Revisions in analysts' earnings forecasts: evidence of non‐linear adaptive expectations

Prior studies use a linear adaptive expectations model to describe how analysts revise their forecasts of future earnings in response to current forecast errors. However, research shows that extreme forecast errors are less likely than small forecast errors to persist in future years. If analysts recognize this property, their marginal forecast revisions should decrease with the forecast error's magnitude. Therefore, a linear model is likely to be unsatisfactory at describing analysts' forecast revisions. We find that a non‐linear model better describes the relation between analysts' forecast revisions and their forecast errors, and provides a richer theoretical framework for explaining analysts' forecasting behaviour. Our results are consistent with analysts' recognizing the permanent and temporary nature of forecast errors of differing magnitudes. Copyright © 2000 John Wiley & Sons, Ltd.     

It starts at home? Climate policies targeting household consumption and behavioral decisions are key to low-carbon futures

Through their consumption behavior, households are responsible for 72% of global greenhouse gas emissions. Thus, they are key actors in reaching the 1.5 °C goal under the Paris Agreement. However, the possible contribution and position of households in climate policies is neither well understood, nor do households receive sufficiently high priority in current climate policy strategies. This paper investigates how behavioral change can achieve a substantial reduction in greenhouse gas emissions in European high-income countries. It uses theoretical thinking and some core results from the HOPE research project, which investigated household preferences for reducing emissions in four European cities in France, Germany, Norway and Sweden. The paper makes five major points: First, car and plane mobility, meat and dairy consumption, as well as heating are the most dominant components of household footprints. Second, household living situations (demographics, size of home) greatly influence the household potential to reduce their footprint, even more than country or city location. Third, household decisions can be sequential and temporally dynamic, shifting through different phases such as childhood, adulthood, and illness. Fourth, short term voluntary efforts will not be sufficient by themselves to reach the drastic reductions needed to achieve the 1.5 °C goal; instead, households need a regulatory framework supporting their behavioral changes. Fifth, there is a mismatch between the roles and responsibilities conveyed by current climate policies and household perceptions of responsibility. We then conclude with further recommendations for research and policy.     

Environmental impacts of household appliances in Europe and scenarios for their impact reduction

Assessing the environmental impact due to consumption of goods and services is a pivotal step towards achieving the sustainable development goal related to responsible production and consumption (i.e. SDG 12). Household appliances plays a crucial role and should be assessed in a systemic manner, namely considering all life cycle stages, technological efficiency, and affluence aspects. The present study assess the impact of such household appliances used in Europe, and tests scenarios of potential impact reduction at various scales. Life cycle assessment is applied to 14 different household appliances (ranging from dishwashers to television devices) selected to build a set of representative products, based on their economic value and diffusion in households in Europe. Related impacts are calculated with the Environmental Footprint method for calculating a Consumer Footprint “appliances” for the baseline year 2010. A number of scenarios encompassing eco-solutions on a technical level, changes in consumption pattern, behavioral changes, as well as the combination of all these aspects are run to estimate the Consumer Footprint related to household appliances for the year 2030, compared against this baseline scenario. The baseline Consumer Footprint is confirming the importance of the use phase in leading the impacts in almost all impact categories. Testing different scenarios concludes that there is a reduction of the impact for most of the categories (with up to 67% for the ozone depletion potential, and still around 35% for the global warming potential), while two of the here examined impact categories (i.e. land-use and mineral resource depletion) show an overall potential that is even negative – i.e. the results of all scenarios are higher than the ones of the 2010 baseline scenario. The increase in purchase and use of such appliances may offset energy efficiency benefits in some of the examined categories. Hence, the assessment of sustainability of appliances consumption should always include several scales, from the efficiency of the products (micro scale), to the improvement of the energy mix (meso scale), up to accounting for socio-economic drivers and patterns of consumption affecting the overall appliances stock (macro scale).     

利益冲突对分析师盈利预测的影响

选取2007-2009年度分析师对于上市公司每股收益的盈利预测数据,衡量预测误差的影响因素,重点考察分析师与基金经理的利益冲突对其的影响。采用多元线性回归模型及二元选择Logit模型,发现基金重仓股的盈利预测误差高于其他股票,但是误差来源并非来自于乐观倾向,而是来自其悲观倾向。分析师和基金经理之间的利益冲突对其盈利预测的影响不是很大。分析师对于盈利突变的情况难以把握是造成预测偏差的重要影响因素。     

Environmental impacts of household appliances in Europe and scenarios for their impact reduction

Assessing the environmental impact due to consumption of goods and services is a pivotal step towards achieving the sustainable development goal related to responsible production and consumption (i.e. SDG 12). Household appliances plays a crucial role and should be assessed in a systemic manner, namely considering all life cycle stages, technological efficiency, and affluence aspects. The present study assess the impact of such household appliances used in Europe, and tests scenarios of potential impact reduction at various scales. Life cycle assessment is applied to 14 different household appliances (ranging from dishwashers to television devices) selected to build a set of representative products, based on their economic value and diffusion in households in Europe. Related impacts are calculated with the Environmental Footprint method for calculating a Consumer Footprint “appliances” for the baseline year 2010. A number of scenarios encompassing eco-solutions on a technical level, changes in consumption pattern, behavioral changes, as well as the combination of all these aspects are run to estimate the Consumer Footprint related to household appliances for the year 2030, compared against this baseline scenario. The baseline Consumer Footprint is confirming the importance of the use phase in leading the impacts in almost all impact categories. Testing different scenarios concludes that there is a reduction of the impact for most of the categories (with up to 67% for the ozone depletion potential, and still around 35% for the global warming potential), while two of the here examined impact categories (i.e. land-use and mineral resource depletion) show an overall potential that is even negative – i.e. the results of all scenarios are higher than the ones of the 2010 baseline scenario. The increase in purchase and use of such appliances may offset energy efficiency benefits in some of the examined categories. Hence, the assessment of sustainability of appliances consumption should always include several scales, from the efficiency of the products (micro scale), to the improvement of the energy mix (meso scale), up to accounting for socio-economic drivers and patterns of consumption affecting the overall appliances stock (macro scale).     

How well do analysts forecast interest rates?

This paper examines interest rate forecasts made for the period 1982–90 and examines three issues: (1) Is there a general agreement among analysts about the level of interest rates six months in the future? (2) Are all the forecasters equally good? (3) Are the forecasts valuable to prospective users? We use distributions of the cross-sections of forecasts, Friedman's statistic for analysis of variance by rank, and tests of independence between forecasts and outcomes to examine these questions. We conclude that there usually was a consensus among analysts, that there was no significant difference in the ability to forecast short-term rates but there was a difference with respect to the long-term predictions, and that these forecasts were not significantly better than random walk forecasts.     

人工神经网络在储层地质建模中的应用研究

在油田注水开发过程中,储层物性、微观孔隙结构和非均质性都会发生动态变化。通过综合应用多学科的理论方法,利用计算机手段来构建地质模型,研究不同开发环境中储层参数的变化和规律,对预测储层剩余油的分布规律、提高油田开发效果具有十分重要的地质意义。人工神经网络技术具有极强的自适应和自学习能力,其通过很强的非线性映射,能够精确地建立储层参数与测井响应之间的非线性模型。在地质模型中,历史储层资料的基础上,采用神经网络技术,对大量宏观储层数据进行分析、学习与训练,选取具有代表性的储层参数,表示出各井点储层参数随时间的演变规律,进而有效预测剩余油的分布。     

Linear combination of restrictions and forecasts in time series analysis

An important tool in time series analysis is that of combining information in an optimal way. Here we establish a basic combining rule of linear predictors and show that such problems as forecast updating, missing value estimation, restricted forecasting with binding constraints, analysis of outliers and temporal disaggregation can be viewed as problems of optimal linear combination of restrictions and forecasts. A compatibility test statistic is also provided as a companion tool to check that the linear restrictions are compatible with the forecasts generated from the historical data. Copyright © 2000 John Wiley & Sons, Ltd.     

Model‐based forecast adjustment: With an illustration to inflation

This paper introduces the idea of adjusting forecasts from a linear time series model where the adjustment relies on the assumption that this linear model is an approximation of a nonlinear time series model. This way of creating forecasts could be convenient when inference for a nonlinear model is impossible, complicated or unreliable in small samples. The size of the forecast adjustment can be based on the estimation results for the linear model and on other data properties such as the first few moments or autocorrelations. An illustration is given for a first‐order diagonal bilinear time series model, which in certain properties can be approximated by a linear ARMA(1, 1) model. For this case, the forecast adjustment is easy to derive, which is convenient as the particular bilinear model is indeed cumbersome to analyze in practice. An application to a range of inflation series for low‐income countries shows that such adjustment can lead to some improved forecasts, although the gain is small for this particular bilinear time series model.     

Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation

Large Bayesian vector autoregressions with the natural conjugate prior are now routinely used for forecasting and structural analysis. It has been shown that selecting the prior hyperparameters in a data-driven manner can often substantially improve forecast performance. We propose a computationally efficient method to obtain the optimal hyperparameters based on automatic differentiation, which is an efficient way to compute derivatives. Using a large US data set, we show that using the optimal hyperparameter values leads to substantially better forecast performance. Moreover, the proposed method is much faster than the conventional grid-search approach, and is applicable in high-dimensional optimization problems. The new method thus provides a practical and systematic way to develop better shrinkage priors for forecasting in a data-rich environment.