bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using the life cycle to anticipate satisfaction at work
Authors: De Vries Manfred F. R. Kets , Miller Danny , Toulouse Jean‐Marie , Friesen Peter H. , Boisvert Maurice , Theriault Roland 
Published: July 2007

AbstractIt was hypothesized that there are important non‐linear life‐cycle influences upon job and organizational satisfaction. Five common life‐cycle stages were identified from the literature: the phases of ‘reality shock’, ‘socialization and growth’, ‘mid‐career crisis’, ‘acceptance’, and ‘pre‐retirement’. The first, third and last stages were expected to show declines in job and organizational satisfaction because of the personal and job‐related disappointments and crises that typically occur during these periods of life. The second and fourth stages were expected to show increases in satisfaction because of the pleasant life experiences that often occur then. All but the first stage supported these hypotheses when we controlled for the influence of sex, education, job experience, level in the hierarchy and occupational upward mobility.
An examination of factors contributing to delphi accuracy
Authors: Parenté Frederick J. , Anderson Janet K. , Myers Patrick , O'brien Thomas 
Published: July 2007

AbstractThree experiments examined the accuracy in the Delphi method. The first experiment assessed the accuracy of group predictions over 1‐, 2‐ and 3‐ month time spans. Results indicated that predictions derived from the group were more accurate than those of 95 per cent of the individual panelists, but did not exceed in accuracy the best panelists. Experiment 2 evaluated the gross contributions of polling and feedback to Delphi accuracy. The manipulations did not improve the group's ability to forecast the probabilities of the occurrence of events, but did decrease the error in predicting when the events would occur. Experiment 3 separated the effects of polling and feedback as determinants of accuracy. Neither manipulation improved the accuracy of the group's predictions of whether an event would occur. The effect of iterated polling was to reduce the group's error in predicting the time course for those scenarios that did occur.
Bayesian forecasts following a major level change in exponential smoothing
Authors: Jun Duk Bin , Oliver Robert M. 
Published: July 2007

AbstractExponential smoothing techniques enjoy a wide range of applications to problems in signal detection, inventory and production control, financial planning, and many other areas of business and engineering. One of the most useful models used to explain the theoretical structure of the process is a changing levels model, in which the underlying level of the stochastic process is assumed to undergo random changes in each time period. The observation is modelled as a noisy disturbance of this level. Occasionally a major intervention or level change occurs that is much larger than the typical period‐to‐period fluctuation in the random level. Using a Bayesian approach it is the purpose of this paper to show how the distribution of the major level change can be detected, estimated and then incorporated in forecasts. Updating equations are obtained for the posterior mean and variance of the major level change as well as the new level.
Bayesian consensus forecasts of macroeconomic variables
Authors: Agnew Carson E. 
Published: July 2007

AbstractEconomists, like other forecasters, share knowledge, data and theories in common. Consequently, their forecast errors are likely to be highly dependent. This paper reports on an empirical study of 16 macroeconomic forecasters. Composite forecasts are computed using a sequential weighting scheme that takes dependence into account; these are compared to a simple average and median forecasts. A within‐sample composite is also calculated. Both these methods perform significantly better than the average or median of the forecasts. This improvement in accuracy is apparently because the dependence between the forecasters' errors is so high that the optimal composite forecasts sometimes lie outside the range of the individual forecasts.
Forecasting with an econometric model: The ‘ragged edge’ problem
Authors: Wallis Kenneth F. 
Published: July 2007

AbstractIn practical econometric forecasting exercises, incomplete data on current and immediate past values of endogenous variables are available. This paper considers various approaches to this ‘ragged edge’ problem, including the common device of treating as ‘temporarily exogenous’ an endogenous variable whose value is known, by deleting it from the set of endogenous variables for whose forecast values the model is solved and suppressing the corresponding structural equation. It is seen that this forecast can be adjusted to coincide with the optimal forecast. The initial discussion concerns the textbook linear simultaneous equation model; extensions to non‐linear dynamic models are described.
The flat maximum effect and linear scoring models for prediction
Authors: Lovie A. D. , Lovie P. 
Published: July 2007

AbstractOur paper challenges the conventional wisdom that the flat maximum inflicts the ‘curse of insensitivity’ on the modelling of judgement and decision processes. In particular, we argue that this widely demonstrated failure on the part of conventional statistical methods to differentiate between competing models has a useful role to play in the development of accessible and economical applied systems, since it allows a low cost choice between systems which vary in their cognitive demands on the user and in their ease of development and implementation. To illustrate our thesis, we take two recent applications of linear scoring models used for credit scoring and for the prediction of sudden infant death.The paper discusses the nature and determinants of the flat maximum as well as its role in applied cognition. Other sections mention certain unanswered questions about the development of linear scoring models and briefly describe competing formulations for prediction.
Application of intervention analysis to a road fatality series in ontario
Authors: Abraham Bovas 
Published: July 2007

AbstractEffect of an intervention on a road fatality time series is studied using (i) the usual intervention analysis of Box and Tiao (1975), and (ii) CUSUM charts for the one‐step‐ahead forecast errors. It is shown that the seat belt and speed limit legislations of the Ontario Government had some impact in bringing down the road toll level.
Household projection methods
Authors: Corner Ian E. 
Published: July 2007

AbstractThe role of household projections as a basis for forecasts of households at national and sub‐national level is discussed and a number of criteria for such projections are outlined. The projection method used by the Department of the Environment is examined in the context of these criteria and it is concluded that it is both practical and robust. However, it is open to criticism, first because of its failure to make the best use of the available data and of theoretical knowledge, and secondly because of its ‘black box’ nature. An alternative two‐stage strategy is developed. The first stage involves constructing projections using a new curve‐fitting method which takes account of within cohort life‐cycle headship rate changes. The second is a method of analysing the resulting projections by modelling transition rates between different household states. Worked examples of both methods are presented.
Innovators and imitators in innovation diffusion modelling
Authors: Tanny S. M. , Derzko N. A. 
Published: July 2007

AbstractWe examine the theoretical underpinnings of the Bass innovation diffusion model and show that, contrary to the present understanding in the literature, it does not describe the behavioural theory of an innovator‐imitator dichotomy within the adopting population as was intended. Rather, this model describes the behaviour of a homogeneous population in which it is assumed that product adoption occurs as a result of exposure to a news source or from the binary interaction between adopters and non‐adopters. We provide an alternative framework in which the innovator‐imitator explanation of diffusion is explicitly incorporated. In the so‐called two‐compartment model the innovators and imitators constitute two distinct but interacting subpopulations. However, the added structural complexity cannot be supported by the available diffusion data, resulting in unsatisfactory model estimation results. This suggests that the innovator‐imitator population dichotomy assumed to underlie the present understanding of the innovation diffusion process may be inappropriate. Replacing it with the assumption of a homogeneous population could have potentially important implications for new product advertising strategies.
On detecting and estimating a major level or slope change in general exponential smoothing
Authors: Jun Duk B. 
Published: July 2007

AbstractExponential smoothing methods do not adapt well to a major level or slope change. In this paper, Bayesian statistical theory is applied to the dynamic linear model, altered by inclusion of dummy variables, and statistics are derived to detect such changes and to estimate both the change‐point and the size. The paper also gives test statistics for such problems related to exponential smoothing. The statistics are simple functions of exponentially weighted moving averages of the forecast errors, using the same discount factor used in the exponential smoothing. Gardner has derived an approximate test statistic to detect a mean change in the constant mean model. When the present results are applied to this model they give the exact statistic.
Feedback forecasting games: An overview
Authors: Bowden Roger J. 
Published: July 2007

AbstractThe range and rationales for forecasting schemes involving feedback are surveyed. Classic Delphi schemes involve an iterated exchange of information between a small group of experts in the pursuit of efficiencies arising from the ‘collective mind’. In other applications the event or state to be forecast may itself depend upon the forecast, and feedback forecasting schemes can be derived to allow for such reflexivity. Most situations of informational feedback are at least implicitly informational or predictive games, and a recognition of this character may help in understanding the possible or likely outcomes, including their social desirability. Various informational transfer devices such as real time surveys may speed up the process of feedback.
Unbiasedness, efficiency and the combination of economic forecasts
Authors: Holden K. , Peel D. A. 
Published: July 2007

AbstractThis paper considers the problem of determining whether forecasts are unbiased and examines the implications this has for combining different forecasts. The practical issues of how economic forecasts might be combined are discussed. There is an empirical illustration of the procedures in which the properties of UK forecasts from the London Business School, the National Institute, the Henley Centre for Forecasting, Phillips and Drew and the OECD are examined.
Linear combination of forecasts: A general Bayesian model
Authors: Anandalingam G. , Chen Lian 
Published: July 2007

AbstractWe provide a general Bayesian model for combining forecasts from experts (or forecasting models) who might be biased and correlated with each other. The combination procedure involves debiasing and then combining unbiased forecasts. We also provide a sequential method for learning about the forecasters' biases in the process of combining information from them.
Discretionary databases in forecasting
Authors: Connolly Terry , Porter Alan L. 
Published: July 2007

AbstractInformation for forecasting databases is often initially under the control of individuals who have no compelling reason to contribute, and who face various significant costs if they do. Such discretionary databases are subject to public goods problems, and are likely to be undersupplied, even when all participants agree that the overall benefits outweigh the overall costs. This paper explores the implications of this incentive structure for the existence, completeness and accuracy of forecasting databases. It also offers some hypotheses as to when the difficulties will be more and less severe, and outlines some directions for possible remedial strategies.
Disaggregation methods to expedite product line forecasting
Authors: Gross Charles W. , Sohl Jeffrey E. 
Published: July 2007

AbstractThis paper addresses the issue of forecasting individual items within a product line; where each line includes several independent but closely related products. The purpose of the research was to reduce the overall forecasting burden by developing and assessing schemes of disaggregating forecasts of a total product line to the related individual items. Measures were developed to determine appropriate disaggregated methodologies and to compare the forecast accuracy of individual product forecasts versus disaggregated totals. Several of the procedures used were based upon extensions of the combination of forecast research and applied to disaggregations of total forecasts of product lines. The objective was to identify situations when it was advantageous to produce disaggregated forecasts, and if advantageous, which method of disaggregation to utilize. This involved identification of the general conceptual characteristics within a set of product line data that might cause a disaggregation method to produce relatively accurate forecasts. These conceptual characteristics provided guidelines for forecasters on how to select a disaggregation method and under what conditions a particular method is applicable.
The role of judgement in macroeconomic forecasting
Authors: Turner D. S. 
Published: July 2007

AbstractThis paper examines the rationale for, and influence of, judgemental adjustments in macroeconomic forecasting, using two particular forecasts for the UK economy recently published by the National Institute of Economic and Social Research and the London Business School. It is found that in some cases such adjustments have a major effect on the forecasts and can also explain some of the differences in the two rival forecasts. However the number of adjustments for which this is true is not great. An implication of these findings is that, if these forecasts can be regarded as typical, then macroeconomic forecasters should be urged to give a reasonable account of the role of judgemental adjustments in their forecasts, particularly since the amount of information which would be required is not likely to be excessive.
Judgemental revision of sales forecasts: Effectiveness of forecast selection
Authors: Mathews Brian P. , Diamantopoulous A. 
Published: July 2007

AbstractWhen managers make revisions to sales forecasts initially generated by a rational quantitative model it is important that the particular forecasts selected for adjustment are those which would benefit most from the adjustment process (i.e. realize high errors). This study reports an empirical investigation on this issue, spanning six quarterly forecasting periods and incorporating forecasting data on over 850 products. The results show that the errors of the forecasts chosen for revision are, in general, higher than those which were not chosen. In addition, it is shown that managesrs tend to revise forecasts which are initially low, hence possibily introducing some degree of bias into the overall forecasts.
Prediction of seasonal rainfall in the sahel region using empirical and dynamical methods
Authors: Folland Chris , Owen John , Ward M. Neil , Colman Andrew 
Published: July 2007

AbstractLong‐range weather forecasting is a notoriously difficult area of environmental science. However, recent improved understanding of atmospheric dynamics and better observations indicate that useful progress, rooted in scientifically sound ideas, may be possible with long‐range forecasting in the tropics. We describe recent research into the mechanisms and prediction of rainfall in the sub‐Sahara during the main summer rainfall season, concentrating on the Sahel region. We use a complex physical model of the atmosphere (a ‘general circulation’ model) and two relatively simple statistical models to show that large‐scale variations in sea surface temperature (SST) can strongly influence seasonal Sahel rainfall. Persistence of patterns of SST anomalies (deviations from long‐term average) is sufficient to allow useful forecasting techniques to be based on fields of SST anomalies observed in the preceding spring. However, persistence of the SST anomalies may not always be sufficient to provide a skilful forecast.
Sources of error in forecasts and expectations: U.K. economic models, 1984–8
Authors: Wallis Kenneth F. , Whitley John D. 
Published: July 2007

AbstractThis paper presents a comparative analysis of the sources of error in forecasts for the UK economy published over a recent four‐year period by four independent groups. This analysis rests on the archiving at the ESRC Macroeconomic Modelling Bureau of the original forecasts together with all their accompanying assumptions and adjustments. A method of decomposing observed forecast errors so as to distinguish the contributions of forecaster and model is set out; the impact of future expectations treated in a ‘model‐consistent’ or ‘rational’ manner is specifically considered. The results show that the forecaster's adjustments make a substantial contribution to forecast performance, a good part of which comes from adjustments that bring the model on track at the start of the forecast period. The published ex‐ante forecasts are usually superior to pure model‐based ex‐post forecasts, whose performance indicates some misspecification of the underlying models.
Simultaneous transfer functions versus vector arma models
Authors: Grillenzoni Carlo 
Published: July 2007

AbstractThis work compares two classes of multiple time series models which have been developed in past decades and are usually believed to be equivalent: the vector ARMA model and the system of simultaneous transfer functions (STF). The first part analyzes the mathematical structure of the two schemes; their properties of stability, structural identification and realization. In the second, algorithms of order identification and parameter estimation are derived, following the approach of stochastic approximation. The proposed solutions are easily implementable on standard statistical software and in an extended empirical example their performance is checked. The superiority of the STF model will be well established.
Forecasting non‐normal time series
Authors: Swift A. L. , Janacek G. J. 
Published: July 2007

AbstractWe look at the problem of forecasting time series which are not normally distributed. An overall approach is suggested which works both on simulated data and on real data sets. The idea is intuitively attractive and has the considerable advantage that it can readily be understood by non‐specialists. Our approach is based on ARMA methodology and our models are estimated via a likelihood procedure which takes into account the non‐normality of the data. We examine in some detail the circumstances in which taking explicit account of the nonnormality improves the forecasting process in a significant way. Results from several simulated and real series are included.
Estimating daily seasonality in foreign exchange rate changes
Authors: Copeland Laurence S. , Wang Peijie 
Published: July 2007

AbstractCombining time‐ and frequency‐domain analysis demonstrates considerable improvement in modelling seasonal patterns in daily exchange rate changes. A high‐pass filter in the frequency domain is used, followed by the usual time‐domain analysis with GARCH models, to estimate day‐of‐the‐week effects in the spot returns on five exchanges against US dollars and the results are seen to compare favourably with those from a pure time‐domain GARCH approach.
An exploratory analysis of portfolio managers' probabilistic forecasts of stock prices
Authors: Muradoǧlu Gülnur , Önkal Dilek 
Published: July 2007

AbstractThis study reports the results of an experiment that examines (1) the effects of forecast horizon on the performance of probability forecasters, and (2) the alleged existence of an inverse expertise effect, i.e., an inverse relationship between expertise and probabilistic forecasting performance. Portfolio managers are used as forecasters with substantive expertise. Performance of this ‘expert’ group is compared to the performance of a ‘semi‐expert’ group composed of other banking professionals trained in portfolio management. It is found that while both groups attain their best discrimination performances in the four‐week forecast horizon, they show their worst calibration and skill performances in the 12‐week forecast horizon. Also, while experts perform better in all performance measures for the one‐week horizon, semi‐experts achieve better calibration for the four‐week horizon. It is concluded that these results may signal the existence of an inverse expertise effect that is contingent on the selected forecast horizon.
Correspondence on the selection of error measures for comparisons among forecasting methods
Authors: Armstrong J. Scott , Fildes Robert 
Published: July 2007

AbstractClements and Hendry (1993) proposed the Generalized Forecast Error Second Moment (GFESM) as an improvement to the Mean Square Error in comparing forecasting performance across data series. They based their conclusion on the fact that rankings based on GFESM remain unaltered if the series are linearly transformed. In this paper, we argue that this evaluation ignores other important criteria. Also, their conclusions were illustrated by a simulation study whose relationship to real data was not obvious. Thirdly, prior empirical studies show that the mean square error is an inappropriate measure to serve as a basis for comparison. This undermines the claims made for the GFESM.
Forecasting volatility in commodity markets
Authors: Kroner Kenneth F. , Kneafsey Kevin P. , Claessens Stijn 
Published: July 2007

AbstractThis paper uses recent advances in time‐series modeling to derive long‐horizon forecasts of commodity price volatility which incorporate investors' expectations of volatility. Our results are promising. We compare several different forecasts of commodity price volatility, which we divide into three categories: (1) forecasts using only expectations derived from options prices; (2) forecasts using only time‐series modeling; and (3) forecasts which combine market expectations and time‐series methods. The forecasts in (1) and (2) are used extensively in the literature, while those in (3) are new in this paper. On comparing these different forecasts, we find that our proposed forecasts from category (3) outperform both market expectations forecasts and time‐series forecasts. This result holds both in and out of sample for virtually all commodities considered.
A BVAR model for the connecticut economy
Authors: Dua Pami , Ray Subhash C. 
Published: July 2007

AbstractA Bayesian vector autoregressive (BVAR) model is developed for the Connecticut economy to forecast the unemployment rate, nonagricultural employment, real personal income, and housing permits authorized. The model includes both national and state variables. The Bayesian prior is selected on the basis of the accuracy of the out‐of‐sample forecasts. We find that a loose prior generally produces more accurate forecasts. The out‐of‐sample accuracy of the BVAR forecasts is also compared with that of forecasts from an unrestricted VAR model and of benchmark forecasts generated from univariate ARIMA models. The BVAR model generally produces the most accurate short‐ and long‐term out‐of‐sample forecasts for 1988 through 1992. It also correctly predicts the direction of change.