Industrial coal demand in China: A provincial analysis

The general concern on the environmental implications of the rising demand for coal registered in China during the last few years has induced considerable research effort to produce accurate forecasts of China's energy requirements. Nevertheless, no previous study has modelled the coal demand in China at provincial level. The aim of this paper is twofold. First, we estimate and forecast the Chinese demand for coal using panel data disaggregated by provinces and accounting for spatial heterogeneity. Second, given the spatial nature of the data, we explicitly capture the spatial autocorrelation among provinces using spatial econometrics. In particular, we specify the Chinese industrial coal demand at provincial level with fixed-effect spatial models. The empirical results show that the fixed-effect spatial ADL model is able to capture the existing interdependence between provinces. This model forecasts an average annual increase in coal demand to 2010 of nearly 2%.     

Car ownership and use in Britain: a comparison of the empirical results of alternative cointegration estimation methods and forecasts

This paper addresses two problems faced by many forecasters in the transport sector, namely how to use a relatively small sample to forecast car ownership over a long period of time and avoid the difficulties caused by spurious or nonsense regressions. Five alternative estimation methods are used to test for cointegrating relationships between per capita car ownership (and use) and real per capita personable disposable income, real motoring costs and real bus fares. These are the Engle-Granger two-stage, the Phillips-Hansen fully modified, the Wickens-Breusch one-stage, the autoregressive distributed lag, and the Johansen maximum likelihood methods. The corresponding error correction models are estimated, and a comparison made between the derived short- and long-run demand elasticities for car ownership and use. The ex-post forecasting performance of the error correction models, together with an ARIMA model specification, is evaluated using a number of performance criteria. The long-range time series forecasts obtained from the cointegrating regressions are compared with those from the cross-sectional approach used by the UK Department of the Environment, Transport and the Regions, and the policy implications discussed.     

中国能源需求向量自回归模型的建立与分析

随着我国经济的快速发展,对能源需求日益增加,在未来一段时期内,如何科学地预测我国能源需求量,对于保证经济的可持续发展、小康社会目标的实现与和谐社会的构建具有重要的现实意义。向量自回归模型是基于数据的统计性质建立模型,把系统中每一个内生变量作为系统中所有内生变量的滞后值的函数来构造模型,是处理多个相关经济指标的分析和预测最容易操作的模型之一,常用于预测相互联系的时间序列系统。能源需求量是由煤炭、石油、天然气等一次能源的消费量组成,他们之间存在着密切的联系,基于此,应用VAR模型对我国中长期的能源需求总量、煤炭、石油、天然气的消费量进行预测,为科学地制定能源发展战略提供理论指导。     

中国经济增长与煤炭消费结构的关系

基于对我国煤炭供需的基本分析,本文运用协整理论和误差修正模型建立中国煤炭消费的结构需求模型,并将中国煤炭消费的长期均衡引入到短期预测,从而得到经济增长的总量仍然在较大程度上依赖于煤炭资源的消耗.然而,从得到的误差修正模型来看,第二产业在煤炭消费上呈现出集约化和利用效率提高的趋势.利用格兰杰因果关系检验证实了以上结论.     

Introducing errors in progress ratios determined from experience curves

Progress ratios (PRs) derived from historical data in experience curves are used for forecasting development of many technologies as a means to model endogenous technical change in for instance climate–economy models. These forecasts are highly sensitive to uncertainties in the progress ratio. As a progress ratio is determined from fitting data, a coefficient of determination R² is frequently used to show the quality of the fit and accuracy of PR. Although this is instructive, we recommend using the error σ_PR in PR, which can be directly determined from fitting the data. In this paper we illustrate this approach for three renewable energy technologies, i.e., wind energy, bio-ethanol, and photovoltaics.     

MONTHLY GDP FORECASTING USING BRIDGE MODELS: APPLICATION FOR THE FRENCH ECONOMY

This paper presents a model to predict French gross domestic product (GDP) quarterly growth rate. The model is designed to be used on a monthly basis by integrating monthly economic information through bridge models, for both supply and demand sides, allowing thus economic interpretations. For each GDP component, bridge equations are specified by using a general‐to‐specific approach implemented in an automated way by Hoover and Perez and improved by Krolzig and Hendry. This approach allows to select explanatory variables among a large data set of hard and soft data. A rolling forecast study is carried out to assess the forecasting performance in the prediction of aggregated GDP, by taking publication lags into account in order to run pseudo real‐time forecasts. It turns out that the model outperforms benchmark models. The results show that changing the set of equations over the quarter is superior to keeping the same equations over time. In addition, GDP growth seems to be more precisely predicted from a supply‐side approach rather than a demand‐side approach.     

Analysis of the supply and demand in the TFT–LCD market

A methodology for the analysis of the supply and demand in the Thin-Film Transitor (TFT)-liquid crystal display (LCD) market in 2004 is proposed. The quarterly history supply and demand data are collected from 63 factories in Taiwan, Korea, Japan, and China during 2000–2003. This method takes into the account supply, demand, and differences between supply/demand. For the supply, a heuristic approach is used to forecast the future supply. For the demand, a transfer function model is used to forecast the future demand. The difference analysis of the supply and demand shows that it can predict whether or not there appears to be a shortage in the market of 2004. In addition, three important managerial implications such as pricing strategy, product-mix decision, and customer's priority are discussed in this paper.     

Seasonal models of peak electric load demand

Energy consumption in a pilgrim city belonging to a Gulf Cooperation Council (GCC) country exhibits strong seasonal pattern due to higher demand in summer season and additional load during the pilgrimage months. The pilgrimage month's timing is not fixed in the Gregorian calendar. The event varies according to the lunar calendar called the Hegira calendar, which lags behind the former by approximately 14 days in a year. Ten seasonal demand models are developed to model energy estimate for a GCC pilgrimage city. Among the long-range forecast models, three trigonometric models, a multiplicative model, and a multivariate model using categorical variables are considered. Further, a composite nonlinear model whose coefficients are nonlinear is suggested. This model combines the seasonality extracted from a multivariate regression model and a model that represents the peak electric load pattern. Adopting least square fit of a chi-square error function expanded by parabolic expansion, the parameters of the nonlinear model are identified. Moreover, smoothing-based techniques, such as moving average, double exponential smoothing, Winter's, and a multiplicative seasonal model, are suggested. The peak electric load model on lunar and solar calendars is closely related, and the deference in fitting error can be attributed to the magnitude of data. Computational results and statistical tests are presented to analyze the models. It is observed that the multiplicative model performs better to predict the peak electric load demand.     

Forecasting the probability of US recessions: a Probit and dynamic factor modelling approach

Abstract Quantifying the probability of U.S. recessions has become increasingly important since August 2007. In a data‐rich environment, this paper is the first to apply a Probit model to common factors extracted from a large set of explanatory variables to model and forecast recession probability. The results show the advantages of the proposed approach over many existing models. Simulated real‐time analysis captures all recessions since 1980. The proposed model also detects a significant jump in the next six‐month recession probability based on data up to November 2007, one year before the formal declaration of the recent recession by the NBER.     

A variance equality test of the ICAPM on Philippine stocks: post-Asian financial crisis period

Analysis of the data generated from various residential time-of-day electricity pricing projects often involves estimation of a system of demand equations. Both ad hoc demand models and neoclassical demand models have been estimated, often resulting in considerably different estimates of price elasticities. In this paper, elasticity estimates are presented from each type of demand model using data from the Arizona and North Carolina rate demonstration projects. The relationship between the elasticities generated from ad hoc models and separable neoclassical models is explored and the divergent price elasticity estimates are reconciled.     

我国电煤应急储备的必要性和可行性研究

我国是能源消耗大国,其中的电能主要来自火力发电,而电煤资源消耗是煤炭消耗的主要部分。由于突发性自然灾害、季节性需求变动以及价格波动等因素的影响,我国电煤供应在局部地区和局部时段会出现短缺现象,带来重大的经济损失,严重影响了我国能源应用安全。论述了电煤应急储备的内涵和相关理论,利用灰色预测模型对我国电煤需求作出了预测,同时,利用基尼系数法分析了我国煤炭分布不均衡的现象,在深入分析电煤供需现状的基础上,提出了我国建立电煤应急储备的必要性和可行性。     

Forecasting demand for a newly introduced product using reservation price data and Bayesian updating

Forecasting demand during the early stages of a product's life cycle is a difficult but essential task for the purposes of marketing and policymaking. This paper introduces a procedure to derive accurate forecasts for newly introduced products for which limited data are available. We begin with the assumption that the consumer reservation price is related to the timing with which the consumer adopts the product. The model is estimated using reservation price data derived through a consumer survey, and the forecast is updated with sales data as they become available using Bayes's rule. The proposed model's forecasting performance is compared with that of benchmark models (i.e., Bass model, logistic growth model, and a Bayesian model based on analogy) using 23 quarters' worth of data on South Korea's broadband Internet services market. The proposed model outperforms all benchmark models in both prelaunch and postlaunch forecasting tests, supporting the thesis that consumer reservation price can be used to forecast demand for a new product before or shortly after product launch.     

THE (AGGREGATE) DEMAND FOR STATE‐LOTTERY TICKETS: WHAT HAVE WE REALLY LEARNED?

Lottery‐demand models using aggregate data are often used to make inferences regarding individual behavior, the most important being the distributional burden of lottery‐ticket expenditures. It is shown here that estimates for the income elasticity and the cross‐price elasticity will only be representative of individual behavior under extremely restrictive assumptions. In fact, estimation of aggregate‐demand models presupposes that the income elasticity is equal to one. Cross‐sectional analyses using microlevel data face similar restrictions on consumer behavior. Remedies are discussed, but more conclusive evidence on the distributional burden of lotteries will remain elusive until better individual‐level data become available. (JEL D11, H71, H22)     

Nowcasting sales growth of manufacturing companies in India

The performance of private corporate sector is used as an important demand indicator for monetary policy making. As these data are received with a lag, assessing and monitoring of corporate sales on a real-time basis poses a significant challenge to policy makers in India. In this context, this article attempts to nowcast quarterly sales growth of Indian manufacturing companies and GDP growth of India using dynamic factor modelling framework. A multiple-level framework through turning point analysis and elastic net structure is used to overcome the overfitting problem during variable selection. Empirical results show improvement in forecast accuracy for one quarter ahead nowcast using 3-factor and 4-factor models over the benchmark model. However, absolute dominance of 3-factor models over 4-factor models was not established. As such, the article has proposed a forecast combination technique to nowcast sales growth of manufacturing companies in India.     

A generalized additive Tobit model

This paper proposes a semi-parametric approach to estimation in Tobit models. A generalized additive Tobit model of residential local long distance (intra-LATA) telephone demand is estimated on a cross-section of residential telephone consumers across twenty-eight states. While past studies of telecommunications demand have used fully parametric models, the model presented here is non-parametric in two dimensions: first no distributional assumption is made for the error distribution, and second, the demand equation is non-parametric with respect to price. We find that the elasticity of demand is substantially lower (in absolute value) that found in previous studies for a 40% cut in tariffs. First version received: July 2000/Final version received: March 2001 RID="*" ID="*"  I thank the referee and Associate Editor for suggestions which improved the paper. The views expressed here are of the author and not Analysis Group | Economics.     

A review of experience curve analyses for energy demand technologies

Transitioning towards a sustainable energy system requires the large-scale introduction of novel energy demand and supply technologies. Such novel technologies are often expensive at the point of their market introduction but eventually become cheaper due to technological learning. In order to quantify potentials for price and cost decline, the experience curve approach has been extensively applied to renewable and non-renewable energy supply technologies. However, its application to energy demand technologies is far less frequent. Here, we provide the first comprehensive review of experience curve analyses for energy demand technologies. We find a widespread trend towards declining prices and costs at an average learning rate of 18 ± 9%. This finding is consistent with the results for energy supply technologies and for manufacturing in general. Learning rates for individual energy demand technologies are symmetrically distributed around the arithmetic mean of the data sample. Absolute variation of learning rates within individual technology clusters of 7 ± 4%-points and between technology clusters of 7 ± 5%-points both contribute to the overall variability of learning rates. Our results show that technological learning is as important for energy demand technologies as it is for energy supply technologies. Applying the experience curve approach to forecast technology costs involves, however, unresolved uncertainties, as we demonstrate in a case study for the micro-cogeneration technology.     

A new energy model to capture the behavior of energy price processes

Owing to the vague fluctuation of energy prices from time to time, a new energy model, which considers both the mean-reverting behavior and the long memory property, is proposed in this paper. Since the problem of estimating parameters, in discrete time for this model, plays a central role in forecast inference, the problem of estimating the unknown parameters has been dealt with for the fractional Ornstein–Uhlenbeck process observed discretely. The asymptotic properties of these estimates are also provided. The numerical simulation results confirm the theoretical analysis and show that our method is effective. To show how to apply our approach in realistic contexts, an empirical study of energy in China, namely Daqing crude oil, is presented. The empirical results seem reasonable when compared to the real data.     

云贵煤炭基地“西煤东送”运输网络优化研究

云贵两省是南方地区的主要煤炭基地,而华南地区是重要的经济区和煤炭资源贫乏区,尤其是广东有着不断增长的煤炭消费需求,煤炭运输是两区域统筹解决的重要问题。对云贵煤炭基地的资源分布、生产趋势与两广地区的煤炭消费趋势进行分析,揭示了南方煤炭资源的供需矛盾;考察了云贵煤炭基地与华南地区间的煤炭运输网络特征与存在问题,以及两地区面临的新趋势与新矛盾。最后提出了云贵煤炭基地＂西煤东运＂运输网络的优化方案,指出煤炭铁水联运、水水联运和铁路运输是重要的优化途径。     

Analysis of electricity prices for Central American countries using dynamic conditional score models

In this paper, we compare the performance of dynamic conditional score (DCS) and standard financial time-series models for Central American energy prices. We extend the Student’s t and the exponential generalised beta distribution of the second kind stochastic location and stochastic seasonal DCS models. We consider the generalised t distribution as an alternative for the error term and also consider dynamic specifications of volatility. We use a unique dataset of spot electricity prices for El Salvador, Guatemala and Panama. We consider two data windows for each country, which are defined with respect to the liberalisation and development process of the energy market in Central America. We study the identification of a wide range of DCS specifications, likelihood-based model performance, time-series components of energy prices, maximum likelihood parameter estimates, the discounting property of conditional score, and out-of-sample forecast performance. Our main results are the following. (i) We determine the most robust models of energy prices, with respect to parameter identification, from a wide range of DCS specifications. (ii) For most of the cases, the in-sample statistical performance of DCS is superior to that of the standard model. (iii) For El Salvador and Panama, the standard model provides better point forecasts than DCS, and for Guatemala the point forecast precision of standard and DCS models does not differ significantly. (iv) For El Salvador, the standard model provides better density forecasts than DCS, and for Guatemala and Panama, the density forecast precision of standard and DCS models does not differ significantly.