seasonal factors and outlier effects in rate of return on electricity spot prices in australia s national electricity market this article documents seasonal patterns and other characteristics of electricity spot prices in the australian national electricity market nem over a 7 year sample period the goal is to investigate more specifically the influence of seasonalities and outliers noted in the body of literature on electricity prices the results confirm that electricity prices exhibit significant time of day and day of week effects and monthly and yearly effects are significant to a lesser degree extremely high spikes in the price series are an important characteristic of electricity prices and are shown to be a highly significant component of returns behaviour negative prices are unusual in financial time series data but occur in australian electricity prices and are found to be influential on returns the implications of these finding are that seasonal and outlier effects should not be ignored in efforts to model electricity prices

discovering price load relationships in california s electricity market this paper reports on characterizing recent price behavior in the califomia electricity market market participants that is producers consumers and traders are highly motivated by the potential for profits to develop strategies to explore and exploit the limits of system operation these strategies should be reflected in the market as different price to load relationships we show that a number of regimes i e characteristic behaviors exist in the price time series and provide a brief analysis of each regime knowledge of the number of regimes their characteristics and switching dynamics allows insight into the market and power system performance

modeling and forecasting electricity prices with input output hidden markov models in competitive electricity markets in addition to the uncertainty of exogenous variables such as energy demand water inflows and availability of generation units and fuel costs participants are faced with the uncertainty of their competitors behavior the analysis of electricity price time series reflects a switching nature related to discrete changes in competitors strategies which can be represented by a set of dynamic models sequenced together by a markov chain an input output hidden markov model iohmm is proposed for analyzing and forecasting electricity spot prices the model provides both good predictions in terms of accuracy as well as dynamic information about the market in this way different market states are identified and characterized by their more relevant explanatory variables moreover a conditional probability transition matrix governs the probabilities of remaining in the same state or changing to another whenever a new market session is opened the model has been successfully applied to real clearing prices in the spanish electricity market

construction of an efficient portfolio of power purchase decisions based on risk diversification tradeoff abstract we present a methodology based on the tradeoff between risk and diversification in order to evaluate a purchase portfolio of energy where the assets refer to purchasing strategies of a retailer generator of electricity in three markets spot regulated and non regulated markets we use two measures of diversification i entropy based on factors constructed by principal components analysis and ii entropy based on asset risk in each case weights for each strategy are estimated by using the interior point method for which monthly forecasts of returns are calculated a year ahead for each market spot prices are modeled using an arima model and bilateral contracts are modeled using growth rates we compare risk diversified portfolios with mean variance portfolio although diversification does not necessarily mean a lower risk we show that the mean variance portfolio s risk is not always lower than the risk diversified approaches also we show that diversification converges to one for the highest risk portfolio but this does not happen in the case of entropy based on factors because one asset can participate in more than one principal component clearly the mean variance approach is unable to perform a diversified allocation these results are useful for retailer generators who want combine the criteria of risk and diversification

an adaptive wavelet neural network based energy price forecasting in electricity markets in a competitive electricity market an accurate forecasting of energy prices is an important activity for all the market participants either for developing bidding strategies or for making investment decisions an adaptive wavelet neural network awnn is proposed in this paper for short term price forecasting stpf in the electricity markets a commonly used mexican hat wavelet has been chosen as the activation function for hidden layer neurons of feed forward neural network ffnn to demonstrate the effectiveness of the proposed approach day ahead prediction of market clearing price mcp of spain market which is a duopoly market with a dominant player and locational marginal price lmp forecasting in pjm electricity market are considered the forecasted results clearly show that awnn has good prediction properties compared to other forecasting techniques such as wavelet arima multi layer perceptron mlp and radial basis function rbf neural networks as well as recently proposed fuzzy neural network fnn

stochastic joint optimization of wind generation and pumped storage units in an electricity market one of the main characteristics of wind power is the inherent variability and unpredictability of the generation source even in the short term to cope with this drawback hydro pumped storage units have been proposed in the literature as a good complement to wind generation due to their ability to manage positive and negative energy imbalances over time this paper investigates the combined optimization of a wind farm and a pumped storage facility from the point of view of a generation company in a market environment the optimization model is formulated as a two stage stochastic programming problem with two random parameters market prices and wind generation the optimal bids for the day ahead spot market are the ldquohere and nowrdquo decisions while the optimal operation of the facilities are the recourse variables a joint configuration is modeled and compared with an uncoordinated operation a realistic example case is presented where the developed models are tested with satisfactory results

how giving affects giving a long term analysis of donations social preferences theories suggest that at least some people show altruistic behaviour however this philanthropic behaviour ranges from pure altruistic preferences to reluctant altruists the fa

on the market impact of wind energy forecasts this paper presents an analysis of how day ahead electricity spot prices are affected by day ahead wind power forecasts demonstration of this relationship is given as a test case for the western danish price area of the nord pool s elspot market impact on the average price behaviour is investigated as well as that on the distributional properties of the price by using a non parametric regression model to assess the effects of wind power forecasts on the average behaviour the non linearities and time variations in the relationship are captured well and the effects are shown to be quite substantial furthermore by evaluating the distributional properties of the spot prices under different scenarios the impact of the wind power forecasts on the price distribution is proved to be considerable the conditional price distribution is moreover shown to be non gaussian this implies that forecasting models for electricity spot prices for which parameters are estimated by a least squares techniques will not have gaussian residuals hence the widespread assumption of gaussian residuals from electricity spot price models is shown to be inadequate for these model types the revealed effects are likely to be observable and qualitatively similar in other day ahead electricity markets significantly penetrated by wind power

short term variations and long term dynamics in commodity prices in this article we develop a two factor model of commodity prices that allows meanreversion in short term prices and uncertainty in the equilibrium level to which prices revert although these two factors are not directly observable they may be estimated from spot and futures prices intuitively movements in prices for long maturity futures contracts provide information about the equilibrium price level and differences between the prices for the short and long term contracts provide information about short term variations in prices we show that although this model does not explicitly consider changes in convenience yields over time this short term long term model is equivalent to the stochastic convenience yield model developed in gibson and schwartz 1990 we estimate the parameters of the model using prices for oil futures contracts and apply the model to some hypothetical oil linked assets to demonstrate its use and some of its advantages over the gibson schwartz model

location and the multinational enterprise a neglected factor this article first traces the changing world economic scenario for international business over the past two decades and then goes on to examine its implications for the location of foreign direct investment and multinational enterprise activity it suggests that many of the explanations of the 1970s and early 1980s need to be modified as firm specific assets have become mobile across natural boundaries a final section of the article examines the dynamic interface between the value added activities of multinational enterprises in different locations

ireland s emerging information economy recent trends and future prospects grimes s 2003 ireland s emerging information economy recent trends and future prospects reg studies 37 3 14 this paper examines the emergence of information economy related activity through inward investment and the indigenous sector in one of europe s most peripheral regions ireland s recent economic resurgence has benefited considerably from international corporate restructuring as us multinationals in particular reconfigured their operations within europe s single market despite the on going concern about over dependence on fdi policy makers in ireland seek to ensure that both skills and infrastructural requirements are adequately provided in order to solidify ireland s attractiveness for investment while at the same time encouraging the growth of indigenous high technology companies although internationally traded services sectors such as software financial services telemarketing and shared services have expanded considerably in recent years the overall level of regionalization of th

application of public domain market information to forecast ontario s wholesale electricity prices this paper evaluates the usefulness of publicly available electricity market information in forecasting the hourly ontario energy price hoep in order to do so relevant data from ontario and its neighboring electricity markets namely new york new england and pjm electricity markets are investigated and a final set of explanatory variable candidates that are available before real time are selected multivariate transfer function and dynamic regression models are employed to relate hoep behavior to the selected explanatory variable candidates univariate arima models are also developed for the hoep the hoep models are developed on the basis of two forecasting horizons i e 3 h and 24 h and forecasting performance of the multivariate models is compared with that of the univariate models the outcomes show that the market information publicly available before real time can be used to improve hoep forecast accuracy to some extent however unusually high or low prices remain unpredictable and hence the available data cannot lead to significantly more accurate forecasts nevertheless the generated forecasts in this paper are significantly more accurate than currently available hoep forecasts to analyze the relatively low accuracy of the hoep forecasts comparisons are made with respect to arima models developed for locational marginal prices lmps of ontario s three neighboring markets and price volatility analyses are presented

modeling real time balancing power market prices using combined sarima and markov processes this paper describes modeling of real time balancing power market prices by using combined seasonal auto regressive integrated moving average sarima and discrete markov processes the combination of such processes allows generation of price series with periods where no demand for balancing power exists the purpose of the model is simulation of prices to construct scenario trees representing possible realization of the stochastic prices such scenario trees can be used in planning models based on stochastic optimization to generate bid sequences to the balancing market the spread of the prices in the tree and the shape of the scenarios are of central importance model parameter estimation methods reflecting the demands on scenario trees have therefore been used the proposed model is also applied to data from the nordic power market the conclusion of this paper is that the developed model is appropriate for modeling real time balancing power prices

transnational firms and the changing organisation of innovative activities it is suggested that a transition is taking place towards new modes of organising transnational corporations innovative activities first different units of multinational firms including foreign based subsidiaries are increasingly involved in the generation use and transmission of knowledge secondly multinationals are developing external networks of relationships with local counterparts through which foreign affiliates gain access to external knowledge sources and application abilities as a result of this evolutionary process multinationals organisation is subject to both centripetal and centrifugal forces considerable efforts are then necessary to innovate coordination procedures and mechanisms in order to enhance the generation circulation and use of knowledge a number of empirical works are reviewed providing some evidence of the evolutionary process discussed in the paper 1 transnational companies as networks of innovators economists have long viewed innovation as being primarily generated by firms in their home countries classic contributions in the economics of multinational corporations have consolidated this view referring mainly to the us based multinationals vernon 1966 kindleberger 1969 and stopford and wells 1972 theorised a quasi colonial relationship between the parent company and foreign subsidiaries wherein the latter are in charge of replicating the former s activities abroad with strategic decisions including r d and innovation strategies being rigidly centralised particularly vernon emphasised that coordinating international innovative activities would be too costly owing to the difficulties of collecting and controlling relevant information across national borders host countries and foreign subsidiaries would then play a role almost exclusively in the adoption and diffusion of centrally created technology this view was supported mainly by us economists and based on us evidence from the early post world war ii period but it was very influential for the development of studies on the economics of transnational companies tncs and the internationalisation of

hmm filtering and parameter estimation of an electricity spot price model abstract in this paper we develop a model for electricity spot price dynamics the spot price is assumed to follow an exponential ornstein uhlenbeck ou process with an added compound poisson process in this way the model allows for mean reversion and possible jumps all parameters are modulated by a hidden markov chain in discrete time they are able to switch between different economic regimes representing the interaction of various factors through the application of reference probability technique adaptive filters are derived which in turn provide optimal estimates for the state of the markov chain and related quantities of the observation process the em algorithm is applied to find optimal estimates of the model parameters in terms of the recursive filters we implement this self calibrating model on a deseasonalised series of daily spot electricity prices from the nordic exchange nord pool on the basis of one step ahead forecasts we found that the model is able to capture the empirical characteristics of nord pool spot prices

charity donations in the uk new evidence based on panel data abstract this paper presents a set of panel data estimates for 159 of the most prominent uk charities for the period 1983 1990 in evaluating alternative specifications we settle on a fixed effects estimation social welfare charities are shown to fund raise short of the point at which net revenues are maximized whereas health and overseas charities are found to net revenue maximize religious charities maximize total revenues estimates of price and fund raising elasticities are presented for charities overall and for four cohorts for alternative income sources no evidence of crowding out is found

reducing electricity price forecasting error using seasonality and higher order crossing information commodity prices are volatile and their volatility changes over time electricity prices are even more volatile in general because customers cannot easily switch to other energy source when the electricity prices fluctuate electricity prices also have strong seasonal behavior because the demand of electricity depends on the season to address these characteristics a time series forecasting model with seasonality and higher order crossing information for electricity prices is proposed the model measures the similarity between two time series by using their higher order crossing information the model can predict not only day ahead prices short term forecasting but also a series of prices for one week ahead or even longer simultaneously medium term price curve forecasting the model is tested on both high and low volatile data sets to evaluate its robustness and generality it is also shown that the model can produce more accurate prediction than some well known models

electricity market price forecasting based on weighted nearest neighbors techniques this paper presents a simple technique to forecast next day electricity market prices based on the weighted nearest neighbors methodology first it is explained how the relevant parameters defining the adopted model are obtained such parameters have to do with the window length of the time series and with the number of neighbors chosen for the prediction then results corresponding to the spanish electricity market during 2002 are presented and discussed finally the performance of the proposed method is compared with that of recently published techniques

an experimental test of warm glow giving abstract this paper reports the results of an experimental test of the warm glow hypothesis a participant is presented with the opportunity to contribute from her own endowment to a charity of choice the experiment is designed so that a pure altruist has no incentive to donate the amount the designated charity will receive is preset any contribution by the participant crowds out dollar for dollar giving by the proctor we find that participants on average donated 20 of their endowments and that approximately 57 of the participants made a donation

economic impact of electricity market price forecasting errors a demand side analysis several techniques have been proposed in the literature to forecast electricity market prices and improve forecast accuracy however no studies have been reported examining the economic impact of price forecast inaccuracies on forecast users therefore in this paper the application of electricity market price forecasts to short term operation scheduling of two typical and inherently different industrial loads is examined and the associated economic impact is analyzed using electricity market price forecasts as the expected next day electricity prices optimal operating schedules and the associated costs are determined for each load these costs are compared with those of a perfect price forecast scenario in which actual prices are used to determine the operating schedules numerical results and discussions are provided based on price forecasts with different error characteristics

classification of future electricity market prices forecasting short term electricity market prices has been the focus of several studies in recent years although various approaches have been examined achieving sufficiently low forecasting errors has not been always possible however certain applications such as demand side management do not require exact values for future prices but utilize specific price thresholds as the basis for making short term scheduling decisions in this paper classification of future electricity market prices with respect to pre specified price thresholds is introduced two alternative models based on support vector machines are proposed in a multi class multi step ahead price classification context numerical results are provided for classifying prices in ontario s and alberta s markets

partners in giving the crowding in effects of uk government grants abstract this study examines the determinants of voluntary donations to uk charities using empirical specifications that combine error component and simultaneous equations methods in a series of tests we identify the one way fixed effects endogenous representation for government grants as the best empirical model when this endogeneity is taken into account government grants cause significant crowding in for the full sample and the two largest cohorts price coefficients are negative and fund raising coefficients are positive religion charities are net revenue maximizers whereas other charity cohorts fund raise short of net revenue maximization

a novel approach to forecast electricity price for pjm using neural network and similar days method price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk and it also plays a key role in the economic optimization of the electric energy industry this paper explores a technique of artificial neural network ann model based on similar days sd method in order to forecast day ahead electricity price in the pjm market to demonstrate the superiority of the proposed model publicly available data acquired from the pjm interconnection were used for training and testing the ann the factors impacting the electricity price forecasting including time factors load factors and historical price factors are discussed comparison of forecasting performance of the proposed ann model with that of forecasts obtained from similar days method is presented daily and weekly mean absolute percentage error mape of reasonably small value and forecast mean square error fmse of less than 7 mwh were obtained for the pjm data which has correlation coefficient of determination of 0 6744 between load and electricity price simulation results show that the proposed ann model based on similar days method is capable of forecasting locational marginal price lmp in the pjm market efficiently and accurately

electricity price curve modeling and forecasting by manifold learning this paper proposes a novel nonparametric approach for the modeling and analysis of electricity price curves by applying the manifold learning methodology locally linear embedding lle the prediction method based on manifold learning and reconstruction is employed to make short term and medium term price forecasts our method not only performs accurately in forecasting one day ahead prices but also has a great advantage in predicting one week ahead and one month ahead prices over other methods the forecast accuracy is demonstrated by numerical results using historical price data taken from the eastern u s electric power markets

forecasting next day electricity prices by time series models in the framework of competitive electricity markets power producers and consumers need accurate price forecasting tools price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities respectively this paper provides two highly accurate yet efficient price forecasting tools based on time series analysis dynamic regression and transfer function models these techniques are explained and checked against each other results and discussions from real world case studies based on the electricity markets of mainland spain and california are presented

analysis of the probability distribution of lmp by central limit theorem the main goal of this paper is to provide an analytical study on the statistical behaviors of the electricity price at various conditions of the load and the network in order to achieve this goal a novel two step modeling is proposed for the electricity price at the first step it is proved that there is a linear relationship between the locational marginal prices lmp and the strategies of generating companies gencos in this linear relationship the lmp is decomposed to the weighted summation of the strategies of marginal units plus constant value at the second step the lmp is modeled by classic central limit theorem clt using this model an analytical relationship is derived between the price discovery mechanism and characteristics of its probability density function pdf as a result this paper shows analytically that increasing system load causes the price pdf deviates from the normal distribution on the other hand by decreasing system load the electricity price distribution tends toward the normal distribution the statistical studies on the real power markets data confirm these analytical results furthermore the simulation results of the ieee 300 bus test system illustrate the efficiency of the proposed model

electricity price short term forecasting using artificial neural networks this paper presents the system marginal price smp short term forecasting implementation using the artificial neural networks ann computing technique the described approach uses the three layered ann paradigm with backpropagation the retrospective smp real world data acquired from the deregulated victorian power system was used for training and testing the ann the results presented in this paper confirm considerable value of the ann based approach in forecasting the smp

a signaling explanation for charity this article examines a novel but plausible motive for charitable giving charitable donations which are observable can signal wealth or income the signaling equilibrium of charitable donations has attractive properties donations increase proportionally with population size governmental funding for public goods affects private charity only through an income effect crowding out need not be complete income redistribution is generically nonneutral increasing the spread between the poorest and richest person in a community tends to increase private donations our theory are based on the assumption that people are willing to make charitable donations even if they will not increase provision of the public good the data on charitable giving support the hypothesis that donors donate at least partly for signaling purposes rather than only to aid the recipient or to obtain satisfactions unrelated to status though charitable organizations usually report donations within categories thus not allowing perfect revelation for analytical tractability we shall consider an equilibrium with perfect revelation and a continuum of types as characterized in george j mailath 1987 and norman j ireland 1994

a state transformed by information texas regional economy in the 1990s tu w and sui d z a state transformed by information texas regional economy in the 1990s regional studies the structural change of the texas regional economy in the 1990s is analysed using input output and shift share analyses as the fastest growing economic sectors in all thirteen texas regions the information segment is also found to be more important as an input factor in the production processes the informatization of the texas economy is found to be accompanied by a widening development gap between the core and the periphery region in the state a better understanding of the dynamics of growth processes and spatial patterns of the information economy is essential to prepare for future regional development plans tu w et sui d z un etat transforme par l information l economie regionale de texas dans les annees 1990 regional studies on analyse la transformation structurelle de l economie regionale de texas dans les annees 1990 a partir des tableaux des echanges interindustriels et d une

day ahead price forecasting of electricity markets by mutual information technique and cascaded neuro evolutionary algorithm in a competitive electricity market price forecasts are important for market participants however electricity price is a complex signal due to its nonlinearity nonstationarity and time variant behavior in spite of much research in this area more accurate and robust price forecast methods are still required in this paper a combination of a feature selection technique and cascaded neuro evolutionary algorithm cnea is proposed for this purpose the feature selection method is an improved version of the mutual information mi technique the cnea is composed of cascaded forecasters where each forecaster consists of a neural network nn and an evolutionary algorithm ea an iterative search procedure is also incorporated in our solution strategy to fine tune the adjustable parameters of both the mi technique and cnea the price forecast accuracy of the proposed method is evaluated by means of real data from the pennsylvania new jersey maryland pjm and spanish electricity markets the method is also compared with some of the most recent price forecast techniques