforms the Gaussian kernel with lower forecasting error, better generation capability and more computation efficiency. Copyright © 2014 John Wiley & Sons, Ltd.     

某型反舰导弹弹道规划仿真系统

为了提高反舰导弹的作战能力,需要对导弹的航迹进行合理规划。在对反舰导弹的弹道特点和导引方法进行认真分析的基础上,建立了方案弹道、导引弹道、反舰导弹六自由度闭环数学模型,在VC环境下调用OpenGL图形程序接口,开发了用于进行弹道规划的三维可视化仿真系统。通过对某型反舰导弹弹道进行验证,仿真结果表明本系统能较好地满足要求。     

Forecasting from fallible data: Correcting prediction bias with stein-rule least squares

In the presence of fallible data, standard estimation and forecasting techniques are biased and inconsistent. Surprisingly, the magnitude of this bias tends to increase, and not diminish, in time series applications as more observations become available. A solution to this ever-present problem, Stein-rule least squares (SRLS), is offered. It corrects for the bias and inconsistency of traditional estimators and provides a means for significantly improving the predictive accuracy of regression-based forecasting techniques. A Monte Carlo study of the forecasting accuracy of SRLS, compared to its alternatives reveals its practical significance and small sample behaviour.