Electricity derivatives pricing with forward-looking information

In order to increase overall transparency on key operational information, power transmission system operators publish an increasing amount of fundamental data, including forecasts of electricity demand and available capacity. We employ a fundamental model for electricity prices which lends itself well to integrating such forecasts, while retaining ease of implementation and tractability to allow for analytic derivatives pricing formulae. In an extensive futures pricing study, the pricing performance of our model is shown to further improve based on the inclusion of electricity demand and capacity forecasts, thus confirming the general importance of forward-looking information for electricity derivatives pricing. However, we also find that the usefulness of integrating forecast data into the pricing approach is primarily limited to those periods during which electricity prices are highly sensitive to demand or available capacity, whereas the impact is less visible when fuel prices are the primary underlying driver to prices instead.     

Evidence on the motivation for management forecasts of corporate earnings

A multivariate model of the process by which managers decide to release public forecasts of their firms' earnings is developed, based on factors that are hypothesized to affect the demand for and the willingness to supply such forecasts. We test the model on data from a comprehensive sample of earnings forecasts, and find support for our hypotheses about the likely joint influence of those factors. Larger firm size, greater leverage, higher and more stable earnings rates and less rapid growth rates are found to be associated with an increased propensity for management to provide earnings forecasts for their firms.     

A microeconomic planning model for the new york times newspaper

The New York Times model is a large-scale model which forecasts sales and earnings for the New York Times newspaper. Sturcturally, it is composed of two major blocks; a demand module, and a production, cost and revenue module. The demand module, the heart of the model, is a set of simultaneous nonlinear econometric equations which forecast physical volume, approximately 35 categories of advertising lines and 10 categories of circulation. The second block is recursive and contains roughly 300 equations, some of which are stochastic behavioral equations. This block converts the volume forecasts into paging, newsprint consumption, newsprint distribution and manning requirements. These physical flows are then monetized, using price and wage forecasts, to produce estimates of revenue, fixed and variable costs, and operating profit. This paper summarizes the development of the model, with emphasis on the advertising and circulation model. It should be noted that the structure of the model is constantly evolving. Consequently, emphasis is placed on the conceptual underpinnings of the model not on a detailed presentation of the current structure.     

Reconciled boosted models for GEFCom2017 hierarchical probabilistic load forecasting

When forecasting time series in a hierarchical configuration, it is necessary to ensure that the forecasts reconcile at all levels. The 2017 Global Energy Forecasting Competition (GEFCom2017) focused on addressing this topic. Quantile forecasts for eight zones and two aggregated zones in New England were required for every hour of a future month. This paper presents a new methodology for forecasting quantiles in a hierarchy which outperforms a commonly-used benchmark model. A simulation-based approach was used to generate demand forecasts. Adjustments were made to each of the demand simulations to ensure that all zonal forecasts reconciled appropriately, and a weighted reconciliation approach was implemented to ensure that the bottom-level zonal forecasts summed correctly to the aggregated zonal forecasts. We show that reconciling in this manner improves the forecast accuracy. A discussion of the results and modelling performances is presented, and brief reviews of hierarchical time series forecasting and gradient boosting are also included.     

A general method for addressing forecasting uncertainty in inventory models

In practice, inventory decisions depend heavily on demand forecasts, but the literature typically assumes that demand distributions are known. This means that estimates are substituted directly for the unknown parameters, leading to insufficient safety stocks, stock-outs, low service, and high costs. We propose a framework for addressing this estimation uncertainty that is applicable to any inventory model, demand distribution, and parameter estimator. The estimation errors are modeled and a predictive lead time demand distribution obtained, which is then substituted into the inventory model. We illustrate this framework for several different demand models. When the estimates are based on ten observations, the relative savings are typically between 10% and 30% for mean-stationary demand. However, the savings are larger when the estimates are based on fewer observations, when backorders are costlier, or when the lead time is longer. In the presence of a trend, the savings are between 50% and 80% for several scenarios.     

城市物流需求供给研究

对物流需求进行分析的同时,构建了较为全面的物流需求指标体系,同时利用多种预测方法对各个指标进行了动态预测,最后建立了物流需求供给匹配模型。     

Long term electricity consumption forecast in Brazil: A fuzzy logic approach

The energy companies are always facing the challenge of producing more accurate load forecasts. A fuzzy logic methodology is proposed in order to extract rules from the input variables and provide Brazil's long-term annual electricity demand forecasts. In recent literature, the formulation of these types of models has been limited to treating the explanatory variables in the univariate form, or involving only the GDP. This study proposes an extension of this model, starting with population and the GDP additional value. The proposed model is compared with the official projections. The obtained results are quite promising.     

北京市生态足迹的变动与预测分析

本文将投入产出分析技术引入生态足迹模型,使用北京市历年投入产出表数据对1995年到2005年期间北京市生态足迹规模的变化、主要影响因素进行分析并做出情景预测。结果表明:北京市生态足迹的最终需求、流入量与流出量都持续增加,农业和其他服务业的生态足迹部门构成呈下降趋势,其他部门均有所提高;资源生产能力的提高,使生态足迹有减少的趋势;最终需求变化,是引起生态足迹增加的最主要的因素;在未来,北京市产业发展落实三二一的发展方针,优化产业结构和经济发展模式后,能够更好地处理经济建设与资源利用、生态环境保护的关系。提出倡导创意农业、技术创新和促进生产性服务业发展的政策建议。     

Empirical tests of input-output forecasts

This paper analyzes previous studies of the accuracy of input-output forecasts as compared with projections derived from alternative forecasting techniques. The problem of constructing appropriate tests of input-output forecasts is discussed. Major tests of the interindustry approach and alternative techniques, such as final demand blowup, GNP blowup and multiple regression, conducted in the past four decades are reviewed and the major findings summarized. It is shown here that, contrary to the belief of some economists, the input-output forecasting model performs as well as and usually better than any of the alternatives considered.     

资兴市耕地资源潜力分析与可持续利用研究

文章在对湖南省资兴市耕地资源利用现状和潜力分析研究的基础上,应用灰色系统方法的GM(1,1)模型对资兴市未来耕地资源需求进行了预测,为资兴市土地资源可持续利用与发展提出对策建议。     

Forecasting tourist arrivals using time-varying parameter structural time series models

Empirical evidence has shown that seasonal patterns of tourism demand and the effects of various influencing factors on this demand tend to change over time. To forecast future tourism demand accurately requires appropriate modelling of these changes. Based on the structural time series model (STSM) and the time-varying parameter (TVP) regression approach, this study develops the causal STSM further by introducing TVP estimation of the explanatory variable coefficients, and therefore combines the merits of the STSM and TVP models. This new model, the TVP-STSM, is employed for modelling and forecasting quarterly tourist arrivals to Hong Kong from four key source markets: China, South Korea, the UK and the USA. The empirical results show that the TVP-STSM outperforms all seven competitors, including the basic and causal STSMs and the TVP model for one- to four-quarter-ahead ex post forecasts and one-quarter-ahead ex ante forecasts.     

Using weather ensemble predictions in electricity demand forecasting

Weather forecasts are an important input to many electricity demand forecasting models. This study investigates the use of weather ensemble predictions in electricity demand forecasting for lead times from 1 to 10 days ahead. A weather ensemble prediction consists of 51 scenarios for a weather variable. We use these scenarios to produce 51 scenarios for the weather-related component of electricity demand. The results show that the average of the demand scenarios is a more accurate demand forecast than that produced using traditional weather forecasts. We use the distribution of the demand scenarios to estimate the demand forecast uncertainty. This compares favourably with estimates produced using univariate volatility forecasting methods.     

多元回归时间序列方法在货运市场预测中的应用

为了解决制定某地区货运行业发展规划中的区域货运发展规模的分析和预测问题。提出一种以统计数据为依据,为货运行业需求量建立预测模型,并应用SPSS(Statistical Product Service Solution)分析软件,对预测结果进行评估检验的方法。最后以四川货运行业为例,研究预测了四川省今后数年的货运需求量,为四川货运部门进行决策提供一定的科学依据。     

Daily retail demand forecasting using machine learning with emphasis on calendric special days

Demand forecasting is an important task for retailers as it is required for various operational decisions. One key challenge is to forecast demand on special days that are subject to vastly different demand patterns than on regular days. We present the case of a bakery chain with an emphasis on special calendar days, for which we address the problem of forecasting the daily demand for different product categories at the store level. Such forecasts are an input for production and ordering decisions. We treat the forecasting problem as a supervised machine learning task and provide an evaluation of different methods, including artificial neural networks and gradient-boosted decision trees. In particular, we outline and discuss the possibility of formulating a classification instead of a regression problem. An empirical comparison with established approaches reveals the superiority of machine learning methods, while classification-based approaches outperform regression-based approaches. We also found that machine learning methods not only provide more accurate forecasts but are also more suitable for applications in a large-scale demand forecasting scenario that often occurs in the retail industry.     

基于灰色系统理论的常州市物流需求预测

地区性的物流需求预测对于制定宏观经济政策和促进地区经济发展具有重要意义。根据常州市近10年来的物流需求变化,建立了灰色预测的GM（1,1）模型,通过计算对常州市今后10年的物流需求进行了预测,为政府规划地区物流发展提供相关理论参考。     

Price formation in commodity markets

This paper develops a theory-consistent market model for storable commodities and illustrates its characterization of the data-generating process for a set of major traded commodities. The dynamics of the system incorporate recent advances in modelling techniques. Cointegrated variables in the demand functions are represented by the error correction mechanism (ECM), and expected prices in the stock demand relationship are generated by a rational expectations process. The outside-sample performance of the model is tested against the pure time-series model used to formulate expected prices, and is shown to have a smaller mean square error than that of the time-series model. Thus the model provides comparatively efficient forecasts and, unlike models constructed in their reduced form, permits consideration of key behavioural relationships in commodity markets.     

DeepAR: Probabilistic forecasting with autoregressive recurrent networks

Probabilistic forecasting, i.e., estimating a time series’ future probability distribution given its past, is a key enabler for optimizing business processes. In retail businesses, for example, probabilistic demand forecasts are crucial for having the right inventory available at the right time and in the right place. This paper proposes DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an autoregressive recurrent neural network model on a large number of related time series. We demonstrate how the application of deep learning techniques to forecasting can overcome many of the challenges that are faced by widely-used classical approaches to the problem. By means of extensive empirical evaluations on several real-world forecasting datasets, we show that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.     

Evaluating Point and Density Forecasts of DSGE Models

This paper investigates the accuracy of forecasts from four dynamic stochastic general equilibrium (DSGE) models for inflation, output growth and the federal funds rate using a real‐time dataset synchronized with the Fed's Greenbook projections. Conditioning the model forecasts on the Greenbook nowcasts leads to forecasts that are as accurate as the Greenbook projections for output growth and the federal funds rate. Only for inflation are the model forecasts dominated by the Greenbook projections. A comparison with forecasts from Bayesian vector autoregressions shows that the economic structure of the DSGE models which is useful for the interpretation of forecasts does not lower the accuracy of forecasts. Combining forecasts of several DSGE models increases precision in comparison to individual model forecasts. Comparing density forecasts with the actual distribution of observations shows that DSGE models overestimate uncertainty around point forecasts. Copyright © 2013 John Wiley & Sons, Ltd.     

Estimation and simulation of industry factor demand equations

A factor demand model derived from the Generalized Leontief Cost Function is outlined. The model is estimated for 53 U.S. industries which together make up the entire U.S. economy. The purpose of the model is to provide long-run forecasts of equipment investment and labor productivity within the context of an input-output forecasting model. Extensive discussion is devoted to selection of functional form, modeling of dynamics, and estimation technique, with special emphasis upon the considerations which uniquely confront the forecaster. An array of a priori constraints are imposed upon the estimation, reflecting a body of empirical evidence and theoretical requirements. In addition, elasticity estimates are reported, as well as evidence on the short-run simulation performance of the mode.     

基于灰色系统理论的湖南省物流需求预测分析

文中以灰色系统理论思想为指导,运用灰色GM(1,1)模型,以货运量表征物流规模。根据湖南省近10年来的物流需求变化,建立了灰色预测的GM(1,1)模型,预测湖南省今后5年的物流需求,为政府规划地区物流发展提供相关理论参考。