bluuebunny/crossref_metadata_2025_split huggingface dataset:
Short‐term forecasting of industrial production by means of quick indicators
Authors: Teräsvirta Timo 
Published: July 2007

AbstractThis paper reports results on building transfer function models with linear combinations of quick indicators as inputs for very short‐term prediction of the monthly time series of the volume of industrial production in Finland. The number of input variables in the transfer function models is reduced in two alternative ways: by replacing the original indicators by their two first principal components and by omitting certain indicators. The prediction accuracy of the transfer function models is checked outside the sample and found superior to that of corresponding ARIMA models. Neither of the two ways of reducing the number of input variables leads to consistently more accurate forecasts than the other. It is also found that the prediction accuracy of the transfer function models compares rather favourably with the preliminary values of the volume of industrial production published by the Central Statistical Office during the periods of rapid growth.
The role of time series analysis in the evaluation of econometric models
Authors: Longbottom J. A. , Holly S. 
Published: July 2007

AbstractThis paper compares the properties of a structural model—the London Business School model of the U.K. economy—with a time series model. Information provided by this type of comparison is a useful diagnostic tool for detecting types of model misspecification. This is a more meaningful way of proceeding rather than attempting to establish the superiority of one type of model over another. In lieu of a better structural model, the effects of inappropriate dynamic specification can be reduced by combining the forecasts of both the structural and time series models. For many variables considered here these provide more accurate forecasts than each of the model types alone.
Forecasting demand in international markets: The case of correlated time series
Authors: Ofir Chezy , Raveh Adi 
Published: July 2007

AbstractIn this paper a data analysis tool for analyzing highly correlated time series data is suggested. The main objective is to unify multiple time series into a single series and then apply a univariate method for the purpose of prediction. This method is essentially efficient for analyzing multiple time series with sparse data. Several time series data of relative demand for black and white television receivers in various countries are analyzed and quite accurate predictions are obtained.
Forecasting commercial electricity sales
Authors: Watson Mark W. , Pastuszek Lydia M. , Cody Eric 
Published: July 2007

AbstractAn important component of the New England Electric System Companies' (the 'System') total electricity sales is attributable to commercial customers. Commercial growth has recently been strong; moreover the System's peak demand is highly sensitive to commercial load. In a typical month this class represents 33 per cent of total System sales. Accurate short‐run forecasts of total kWh sales are important for rate making, budgeting, fuel cause proceedings, and corporate planning. In this study we use a variety of econometric and time‐series techniques to produce short‐run forecasts of commercial sales for two geographical areas served by two separate retail companies;.
Forecasting vehicle holdings and usage with a disaggregate choice model
Authors: Berkowitz Michael K. , Gallini Nancy T. , Miller Eric J. , Wolfe Robert A. 
Published: July 2007

AbstractA case study in which a three‐stage choice model of Canadian household vehicle holdings and usage is used to generate short‐run forecasts of changes in household vehicle usage and gasoline consumption in response to a range of energy‐related policies. The objectives of this case study are to (1) demonstrate the application of disaggregate choice modelling methods to the generation of policy‐relevant forecasts of travel behaviour; (2) draw implications from this forecasting exercise concerning the likely impacts of various energy‐related policies; and (3) assess some of the strengths and weaknesses of the current state‐of‐the‐art of forecasting with disaggregate choice models, using the presented study as a case in point.
Comments on testing for forecast and specification optimality using linear composites
Authors: Phillips Robert F. 
Published: July 2007

AbstractThis paper extends the method of using linear composites of forecasts for testing the efficiency of one forecast compared to a finite collection of other forecasts. It also gives necessary and sufficient conditions for forecast optimality in the mean square error sense. Information sets are found to be unnecessary for forecast optimality. Finally, the paper shows that a consistent test of forecast optimality cannot in general be obtained using linear composites. A similar conclusion applies to tests of specification optimality based on linear composites.
Combining algorithms based on robust estimation techniques and co‐integrating restrictions
Authors: Hallman J. , Kamstra M. 
Published: July 2007

AbstractThis paper will motivate alternative combining schemes, provide a statistic for comparing alternative combinations out‐of‐sample and provide an example demonstrating these techniques. The evidence suggests the proposed procedures are likely to do no worse than other approaches and promise to do better under circumstances commonly encountered with economic data: integrated series and contaminated data.
The combining of forecasts using recursive techniques with non‐stationary weights
Authors: Sessions D. N. , Chatterjee S. 
Published: July 2007

AbstractThis paper evaluates six optimal and four ad hoc recursive combination methods on five actual data sets. The performance of all methods is compared to the mean and recursive least squares. A modification to one method is proposed and evaluated. The recursive methods were found to be very effective from start‐up on two of the data sets. Where the optimal methods worked well so did the ad hoc ones, suggesting that often combination methods allowing ‘local bias’ adjustment may be preferable to the mean forecast and comparable to the optimal methods.
Seasonal adjustment and kalman filtering: Extension to periodic variances
Authors: Eiurridge Peter , Wallis Kenneth F. 
Published: July 2007

AbstractThis paper reviews the relations between the methods of seasonal adjustment used by official statistical agencies and the ‘model‐based’ methods that postulate explicit stochastic models for the unobserved components of a time series and apply optimal signal extraction theory to obtain a seasonally adjusted series. The Kalman filter implementation of the model‐based methods is described and some recent results on its properties are reviewed. The model‐based methods employ homogeneous or time‐invariant models that assume in particular that the autocovariance structure does not vary with the season. Relaxing this leads to the class of models known as periodic models, and an example of a seasonally heterosceclastic unobserved‐components ARIMA (SHUCARIMA) model is presented. The calculation of the standard error of a seasonally adjusted series via the Kalman filter is extended to this periodic model and illustrated for a monthly rainfall series.
Predicting small region sectoral responses to changes in aggregate economic activity: A time series approach
Authors: Weller Barry R. 
Published: July 2007

AbstractThe purpose of this paper is to investigate the applicability of a contemporary time series forecasting technique, transfer function modeling, to the problem of forecasting sectoral employment levels in small regional economies. The specific sectoral employment levels to be forecast are manufacturing, durable manufacturing, non‐durable manufacturing and non‐manufacturing employment. Due to data constraints at the small region level, construction of traditional causal econometric models is often very difficult; thus time series approaches become particularly attractive. The results suggest that transfer function models using readily available national indicator series as drivers can provide more accurate forecasts of small region sectoral employment levels than univariate time series models.
Forecasting environmental change
Authors: Beck M. B. 
Published: July 2007

AbstractA context and introduction are provided for nine contributed articles on Forecasting Environmental Change. Since the first quantitative studies of the ‘predicament of mankind’ at the end of the 1960s, large‐scale forecasting models have attracted substantial criticism on the grounds of failing both to inform the policy process and to embrace the notion of surprise‐rich futures. Almost all attempts at developing models of environmental systems, at whatever scale, have been dogged by the sceptic's question: Is the model capable of predicting conditions substantially different from those observed in the past? The question, strictly speaking, is not answerable. The paper defines therefore three approaches to the development of modelsthe mechanical, metric, and linguistic paradigmsand constructs thence a reorientation of the customary problem of prediction in order to explore the concept of reachable, and radically different, futures. Such reorientation depends crucially on a juxtaposition of the linguistic and mechanical descriptions of a system's behaviour. The nine papers of this special issue are then summarized in the context provided by this reconsideration of how we formalize our thinking about the behaviour of systems, and of what might be meant by the systematic analysis of reachable futures.
The impact of forecasting methodology on the accuracy of national population forecasts: Evidence from the Netherlands and Czechoslovakia
Authors: Keilman Nico , Kučera Tomaš 
Published: July 2007

AbstractThis study considers the accuracy of national population forecasts of the Netherlands and the Czechoslovak Socialist Republic (CSSR) produced by the statistical agencies of these countries after World War II. Attempts are made to link patterns of ex‐post errors to changes in forecasting methodology. We look at the demographic components employed in each forecast, the procedure to extrapolate fertility and the level at which assumptions for each component are formulated. Errors in total population size, fertility, mortality and foreign migration, and age structure are considered. We discuss trends in errors and methodology since 1950 and compare the situations in the two countries. The findings suggest that methodology has only a very limited impact on the accuracy of national population forecasts.
Co‐integration, error correction and improved medium‐term regional VAR forecasting
Authors: Shoesmith Gary L 
Published: July 2007

AbstractThis study investigates possible improvements in medium‐term VAR forecasting of state retail sales and personal income when the two series are co‐integrated and represent an error‐correction system. For each of North Carolina and New York, three regional vector autoregression (VAR) models are specified; an unrestricted two‐equation model consisting of the two state variables, a five‐equation unrestricted model with three national variables added and a Bayesian (BVAR) version of the second model. For each state, the co‐integration and error‐correction relationship of the two state variables is verified and an error‐correction version of each model specified. Twelve successive ex ante five‐year forecasts are then generated for each of the state models. The results show that including an error‐correction mechanism when statistically significant improves medium‐term forecasting accuracy in every case.
Estimating the strength of expert judgement: The case of US mortality forecasts
Authors: Alho Juha M. 
Published: July 2007

AbstractThe use of expert judgement is an important part of demographic forecasting. However, because judgement enters into the forecasting process in an informal way, it has been very difficult to assess its role relative to the analysis of past data. The use of targets in demographic forecasts permits us to embed the subjective forecasting process into a simple time‐series regression model, in which expert judgement is incorporated via mixed estimation. The strength of expert judgement is denned, and estimated using the official forecasts of cause‐specific mortality in the United States. We show that the weight given to judgement varies in an improbable manner by age. Overall, the weight given to judgement appears too high. An alternative approach to combining expert judgement and past data is suggested.
A comparison of models for forecasting the discovery of hydrocarbon deposits
Authors: Power M. , Fuller J. D. 
Published: July 2007

AbstractThere are many geologic resource assessment techniques available that can be used to estimate the hydrocarbon potential of frontier basins. The techniques do not, however, produce discovery rate forecasts, which are critical to government and industry planners. Various methods of predicting the discovery rate in frontier basins are reviewed and appraised within the context of the drilling history of Canada's Scotian Shelf. The reviewed models include a mean historical discovery rate technique, the exponential decline model, a logistic curve technique and a class of models based on a sampling without replacement approach to prediction. Models are found to perform best over the longer forecast horizons, with those models based on the sampling without replacement technique tending to perform best.
Modelling peak electricity demand
Authors: Engle Robert F. , Mustafa Chowdhury , Rice John 
Published: July 2007

AbstractThis paper estimates a forecasting equation for the hourly peak electricity demand one day in the future. The models incorporate deterministic influences such as holidays, stochastic influences such as average loads by building bivariate models, and exogenous influences such as the weather which is given a careful non‐linear formulation. Out‐of‐sample comparisons are made using an additional year of data.
The uses and abuses of ‘consensus’ forecasts
Authors: McNees Stephen K. 
Published: July 2007

AbstractClemen's (1989) review of the forecast‐combining literature amply illustrates both the interest in and the importance of this subject. This article stresses the tautological properties of various consensus measures that assure their success relative to most individual forecasts. It confirms the finding of earlier studies that for each specific macroeconomic variable roughly one‐third of individual forecasters are more accurate than a consensus. However, each individual does relatively poorly for some variable while the consensus, in contrast, necessarily never fails relative to most individuals. These results, like most previous studies, describe consensus measures that are synthetic constructs derived from a pre‐existing set of individual forecasts. Strictly speaking, this contemporaneous consensus is not available to individual forecasters when their forecasts are made. A prior consensus measure, which is in their information sets, was relatively much less accurate than the contemporaneous measure. Nevertheless, a small subset of individual forecasters were generally inferior to the known, prior consensus forecast.
Testing weak rationality of forecasts with different time horizons
Authors: Kirchgässner G. 
Published: July 2007

AbstractStarting with conventional tests for weak rationality, we show how additional tests can be performed if predictions with different time horizons are commonly used in them. Next, we show that most of these tests can still be applied if the structure of the economic system changes but only some of them if the true system is unknown. Finally, these tests are applied to the semi‐annual one‐and two‐step predictions of the group of five leading economic research institutes in the Federal Republic of Germany. For the two‐step predictions we find more evidence against the rational expectations hypothesis than for the one‐step predictions.
Combining exchange rate forecasts: What is the optimal consensus measure?
Authors: Macdonald Ronald , Marsh Ian W. 
Published: July 2007

AbstractIn this paper a high‐quality disaggregate database is utilized to examine whether individual forecasters produce efficient exchange rate predictions and also if the properties of the forecasts change when they are combined. The paper links a number of themes in the exchange rate literature and examines various methods of forecast combination. It is demonstrated, inter alia, that some forecasters are better than others, but that most are not as good as a naive no‐change prediction. Combining forecasts adds to the accuracy of the predictions, but the gains mainly reflect the removal of systematic and unstable bias.
Seven components of judgmental forecasting skill: Implications for research and the improvement of forecasts
Authors: Stewart Thomas R. , Lusk Cynthia M. 
Published: July 2007

AbstractA decomposition of the Brier skill score shows that the performance of judgmental forecasts depends on seven components: environmental predictability, fidelity of the information system, match between environment and forecaster, reliability of information acquisition, reliability of information processing, conditional bias, and unconditional bias. These components provide a framework for research on the forecasting process. Selected literature addressing each component is reviewed, and implications for improving judgmental forecasting are discussed.
Combining experts' opinions using a normal‐wishart model
Authors: Wiper Michael P. , French Simon 
Published: July 2007

AbstractThis paper examines how a Bayesian decision maker might update her distributions for continuous variablesXi, i=1, 2, …, upon hearing experts' forecasts expressed as quantiles. To utilize the relationship between the decision maker and experts, and to avoid problems associated with different scales and ranges of the variables, we assume that the decision maker transforms the experts' quantiles in terms of her own prior distribution for eachXi.A model using such a transformation is presented and its properties are examined.
Quarterly forecasts of the italian business cycle by means of monthly economic indicators
Authors: Parigi G. , Schlitzer G. 
Published: July 2007

AbstractRecent years have witnessed a growing availability of high‐frequency indicators which can be used to forecast future economic activity. This paper shows how some of the widely known monthly economic indicators at present available in Italy can be used in a systematic and coordinated manner to forecast the main variables of the National Accounts. In order to reduce as much as possible the amount of judgment in the analysis of the business cycle, a model‐based approach is adopted. Thus, a pseudo macro‐econometric model of the Italian economy is built, which can be used to produce forecasts one semester ahead of the last National Accounts data release. The model can be used autonomously as well as in combination with the Bank of Italy's quarterly econometric model.
Structural, VAR and BVAR models of exchange rate determination: A comparison of their forecasting performance
Authors: Sarantis Nicholas , Stewart Chris 
Published: July 2007

AbstractThis paper compares the out‐of‐sample forecasting accuracy of a wide class of structural, BVAR and VAR models for major sterling exchange rates over different forecast horizons. As representative structural models we employ a portfolio balance model and a modified uncovered interest parity model, with the latter producing the more accurate forecasts. Proper attention to the long‐run properties and the short‐run dynamics of structural models can improve on the forecasting performance of the random walk model. The structural model shows substantial improvement in medium‐term forecasting accuracy, whereas the BVAR model is the more accurate in the short term. BVAR and VAR models in levels strongly out predict these models formulated in difference form at all forecast horizons.
Design of an optical system to extract the image of the pupil for gaze detection
Authors: Tomono Akira 
Published: July 2007

AbstractA system to detect eye direction via image processing is being studied for a better human‐machine interface. It is important in this method that feature points relating to an eye be extracted stably.This paper proposes the design of an optical system which efficiently extracts the image of a pupil as a feature point from its background. The image of a pupil can clearly be recorded by a camera by illuminating the eye with an infrared ray which is reflected from the fundus of the eye and exits through the pupil. However, the distribution of the brightness of the pupil image varies widely depending on the relative position of the ray sources.To analyze these conditions, an eye model consisting of a transparent double‐sphere is introduced. Using this model and a ray‐tracing method, the conditions of the illumination to provide an even image of the entire pupil have been obtained. The result shows that the ray sources should be arranged symmetrically within a specific radius of the optical axis of the camera lens. The calculation based on these conditions agrees with the experimental results, showing the effectiveness of this simulation.The conditions to make the image of a pupil dark have also been obtained by applying the same method. To emphasize the contrast of the image of a pupil against its background, a method using a single camera and two ray sources having different conditions is proposed; the camera produces a set of two images of a pupil (bright and dark), and then the difference of the images is simply digitized.
A summation algorithm with error correction for parallel computers
Authors: Ozawa Kazufumi , Miyazaki Masatoshi 
Published: July 2007

AbstractThis paper proposes an algorithm to accurately compute the sum of floating‐point numbers on parallel computers. This algorithm is an extension of the well‐known recursive doubling technique which computes the sum of n floating‐point number in log2n parallel steps. The time complexity of the present algorithm also is O(log n), and the space complexity is O(n). This algorithm enables a highly accurate result to be obtained with guarantee. The theoretical analysis and the numerical experiments on a parallel computer show that this algorithm is as accurate as Kahan's, which is the fastest and an accurate serial algorithm for the summation of the numbers, and also that the present algorithm is faster than Kahan's provided that two or more processors are available.
Construction of pseudorandom permutations
Authors: Ohnishi Yasuhiro , Maruoka Akira 
Published: July 2007

AbstractThe permutation from {0, 1}2n to {0, 1}2n represented by S(f) in DES (Data Encryption Standard) is used as the basic operation. Let f be a function from {0, 1}n to {0, 1}n and ⊕ denote bitwise exclusive or. Then S(f) is defined S(f)(L.R) = R.[L⊕f(R)] where L, R ϵ {0, 1}n, Moreover, let S(f2, f1, fo) denote the composition of S(f2, S(f1) and S(fo), and {S(f2, f1,fo)} denote a set of S(f2,f1,fo) obtained when fo,f1 and f2 are chosen randomly from a set of pseudorandom functions. Luby et al. have shown that {S(f2,f1,fo)} is pseudorandom.This paper investigates the case in which there are two different pseudorandom functions and shows that while {{S(f1, f1,, fo)} and {S(f1, fo, fo)} are pseudorandom, {S(fo, f1, fo)} is not.