Information and energy policy preferences: a survey experiment on public opinion about electricity pricing reform in rural India

A common argument for the lack of economic reform in developing countries is popular opposition. If current economic policies are dysfunctional, could information about alternatives sway the voters? We examine if a simple argument emphasizing the need to increase electricity prices for improved supply can change public opinion in the case of India’s power sector reforms. The evidence comes from a survey experiment in rural Uttar Pradesh, which is both India’s largest state and has one of the lowest levels of household electrification. As expected, people respond to information about the relationship between electricity pricing, capacity investment, and reliability of supply by increasing their support for higher prices. However, no corresponding increase is observed for privatization of electricity generation. For external validity, we analyze an existing national survey on electricity privatization conducted in 2004/2005, finding patterns that support our argument.     

Forecasting electricity prices with expert,linear, and nonlinear models

This paper compares several models for forecasting regional hourly day-ahead electricity prices, while accounting for fundamental drivers. Forecasts of demand, in-feed from renewable energy sources, fossil fuel prices, and physical flows are all included in linear and nonlinear specifications, ranging in the class of ARFIMA-GARCH models—hence including parsimonious autoregressive specifications (known as expert-type models). The results support the adoption of a simple structure that is able to adapt to market conditions. Indeed, we include forecasted demand, wind and solar power, actual generation from hydro, biomass, and waste, weighted imports, and traditional fossil fuels. The inclusion of these exogenous regressors, in both the conditional mean and variance equations, outperforms in point and, especially, in density forecasting when the superior set of models is considered. Indeed, using the model confidence set and considering northern Italian prices, predictions indicate the strong predictive power of regressors, in particular in an expert model augmented for GARCH-type time-varying volatility. Finally, we find that using professional and more timely predictions of consumption and renewable energy sources improves the forecast accuracy of electricity prices more than using predictions publicly available to researchers.     

Vertical structure and the risk of rent extraction in the electricity industry

This paper studies how competition and vertical structure jointly determine generating capacities, retail prices, and welfare in the electricity industry. Analyzing a model in which demand is uncertain and retailers must commit to retail prices before they buy electricity in the wholesale market, we show that welfare is highest if competition in generation and retailing is combined with vertical separation. Vertically integrated generators choose excessively high retail prices and capacities to avoid rent extraction in the wholesale market when their retail demand exceeds their capacity. Vertical separation eliminates the risk of rent extraction and yields lower retail prices.     

A two-level electricity demand model : Evaluation of the connecticut time-of-day pricing test

A two-stage budgeting model is developed for electricity demand where comsumption in each period is treated as a different commodity. A relative household demand model is first estimated, a consistent price index for electricity is constructed, and then a total electricity consumption model is estimated. Economic procedures are derived which permit application of the model to both time-of-day price situations and also declining vlock price situatiions which result in non-linear budget sets. The model is applied to both types of situations- the data from the Connecticut time-of-day pricing test as well as data from the declining block rate situation of the prevoius year. The model is also tested in a forecasting application to time-of-day customers.     

Demography and the demand for housing A reinterpretation of the Mankiw-Weil demand variable

This paper argues that the Mankiw-Weil forecast of a 47% decline in house prices over the period to 2007 is based on a serious misinterpretation of their demand variable. In a time-series context this variable, which comes from a cross-section regression of house values against the age of occupants, is a measure of the adult population, not the demand for housing services or the stock of houses. Econometric work suggests that in addition to adult population, real income, relative prices and real interest rates are important factors determining the aggregate demand for housing. To be credible, forecasts of house prices need to integrate information on demand with information on cost factors and the supply of new houses.     

Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?

Recent electricity price forecasting studies have shown that decomposing a series of spot prices into a long-term trend-seasonal and a stochastic component, modeling them independently and then combining their forecasts, can yield more accurate point predictions than an approach in which the same regression or neural network model is calibrated to the prices themselves. Here, considering two novel extensions of this concept to probabilistic forecasting, we find that (i) efficiently calibrated non-linear autoregressive with exogenous variables (NARX) networks can outperform their autoregressive counterparts, even without combining forecasts from many runs, and that (ii) in terms of accuracy it is better to construct probabilistic forecasts directly from point predictions. However, if speed is a critical issue, running quantile regression on combined point forecasts (i.e., committee machines) may be an option worth considering. Finally, we confirm an earlier observation that averaging probabilities outperforms averaging quantiles when combining predictive distributions in electricity price forecasting.     

纯分时定价与分时阶梯定价对政策目标实现的对比分析

基于双层需求模型,对分时阶梯定价与纯分时定价下的居民电力需求行为进行比较。利用相对需求方程检验两类定价下的相对需求是否具有非位似偏好性质,运用绝对需求方程分析居民在峰谷时的不同弹性特征。研究结论表明,分时阶梯与纯分时定价下存在不同的收入和电费位似偏好性质;居民的价格与需求弹性显著不同;两类定价下可以实现的政策目标也存在明显差异。本文为系统比较非线性定价与线性定价下的需求奠定了基础。     

The pricing accuracy of alternative equity valuation models: Scandinavian evidence

In many decision contexts, there is a need for benchmark equity valuations, based on simplified modeling and publicly available information. Prior research on U.S. data however shows that the accuracy of such valuation models can be low and sensitive to the choice of model specifications and value driver predictions. In this paper, we test the applicability and pricing accuracy of three fundamental valuation (dividend discount, residual income, and abnormal earnings growth) models, all based on forecasts of company dividends, earnings, and/or equity book values. Extending prior research, we apply these models to Scandinavian firms with accounting data from the period 2005–2014, explicitly testing two approaches for the prediction of the value drivers—exogenously forecasted numbers versus projected historical numbers. Given access to the forecasted value drivers, the dividend discount model comes out as the most accurate valuation model. In particular, this holds in a comparison between the most parsimonious model specifications. The residual income valuation model generates the best pricing accuracy given the prediction of value drivers based on historical financial numbers. Notably, we observe pricing errors that in general are lower than what has been reported in prior U.S.‐based research for the dividend discount and the residual income valuation models. The pricing accuracy of the abnormal earnings growth models is surprisingly weak in the Scandinavian setting. However, these models improve somewhat after a couple of complexity adjustments, in particular with value driver predictions based on the projected history setting.     

A note on forecasting the prices of gold and silver: Asymmetric loss and forecast rationality

Given that the prices of gold and silver have witnessed large and substantial swings in recent years, policymakers and investors need readily available and reliable forecasts of the prices of these two precious metals. Survey data of forecasts of the prices of gold and silver provide a particularly rich data environment for policymakers and investors to study developments in the markets for gold and silver. Our research helps to develop a deeper understanding of the properties of survey data of the prices of gold and silver. We study the shape of forecasters’ loss function and the rationality of their forecasts. Assuming an asymmetric loss function weakens evidence against forecast rationality, but results depend on the empirical model being studied.     

Reverse Pricing and Revenue Sharing in a Vertical Market

Advancing in information technology has empowered firms with unprecedented flexibility when interacting with each other. We compare welfare results in a vertical market (e.g., manufacturers and retailers) for several types of pricing strategies depending upon the following: (1) which side (retailers or manufacturers) chooses retail prices; and (2) whether there is revenue sharing or linear pricing between the two sides. Our results are as follows. Under revenue sharing, retail prices (and thus industry profits) are higher if and only if they are chosen by the side featuring less competition. Under linear pricing, however, retail prices are higher if they are chosen by the side featuring more competition (for linear demand functions). Relative to linear pricing, revenue sharing always leads to lower retail prices, higher consumer surplus and social surplus. However, the comparison on industry profits depends on the demand elasticity ratios. Revenue sharing raises industry profits when the elasticity ratios are small, but the results are reversed when the elasticity ratios are large. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

Probabilistic forecasts of volatility and its risk premia

The object of this paper is to produce distributional forecasts of asset price volatility and its associated risk premia using a non-linear state space approach. Option and spot market information on the latent variance process is captured by using dual ‘model-free’ variance measures to define a bivariate observation equation in the state space model. The premium for variance diffusive risk is defined as linear in the latent variance (in the usual fashion) whilst the premium for variance jump risk is specified as a conditionally deterministic dynamic process, driven by a function of past measurements. The inferential approach adopted is Bayesian, implemented via a Markov chain Monte Carlo algorithm that caters for the multiple sources of non-linearity in the model and for the bivariate measure. The method is applied to spot and option price data on the S&P500 index from 1999 to 2008, with conclusions drawn about investors’ required compensation for variance risk during the recent financial turmoil. The accuracy of the probabilistic forecasts of the observable variance measures is demonstrated, and compared with that of forecasts yielded by alternative methods. To illustrate the benefits of the approach, it is used to produce forecasts of prices of derivatives on volatility itself. In addition, the posterior distribution is augmented by information on daily returns to produce value at risk predictions. Linking the variance risk premia to the risk aversion parameter in a representative agent model, probabilistic forecasts of (approximate) relative risk aversion are also produced.     

A copula-based time series model for global horizontal irradiation

The increasing importance of solar power for electricity generation leads to increasing demand for probabilistic forecasting of local and aggregated photovoltaic (PV) yields. Based on publicly available irradiation data, this paper uses an indirect modeling approach for hourly medium to long-term local PV yields. We suggest a time series model for global horizontal irradiation that allows for multivariate probabilistic forecasts for arbitrary time horizons. It features several important stylized facts. Sharp time-dependent lower and upper bounds of global horizontal irradiations are estimated. The parameters of the beta distributed marginals of the transformed data are allowed to be time-dependent. A copula-based time series model is introduced for the hourly and daily dependence structure based on simple vine copulas with so-called tail dependence. Evaluation methods based on scoring rules are used to compare the model’s power for multivariate probabilistic forecasting with other models used in the literature showing that our model outperforms other models in many respects.     

Equilibrium price and advertisement distributions

We consider an economy where many sellers sell identical goods to many buyers. Each seller has a unit supply and each buyer has a unit demand. The only possible information flow about prices is through costly advertising. We show that in equilibrium the sellers use mixed strategies in pricing which leads to price and advertisement distributions. With convex advertising costs each seller sends only one advertisement in the market. We also delineate a class of advertising costs which ensures that sellers may send multiple advertisements in equilibrium. Higher prices are advertised more than lower prices.     

Dynamic competitive retail pricing behavior with uncertainty and learning

When competing retailers lack full information about rivals' decision processes, how will dynamic pricing behavior vary from patterns observed in more traditional static or full-information models? We investigate this question in a dynamic alternating-moves duopoly model. Retailers update (linear) conjectures about rivals' future prices in a Bayesian fashion. We show that as observed and expected prices converge, a pricing equilibrium is always achieved, whether or not the conjectured and actual values of the slope of the rival's best response function are consistent. Assuming specific parameter values, we compare equilibrium prices and associated profits in our Bayesian learning model with those obtained under the assumptions of static Nash behavior, collusive behavior and dynamically optimal behavior with full information. We apply the notions of strategic substitutability and strategic complementarity to the analysis and find that when products are strategic complements, conjectures of higher rival price responsiveness lead to higher steady-state prices and profits. The reverse is true for strategic substitutes. We also find that learning about a rival's behavior proceeds more quickly, the less intensely related in demand are products. We find, in general, that equilibrium pricing patterns and profits can vary considerably from those in full-information environments, but that even with grossly wrong beliefs about rival behavior, competing retailers are still attracted to an equilibrium. The analysis suggests not only the value of investigating lessthan-full information situations but also the potential incremental value of signalling greater or less aggressiveness than truly characterizes one's behavior as a strategic option.     

Dimensionality reduction in forecasting with temporal hierarchies

Combining forecasts from multiple temporal aggregation levels exploits information differences and mitigates model uncertainty, while reconciliation ensures a unified prediction that supports aligned decisions at different horizons. It can be challenging to estimate the full cross-covariance matrix for a temporal hierarchy, which can easily be of very large dimension, yet it is difficult to know a priori which part of the error structure is most important. To address these issues, we propose to use eigendecomposition for dimensionality reduction when reconciling forecasts to extract as much information as possible from the error structure given the data available. We evaluate the proposed estimator in a simulation study and demonstrate its usefulness through applications to short-term electricity load and financial volatility forecasting. We find that accuracy can be improved uniformly across all aggregation levels, as the estimator achieves state-of-the-art accuracy while being applicable to hierarchies of all sizes.     

Price asymptotics

This paper examines the pricing decisions of a seller facing an unknown demand function. It is assumed that partial information, in the form of an independent random sample of values, is available. The optimal price for the inferred demand satisfies a consistency property—as the size of the sample increases, the maximum profit and price approach the values for the case where demand is known. The main results deduced here are asymptotics for prices. Prices converge at a rate of O _p (n ^−1/3) with a limit that can be expressed as a functional of a Gaussian process. Implications for the comparison of mechanisms are discussed.      

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.     

Price Discrimination in Input–Output

Modern economies are characterized by a great variety of pricing rules. Therefore, the commodities' prices and the primay factors' prices are discriminated in reality. However, in practical input–output tables, as well as in theoy, the prices are uniform and relative. In this work, an input–output model is described with absolute and different prices for final uses commodities and production's primay factors (wages, profits, etc.). In this case, the economic relationships are based on the commodities' demand curves for all the categories of final uses, and the factors' supply curves for all production's branches. In this way, we can establish the linkage and the feedback between factors' prices and quantities, and final uses commodities' prices and quantities. This is obtained by a novel interpretation of input–output, which approximates reality better.     

Evolving Electric Utility Regulatory Policy

A bstract . Current electric utility pricing methods understate the marginal social casts of electricity. Electricity prices are set to cover the utility's average cost rather than the higher marginal social cost. This mispricing hides from consumers the true cost their consumption imposes on society and, thereby, encourages them to ignore efficient conservation opportunities. Additionally, the conservation market suffers from a number of imperfections such as barriers to the acquisition of information, high upfront capital costs and the lack of conservation equipment availability. The electricity and conservation multimarket equilibrium is not achieved. The result is that society overconsumes (excess demand) electricity, overinvests in electric generating plants and underinvests (excess supply) in conservation resources. The large, yet uncertain, level of foregone conservation investment offers new opportunities for regulators and electric utility managers to improve economic efficiency with regulatory and planning policies that appropriately encourage the cost effective use of conservation resources. In the absence of the most efficient policy, marginal social cost pricing, integrated resource planning (IRP) is being adopted as a potential second-best regulatory policy and utility resource planning framework to improve energy efficiency. IRP uses mathematical optimization methods to search among many alternate resource portfolios of electricity creating and saving technologies. These methods identify the mix that best meets society's needs with the least social cost , where the social external costs and benefits of generating plant and conservation, respectively, are considered. Such a goal requires the choice of a resource portfolio that optimizes a complex objective function. As a result, the solution offers a resource action plan for electric utilities that may be Pareto-improving.