Forecasting Spanish Natural Life Expectancy

Knowledge of trends in life expectancy is of major importance for policy planning. It is also a key indicator for assessing future development of life insurance products, substantiality of existing retirement schemes, and long-term care for the elderly. This article examines the feasibility of decomposing age-gender-specific accidental and natural mortality rates. We study this decomposition by using the Lee and Carter model. In particular, we fit the Poisson log-bilinear version of this model proposed by Wilmoth and Brouhns et al. to historical (1975-1998) Spanish mortality rates. In addition, by using the model introduced by Wilmoth and Valkonen we analyze mortality-gender differentials for accidental and natural rates. We present aggregated life expectancy forecasts compared with those constructed using nondecomposed mortality rates.     

Bayesian Reanalysis of the Challenger O-Ring Data

A Bayesian forecasting model is developed to quantify uncertainty about the postflight state of a field-joint primary O-ring (not damaged or damaged), given the O-ring temperature at the time of launch of the space shuttle Challenger in 1986. The crux of this problem is the enormous extrapolation that must be performed: 23 previous shuttle flights were launched at temperatures between 53 °F and 81 °F, but the next launch is planned at 31 °F. The fundamental advantage of the Bayesian model is its theoretic structure, which remains correct over the entire sample space of the predictor and that affords flexibility of implementation. A novel approach to extrapolating the input elements based on expert judgment is presented; it recognizes that extrapolation is equivalent to changing the conditioning of the model elements. The prior probability of O-ring damage can be assessed subjectively by experts following a nominal-interacting process in a group setting. The Bayesian model can output several posterior probabilities of O-ring damage, each conditional on the given temperature and on a different strength of the temperature effect hypothesis. A lower bound on, or a value of, the posterior probability can be selected for decision making consistently with expert judgment, which encapsulates engineering information, knowledge, and experience. The Bayesian forecasting model is posed as a replacement for the logistic regression and the nonparametric approach advocated in earlier analyses of the Challenger O-ring data. A comparison demonstrates the inherent deficiency of the generalized linear models for risk analyses that require (1) forecasting an event conditional on a predictor value outside the sampling interval, and (2) combining empirical evidence with expert judgment.     

A Note on Window Length Selection in Singular Spectrum Analysis

In singular spectrum analysis (SSA) window length is a critical tuning parameter that must be assigned by the practitioner. This paper provides a theoretical analysis of signal–noise separation and time series reconstruction in SSA that can serve as a guide to optimal window choice. We establish numerical bounds on the mean squared reconstruction error and present their almost sure limits under very general regularity conditions on the underlying data generating mechanism. We also provide asymptotic bounds for the mean squared separation error. Evidence obtained using simulation experiments and real data sets indicates that the theoretical properties are reflected in observed behaviour, even in relatively small samples, and the results indicate how, in practice, an optimal assignment for the window length can be made.     

折扣系数法在建立预测模型中的应用

本文针对不同观测点对预测值的不同影响,采用折扣系数法探讨了用折扣最小二乘法和折扣最小一乘法建立预测模型的方法以及用折扣系数法建立组合预测模型的方法。本文的方法不仅适合于一般趋势预测模型的建立,也可推广到一般回归预测模型的建立。这对于提高预测模型的预测精度有着重要的意义。     

A Benchmarking Approach to Forecast Combination

This article is concerned with the development of a statistical model-based approach to optimally combine forecasts derived from an extrapolative model, such as an autoregressive integrated moving average (ARIMA) time series model, with forecasts of a particular characteristic of the same series obtained from independent sources. The methods derived combine the strengths of all forecasting approaches considered in the combination scheme. The implications of the general theory are investigated in the context of some commonly encountered seasonal ARIMA models. An empirical example to illustrate the method is included.     

A SARIMAX coupled modelling applied to individual load curves intraday forecasting

A dynamic coupled modelling is investigated to take temperature into account in the individual energy consumption forecasting. The objective is both to avoid the inherent complexity of exhaustive SARIMAX models and to take advantage of the usual linear relation between energy consumption and temperature for thermosensitive customers. We first recall some issues related to individual load curves forecasting. Then, we propose and study the properties of a dynamic coupled modelling taking temperature into account as an exogenous contribution and its application to the intraday prediction of energy consumption. Finally, these theoretical results are illustrated on a real individual load curve. The authors discuss the relevance of such an approach and anticipate that it could form a substantial alternative to the commonly used methods for energy consumption forecasting of individual customers.     

Bayesian Inventory Management with Potential Change‐Points in Demand

We consider the inventory management problem of a firm reacting to potential change points in demand, which we define as known epochs at which the demand distribution may (or may not) abruptly change. Motivating examples include global news events (e.g., the 9/11 terrorist attacks), local events (e.g., the opening of a nearby attraction), or internal events (e.g., a product redesign). In the periods following such a potential change point in demand, a manager is torn between using a possibly obsolete demand model estimated from a long data history and using a model estimated from a short, recent history. We formulate a Bayesian inventory problem just after a potential change point. We pursue heuristic policies coupled with cost lower bounds, including a new lower bounding approach to non‐perishable Bayesian inventory problems that relaxes the dependence between physical demand and demand signals and that can be applied for a broad set of belief and demand distributions. Our numerical studies reveal small gaps between the costs implied by our heuristic solutions and our lower bounds. We also provide analytical and numerical sensitivity results suggesting that a manager worried about downside profit risk should err on the side of underestimating demand at a potential change point.     

等维新息灰色预测模型及预测精度比较——基于我国城镇居民养老保险预测分析

本文在对我国城镇居民养老保险分析和预测中引用了等维新息灰色预测模型并与传统灰色预测模型进行了预测精度比较,最终引入了等维新息处理的灰色预测模型。等维新息灰色预测在每一步预测中,不断推陈出新对原始数据进行等维新息处理。通过对实际案例研究证实,文中提出的预测模型可以在建模过程中成功地反映数据运动规律,具有合理、有效的中长期预测功能。笔者希望通过对我国城镇居民养老保险的灰色预测分析,为今后养老保险系统的后续研究打点基础。     

A comparative study of series arima/mlp hybrid models for stock price forecasting

ABSTRACT

Series hybrid models are one of the most widely-used hybrid models that in which a time series is assumed to be composed of two linear and nonlinear components. In this paper, the performance of two types of these hybrid models is evaluated for predicting stock prices in order to introduce the more reliable series hybrid model. For this purpose, ARIMA and MLPs are elected for constructing series hybrid models. Empirical results for forecasting three benchmark data sets indicate that despite of more popularity of the conventional ARIMA-ANN model, the ANN-ARIMA hybrid model can overall achieved more accurate results.     

基于波动择时绩效的高维波动率估计量与预测模型研究

对协方差矩阵高频估计量和预测模型的选择，共同影响协方差的预测效果，从而影响波动择时投资组合策略的绩效。资产维数很高时，协方差矩阵高频估计量的构建会因非同步交易而丢弃大量数据，降低信息利用效率。鉴于此，将可以充分利用资产日内价格信息的KEM估计量用于估计中国股市资产的高维协方差矩阵，并与两种常用协方差矩阵估计量进行比较。进一步地，将三种估计量分别用于多元异质自回归模型、指数加权移动平均模型以及短、中、长期移动平均模型进行样本外预测，并比较在三种基于风险的投资组合策略下的经济效益。采用上证50指数中20只不同流动性成份股逐笔高频数据的实证研究发现：（1）无论是在市场平稳时期还是市场剧烈震荡期，长期移动平均模型都是高维协方差估计量预测建模的最优选择，在应用于各种波动择时策略时都可以实现最低成本和最高收益。（2）在市场平稳时期，KEM估计量是高维协方差估计的最优选择，应用于各种波动择时策略时基本都可以实现最低成本和最高收益；在市场剧烈震荡期，使用KEM估计量进行波动择时仍然可以在成本方面保持优势，但在收益上并不占优。（3）无论是在市场平稳时期还是市场剧烈震荡期，最低的成本都是在采用等风险贡献投资组合时实现的，而最高的收益则都是在采用最小方差投资组合时实现的。研究不仅首次检验了KEM估计量在常用波动择时策略中的适用性，而且首次实证了实现最为简单的长期移动平均模型在高维协方差矩阵预测中的优越性，对投资决策和风险管理等实务应用都具有重要意义。