基于GLM的未决赔款准备金评估的随机性链梯法

为度量未决赔款准备金评估结果的波动性,需要研究随机性评估方法。基于GLM的随机性方法,得到准备金估计及预测均方误差。特别地,在过度分散泊松模型中,分别应用参数Bootstrap方法和非参数Bootstrap方法,得到两种方法下未决赔款准备金的预测分布,进而由该分布得到各个分位数以及其它分布度量,并通过精算实务中的数值实例应用R软件加以实证分析。实证结果表明,两种Bootstrap方法得到的参数误差、过程标准差、预测均方误差都与解析表示估计的结果很接近。     

A Phillips curve interpretation of error-correction models of the wage and price dynamics

This paper presents a model of employment, distribution andinflation in which a modern error correction specification ofthe nominal wage and price dynamics (referring to claims onincome by workers and firms) occupies a prominent role. It isbrought out, explicitly, how this rather typical error-correctionsetting, which actually seems to capture the wage and pricedynamics of many large-scale econometric models quite well,is fully compatible with the notion of an old-fashioned Phillipscurve with finite slope. It is shown how the steady-state impactof various shocks to the model can be profitably conceived ofand interpreted in terms of (and to some extent even calculatedby means of) this long-run Phillips curve.     

行为生产:提高预测力的新视角

随着对行为理论研究的深入,行为生产成为生产领域的一个新的研究方向。通过对近10年来国内外有关文献的整理,分析了生产管理与人力资源管理两学科的内在联系,梳理了生产管理和行为理论的研究脉络,提出了对传统生产理论中经济学假设的质疑,最后对行为生产的研究方向和知识结构框架提出了一个初步的设想。     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

Environmental regulation and trade pattern：a case of China

As a growing number of countries, including both developed and developing countries, have in recent yearstaken environmental regulation at different levels, a question of great concern has been raised： can the regulation alterthe existing trade volume and trade pattern, and ultimately drive pollution-intensive industries to countries with low-levelregulations or even those without regulations at all？ Starting from the three different propositions concerning therelationship between environmental regulation and trade pattern, this paper applies cointegration analysis and errorcorrection model to empirically testing the relationship between environmental regulation and trade in China during theperiod of 1985-2005. Our empirical results indicate that in the short run the collection of pollution discharge fees bearsa positive impact on the export share of clean products of total exports. Thus, higher pollution discharge fees raise theratio of clean products exports to total exports. This further indicates that more stringent environmental regulationpromotes the exports of clean products. In the long run pollution discharge fees are positively correlated with the exportshare of clean products but negatively associated with their import share. Such correlations imply that environmentalregulation tends to facilitate the international specialization in line with comparative advantages.     

Comparing linear and nonlinear forecasts for stock returns

We compare the out-of-sample performance of monthly returns forecasts for two indices, namely the Dow Jones (DJ) and the Financial Times (FT) indices. A linear and a nonlinear artificial neural network (ANN) model are used to generate the out-of-sample competing forecasts for monthly returns. Stationary transformations of dividends and trading volume are considered as fundamental explanatory variables in the linear model and the input variables in the ANN model. The comparison of out-of-sample forecasts is done on the basis of forecast accuracy, using the Diebold and Mariano test [J. Bus. Econ. Stat. 13 (1995) 253.], and forecast encompassing, using the Clements and Hendry approach [J. Forecast. 5 (1998) 559.]. The results suggest that the out-of-sample ANN forecasts are significantly more accurate than linear forecasts of both indices. Furthermore, the ANN forecasts can explain the forecast errors of the linear model for both indices, while the linear model cannot explain the forecast errors of the ANN in either of the two indices. Overall, the results indicate that the inclusion of nonlinear terms in the relation between stock returns and fundamentals is important in out-of-sample forecasting. This conclusion is consistent with the view that the relation between stock returns and fundamentals is nonlinear.     

铁路专用线噪声调查与预测特点分析

铁路专用线与国铁相比具有线路长度较短、技术标准较低、运输密度较小等特点。在分析铁路专用线噪声特点的基础上,提出铁路专用线应选取等效声级为噪声评价量,在采用模式预测法进行铁路专用线噪声预测时,应关注各主要修正参数,为科学、客观评价专用线噪声提供依据,同时以某铁路专用线为例进行噪声分析。     

Efficient estimation of forecast uncertainty based on recent forecast errors

Multi-step-ahead forecasts of the forecast uncertainty of an individual forecaster are often based on the horizon-specific sample means of his recent squared forecast errors, where the number of past forecast errors available decreases one-to-one with the forecast horizon. In this paper, the efficiency gains from the joint estimation of forecast uncertainty for all horizons in such samples are investigated. If the forecast uncertainty is estimated by seemingly unrelated regressions, it turns out that the covariance matrix of the squared forecast errors does not have to be estimated, but simply needs to have a certain structure, which is a very useful property in small samples. Considering optimal and non-optimal forecasts, it is found that the efficiency gains can be substantial for longer horizons in small samples. The superior performance of the seemingly-unrelated-regressions approach is confirmed in several empirical applications.     

基于模糊时间序列的预测模型——以上证指数为例

模糊理论使用语义变量本身所蕴含的特性,能减少处理问题时的不确定性所带来的困扰,被广泛的应用于各种领域的研究。首先回顾了基于模糊理论的模糊时间序列定义,对现有的模糊时间序列模型进行分析;在此基础上提出了一种新的模糊时间序列预测方法,以上证指数为对象进行了拟合。从结果看,新的基于模糊时间序列预测方法在MSN、平均误差(%)和标准误差(%)等指标上要优于现有的的预测方法。     

Forecasting inflation with an uncertain output gap

The output gap is a crucial concept in the monetary policy framework, indicating demand pressure that generates inflation. However, its definition and estimation raise a number of theoretical and empirical questions. This paper evaluates a series of univariate and multivariate methods for extracting the output gap in Norway, and compares their value added in predicting inflation. We find that models including the output gap have better predictive power than models based on alternative indicators, and they forecast significantly better than simple benchmark models. Furthermore multivariate measures of the output gap perform better than the univariate gaps. Comments from two anonymous referees, Q. Farooq Akram, Tommy Sveen, Ken West, Fredrik Wulfsberg and seminar participants in Norges Bank are gratefully acknowledged. All mistakes remain our own. The views expressed are those of the authors and do not necessarily represent those of Norges Bank.