A forecasting comparison of some var techniques

Higher dimensional multivariate time series models suffer from the problem of over-parametrisation which impairs their forecasting performance. Starting from such unrestricted vector autoregressive models the paper discusses two ways to cope with this difficulty. The first approach reduces the number of free parameters by applying a subset modelling strategy. The second approach takes a Bayesian point of view by formulating ‘priors’ which are then combined with sample information, but leaving the original specification unaltered. Using Austrian quarterly macroeconomic time series a comparative study is undertaken by running alternative forecasting exercises. Both methods improve out-of-sample forecasting performance substantially at the cost of some bias in ex-post simulations. Comparing the ex-ante predictions of the two approaches, the former does better at short horizons whereas the latter gains as the forecast horizon lengthens.     

项目进度绩效指标的改进及其动态性预测

传统的挣值方法中,进度指标的预测多表现单期、静态的特性,存在严重的局限性。为了改进和完善挣值管理方法中进度指标体系,在考虑时间序列的基础上,将统计方法引入挣值管理进度指标体系中,用更加科学的方法对项目进度指标进行描述,并在此基础上基于贝叶斯推断方法,加入专家意见,从统计学的角度提出了进度绩效指数的动态预测模型,因而,可以为项目管理者提供科学的据侧依据。     

中国上中下游价格传导机制研究* ——基于细分行业数据的实证分析

本文通过运用动态贝叶斯网络模型分析方法,测算了2000-2014年上中下游价格指数及各细分行业价格指数之间的传导效应,并分析了货币供给因素对不同价格指数的驱动作用。结果表明,货币供应量对上中下游所有价格指数均能产生影响,对下游价格指数CPI的驱动效应最显著;上中游价格指数向下游价格指数逐级传导效应显著,但跨级传导效应呈递减趋势;同时下游价格指数的变动也会倒逼中上游价格指数变化,但跨级传导效应基本无趋势性。     

Flexible estimation of price response functions using retail scanner data

Kalyanam and Shively [1998. Estimating irregular pricing effects: a stochastic spline regression approach. Journal of Marketing Research 35 (1), 16–29] and van Heerde et al. [2001. Semiparametric analysis to estimate the deal effect curve. Journal of Marketing Research 38 (2), 197–215] have demonstrated the usefulness of nonparametric regression to estimate pricing effects flexibly. The empirical results of these two studies, however, also revealed that nonparametric regression may suffer from too much flexibility leading to nonmonotonic shapes for price effects. In this paper, we show how the problem of nonmonotonicity can be dealt with without losing the power of flexible estimation techniques. We propose a semiparametric approach based on Bayesian P-splines with monotonicity constraints imposed on own- and cross-price effects. In an empirical application, we illustrate that flexible estimation of own- and cross-price effects can improve the predictive validity of a sales response model substantially, even when price response curves were constrained to show a monotonic shape, as suggested by economic theory. We also discuss the consequences from an unconstrained estimation of price effects.     

Hierarchical Bayesian conjoint models incorporating measurement uncertainty

The authors explore situations where consumers supplement their judgments with a measurement of uncertainty about their own preferences, either implicitly or explicitly, and develop two sets of hierarchical Bayesian conjoint models incorporating such measurements. The first set of models uses the relative location of a rating to determine the importance or weight given to the rating, in a regression setting. The second set uses interval judgment as a dependent variable in a regression setting. After specifying the models, the authors perform a theoretical comparison with a basic Bayesian regression model. They show that, under different conditions, the proposed models will yield more precise individual-level partworth estimates. Two simulated data examples and data from a conjoint study are used to illustrate the gains that could be obtained from modeling uncertainty. In the empirical application, the authors show that model fit improves when ratings for items that respondents do not like are given more weight compared to ratings for items that they do like. Electronic Supplementary Material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

Diversity and business performance: 50 years of research

This study has carried out a review of the literature appearing on diversity in the last 50 years. Research findings from this period reveal it is impossible to assume there is a pure and simple relationship between diversity and performance without considering a series of variables that affect this relationship. In this study, emphasis has been placed on the analysis of results arrived at through empirical investigation on the relation between the most studied dimensions of diversity and performance. The results presented are part of a more extensive research.     

Closed-form Bayesian inferences for the logit model via polynomial expansions

Articles in Marketing and choice literatures have demonstrated the need for incorporating person-level heterogeneity into behavioral models (e.g., logit models for multiple binary outcomes as studied here). However, the logit likelihood extended with a population distribution of heterogeneity doesn’t yield closed-form inferences, and therefore numerical integration techniques are relied upon (e.g., MCMC methods). We present here an alternative, closed-form Bayesian inferences for the logit model, which we obtain by approximating the logit likelihood via a polynomial expansion, and then positing a distribution of heterogeneity from a flexible family that is now conjugate and integrable. For problems where the response coefficients are independent, choosing the Gamma distribution leads to rapidly convergent closed-form expansions; if there are correlations among the coefficients one can still obtain rapidly convergent closed-form expansions by positing a distribution of heterogeneity from a Multivariate Gamma distribution. The solution then comes from the moment generating function of the Multivariate Gamma distribution or in general from the multivariate heterogeneity distribution assumed. Closed-form Bayesian inferences, derivatives (useful for elasticity calculations), population distribution parameter estimates (useful for summarization) and starting values (useful for complicated algorithms) are hence directly available. Two simulation studies demonstrate the efficacy of our approach. JEL Classification C6 · C8 · M3     

当代贝叶斯计量经济学分析框架与展望

随着贝叶斯理论的发展和计算机模拟等数值计算技术的提高,贝叶斯计量经济学开始迅速发展起来。本文通过对经典学派与贝叶斯学派进行比较,简要回顾了贝叶斯计量经济学的发展历程,并从八个方面对贝叶斯计量经济学研究过程中的分析框架进行说明,最后进行了展望。     

Role of Forgetting in Memory-Based Choice Decisions: A Structural Model

We propose a structural model to investigate the impact of forgetting on consumers' brand choice decisions in frequently purchased products. Forgetting results in consumers imperfectly recalling their prior brand evaluations when making a purchase decision in the category. We conceptualize the imperfect recall by positing that consumers recall their prior evaluations with noise. Based on prior research in the behavioral area, we characterize the extent of forgetting as an increasing and concave function of time. Our framework generates analytical results on the impact of forgetting on consumers' brand evaluations and their consequent purchase behavior. We calibrate our model using scanner panel data for liquid detergents. Furthermore, we obtain insights into the consumers' extent of forgetting in the category, extent of learning, predicted price elasticities and implications on state dependence and habit persistence. Our results underscore the importance of modeling consumers' ability to recall only imperfectly.     

基于EM-NB算法的网络调查缺失数据处理方法

将最大期望值算法(EM)与朴素贝叶斯算法(NB)相结合,提出EM-NB算法来填补网络调查中的缺失数据。对比基于处理后的完备数据集的分类统计结果与基于纸质调查得到的分析结果,结果显示,利用EM-NB算法处理缺失数据后的网络调查问卷与纸质调查问卷可得到一致的调查结果。这表明EM-NB算法是一种有效的处理网络调查中缺失数据的方法。