bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modelling the advertising‐sales relationship through use of multiple time series techniques
Authors: Heyse Joseph F. , Wei William W. S. 
Published: July 2007

AbstractWhen time series data are available for both advertising and sales, it may be worth while to model the two series jointly. Such an analysis may contribute to our understanding of the dynamic relationships among the series and may improve the accuracy of forecasts. Multiple time series techniques are applied to the well‐known Lydia Pinkham data to illustrate their use in modelling the advertising‐sales relationship. In analysing the Lydia Pinkham data the need for a joint model is established and a bivariate model is identified, estimated and checked. Its forecasting properties are discussed and compared to other time series approaches.
Tourism forecasting: A review of empirical research
Authors: Sheldon Pauline J. , Var Turgut 
Published: July 2007

AbstractMany publications on tourism forecasting have appeared during the past twenty years. The purpose of this article is to organize and summarize that scattered literature. General conclusions are also drawn from the studies to help those wishing to develop tourism forecasts of their own. The forecasting techniques discussed include time series models, econometric causal models, the gravity model and expert‐opinion techniques. The major conclusions are that time series models are the simplest and least costly (and therefore most appropriate for practitioners); the gravity model is best suited to handle international tourism flows (and will be most useful to governments and tourism agencies); and expert‐opinion methods are useful when data are unavailable. Further research is needed on the use of economic indicators in tourism forecasting, on the development of attractivity and emissiveness indexes for use in gravity and econometric models and on empirical comparisons among the different methods.
Diagnostic checks of non‐standard time series models
Authors: Smith J. Q. 
Published: July 2007

AbstractDiagnostic checks have become a standard tool for helping to assess the adequacy of a forecasting system since Box and Jenkins' (1970) ARIMA modelling technique became popular. However, most of the research has developed checks for normal or second‐order stationary models. This paper gives various diagnostic checks that can be performed simply on nonnormal, non‐standard models such as the class of multiprocess models (Harrison and Stevens, 1976), where residuals are definitely not normal. The performance to date of these models can then be objectively scrutinized on‐line. Examples, including a generalized cusum technique, are given to illustrate the effectiveness of the techniques on specific series.
Inferential processes in the construction of scenarios
Authors: Jungermann Helmut 
Published: July 2007

AbstractThe process of scenario construction is not yet well understood. Procedures appeal to the ‘disciplined intuition’ of experts. From a psychological perspective, however, generating scenarios represents a most difficult cognitive task. Two cognitive functions involved in this task are discussed: forward inferences and backward inferences. Whereas forward inferences explore the implications of given options and help to identify potential consequences, backward inferences explore the implications of given goals and help identify potential options. The first process leads to an exploratory scenario, the second to an anticipatory scenario. It is argued that the two approaches, applied to the same problem, result in different scenarios, i.e. scenarios that differ in their elements, their structures, their ranges and their ‘holes’. A bi‐directional construction method is suggested that balances the drawbacks implied in using one cognitive strategy only (e.g. forward inference) by using complementarily the other strategy (e.g. backward inference). In contrast to other methods proposed in the literature, this method is theoretically derived and can be tested empirically.
Forecasting temporally aggregated vector ARMA processes
Authors: Lütkepohl Helmut 
Published: July 2007

AbstractIf interest centres on forecasting a temporally aggregated multiple time series and the generation process of the disaggregate series is aknownvector ARMA (autoregressive moving average) process then forecasting the disaggregate series and temporally aggregating the forecasts is at least as efficient, under a mean squared error measure, as forecasting the aggregated series directly. Necessary and sufficient conditions for equality of the two forecasts are given. In practice the data generation process is usually unknown and has to be determined from the available data. Using asymptotic theory it is shown that also in this case aggregated forecasts from the disaggregate process will usually be superior to forecasts obtained from the aggregated process.
Subjective confidence in forecasts: A response to fischhoff and MacGregor
Authors: Wright George , Ayton Peter 
Published: July 2007

AbstractHere we evaluate the generalizability of calibration studies which have used general knowledge questions, and argue that on conceptual, methodological and empirical grounds the results have limited applicability to judgemental forecasting. We also review evidence which suggests that judgemental forecast probabilities are influenced by variables such as the desirability, imminence, time period and perceived controllability of the event to be forecast. As these variables do not apply to judgement in the domain of general knowledge, a need for research recognizing and exploring the psychological processes underlying uncertainty about the future is apparent.
A comparison of forecasts from least absolute value and least squares regression
Authors: Dielman Terry E. 
Published: July 2007

AbstractA Monte Carlo simulation is used to compare forecasts from least absolute value and least squares estimated regression equations. When outliers are present, the least absolute value forecasts are shown to be superior to least squares forecasts. The results emphasize the importance of exercising caution when using forecasts from least squares estimated regressions. Use of least absolute value regression (or some other robust regression method) instead of, or as an adjunct to, least squares is recommended. The comparison of forecasts from the two methods provides one way of assessing whether the least squares forecasts have been adversely affected by outliers. If outliers are present, the least absolute value regression forecasts can be used.
Linear constraints and the efficiency of combined forecasts
Authors: Trenkler G. , Liski E. P. 
Published: July 2007

AbstractThis note extends some recent results, achieved by Clemen, on constraining the weights of a combined forecast. There is a great potential for improving the ordinary least squares forecast by imposing linear restrictions, and it will be shown how this potential can be exhausted by using anF‐test. The corresponding decision procedure leads to a pre‐test forecast with good statistical properties.
On tests for non‐linearity in time series analysis
Authors: Chan W. S. , Tong H. 
Published: July 2007

AbstractWe have developed a new test for non‐linearity in time series data in discrete time. A comparative study has been conducted on Subba Rao, Gabr and Hinich's test, Keenan's test, Petruccelli and Davies test, and the new test. Both simulated and real data are used in the study. The implication for forecasting is briefly discussed.
Structural change and the combination of forecasts
Authors: Diebold Francis X. , Pauly Peter 
Published: July 2007

AbstractForecasters are generally concerned about the properties of model‐based predictions in the presence of structural change. In this paper, it is argued that forecast errors can under those conditions be greatly reduced through systematic combination of forecasts. We propose various extensions of the standard regression‐based theory of forecast combination. Rolling weighted least squares and time‐varying parameter techniques are shown to be useful generalizations of the basic framework. Numerical examples, based on various types of structural change in the constituent forecasts, indicate that the potential reduction in forecast error variance through these methods is very significant. The adaptive nature of these updating procedures greatly enhances the effect of risk‐spreading embodied in standard combination techniques.
Forecasts of power‐transformed series
Authors: Pankratz Alan , Dudley Underwood 
Published: July 2007

AbstractConsider a time series transformed by an instantaneous power function of the Box‐Cox type. For a wide range of fractional powers, this paper gives the relative bias in original metric forecasts due to use of the simple inverse retransformation when minimum mean squared error (conditional mean) forecasts are optimal. This bias varies widely according to the characteristics of the data. A fast algorithm is given to find this bias, or to find minimum mean squared error forecasts in the original metric. The results depend on the assumption that the forecast errors in the transformed metric are Gaussian. An example using real data is given.
Outliers, level shifts, and variance changes in time series
Authors: Tsay Ruey S. 
Published: July 2007

AbstractOutliers, level shifts, and variance changes are commonplace in applied time series analysis. However, their existence is often ignored and their impact is overlooked, for the lack of simple and useful methods to detect and handle those extraordinary events. The problem of detecting outliers, level shifts, and variance changes in a univariate time series is considered. The methods employed are extremely simple yet useful. Only the least squares techniques and residual variance ratios are used. The effectiveness of these simple methods is demonstrated by analysing three real data sets.
A new method of scenario analysis for strategic planning
Authors: Brauers Jutta , Weber Martin 
Published: July 2007

AbstractWe first present scenario analysis as a qualitative forecasting technique useful for strategic planning. Then we develop an overview of the two classes of methods for scenario analysis described in the literature. Based on both classes, a new method is developed which especially fits the needs of strategic planning. The method can be divided into three stages: 1. Determination of compatible scenarios, 2. Determination of scenario probabilities, and 3. Determination of main scenarios. An example is given to illustrate the method.
Adaptive prediction for stochastic processes with markov jump parameters
Authors: Xiao‐Jiang Qi 
Published: July 2007

AbstractThis paper is concerned with the adaptive prediction for stochastic processes with abruptly changing parameters modelled as a finite‐state Markov chain. The Markov transition matrix is assumed to be known. For the coloured noise disturbance case, it is shown that the optimal prediction algorithm requires a bank of elemental predictors running in parallel with its number growing exponentially with time. If the noise disturbance is white, it is found that the number of the elemental predictors required increases exponentially with the prediction ahead step instead of time. A suboptimal predictor is proposed with substantial reduced storage and computational requirements. Simulation examples show the good performance of the proposed algorithms.
A dynamic linear model approach for disaggregating time series data
Authors: Al‐Osh M. 
Published: July 2007

AbstractAn approach is proposed for obtaining estimates of the basic (disaggregated) series, xi, when only an aggregate series, yt, of k period non‐overlapping sums of xi's is available. The approach is based on casting the problem in a dynamic linear model form. Then estimates of xican be obtained by application of the Kalman filtering techniques. Anad hocprocedure is introduced for deriving a model form for the unobserved basic series from the observed model of the aggregates. An application of this approach to a set of real data is given.
Invited review combining forecasts—twenty years later
Authors: Granger C. W. J. 
Published: July 2007

AbstractThe combination of forecasts is a simple, pragmatic and sensible way to possibly produce better forecasts. Simple extensions of the original idea involve the use of various available ‘forecasts’ even if some are rather inefficient. Some unsolved questions relate to combining forecasts with horizons longer than one period. More complicated extensions are associated with ‘encompassing’ and the combination of confidence intervals or quantiles. The relevance of information sets is emphasized in both the underlying theory and the interpretation of combinations.
N‐step combinations of forecasts
Authors: Gunter Sevket I. , Aksu Celal 
Published: July 2007

AbstractWhile there is general agreement that a linear combination of forecasts can outperform the individual forecasts, there is controversy about the appropriateness of the combination method to be used in a given situation. Hence, in any given application it may be more beneficial to combine different sets of combined forecasts rather than picking one of them. This paper introduces the concept of N‐step combinations of forecasts which involves combining the combined forecasts obtained from different combination procedures used at the preceding step. Using quarterly GNP data, evidence supporting the increase in the accuracy of the one‐period‐ahead ex‐ante forecasts as the combination step increases is provided. The MSE, MAE, MAPE and their corresponding standard deviations are used to evaluate the accuracy of the forecasts obtained.
Composite model building for foreign exchange rates
Authors: Guerard John B. 
Published: July 2007

AbstractIt has been shown in recent economic and statistical studies that composite forecasts may produce more accurate forecasts than individual ones. The purpose of this study is to develop composite forecasting models that may produce forecasts superior to the individual forecast implicit in forward exchange rates. In an efficient market one would expect to find little improvement with the composite models relative to the forward exchange rate.
A comparison of tests for setar‐type non‐linearity in time series
Authors: Petruccelli Joseph D. 
Published: July 2007

AbstractTests for SETAR‐type non‐linearity in time series have recently been proposed by Petruccelli and Davies (1986), W. S. Chan and Tong (1986), Tsay (1987), Luukkonenet al.(1988), Petruccelli (1987) and Moeanaddin and Tong (1988). In this paper we consider the relative performance of thes: tests.
Decomposition; a strategy for judgemental forecasting
Authors: Edmundson R. H. 
Published: July 2007

AbstractThis paper reports the results of studies concerning the accuracy and efficiency of time‐series extrapolation decisions made with the assistance of an interactive graphical tool called GRAFFECT. The tool facilitates the decomposition of the extrapolation task by permitting the serial decomposition of the cue data as the task proceeds. GRAFFECT uses an interactive graphical interface controlled substantially with the use of a mouse. The extrapolation task is divided into the following: (1) trend modelling and extrapolation, (2) seasonal pattern modelling, and (3) extrapolation from the noise residual series. As each component is modelled its effect is stored and the information is washed out of the cue series. The ultimate forecast is produced by automatic recomposition of the judgementally determined components. The results show a significant improvement in forecast accuracy over unaided judgment, resulting in a subjective extrapolation that betters deseasonalized single exponential smoothing.
Some comparisons of the relative power of simple tests for structural change in regression models
Authors: Bleaney Michael 
Published: July 2007

AbstractThe power of Chow, linear, predictive failure and cusum of squares tests to detect structural change is compared in a two‐variable random walk model and a once‐for‐all parameter shift model. In each case the linear test has greatest power, followed by the Chow test. It is suggested that the linear test be used as the basic general test for structural change in time series data, and tests of forecasting performance be confined to the last few observations. Analysis of recursive residuals and recursive parameter estimates should be regarded as forms of exploratory data analysis and tools for understanding discrepancies with previous results rather than a basis for formal tests of structural change.
Exponential smoothing: Estimation by maximum likelihood
Authors: Broze Laurence , Mélard Guy 
Published: July 2007

AbstractIn this paper several forecasting methods based on exponential smoothing with an underlying seasonal autoregressive‐moving average (SARIMA) model are considered. The relations between the smoothing constants and the coefficients of the autoregressive and moving average polynomials are used. On that basis, a maximum likelihood procedure for parameter estimation is described. The approach rules out the need for initial smoothed values. Prediction intervals are also obtained as a by‐product of the approach and a fast algorithm for implementing the method is outlined.
Models for the assessment of the value of forecast information
Authors: Lawrence David B. 
Published: July 2007

AbstractThis paper reviews, extends and applies alternative normative decision models for the assessment of the value of forecast information, concentrating primarily on the factors influencing the tractability of assessment and interpretation in specific decision problems. As an empirical illustration of t lhe models, the paper presents a valuation analysis of the published judgmental price forecasts of a veteran analyst of the hog market to a perfectly competitive farm enterprise.
When and how to use scenario planning: A heuristic approach with illustration
Authors: Schoemaker Paul J. H. 
Published: July 2007

AbstractThis paper offers a step‐by‐step analysis of a heuristic approach to scenario planning, taking a managerial perspective. The scenario method is contrasted in general with more traditional planning techniques, which tend to perform less well when faced with high uncertainty and complexity. An actual case involving a manufacturing company is used to illustrate the main steps of the proposed heuristic. Its essence is to identify relevant trends and uncertainties, and blend them into scenarios that are internally consistent. In addition, the scenarios should bound the range of plausible uncertainties and challenge managerial thinking. Links to decision making are examined next, including administrative policies as well as integrative techniques. At the strategic level, a key‐success‐factor matrix is proposed for integrating scenarios, competitor analysis and strategic vision. At the operational level, Monte Carlo simulation is suggested and illustrated as one useful technique for combining scenario thinking with formal project evaluation (after appropriate translations). The paper concludes with a general discussion of scenario planning, to place it in a broader perspective.
Scoring the composite leading indicators: A Bayesian turning points approach
Authors: Lesage James P. 
Published: July 2007

AbstractThis paper explores a Bayesian decision‐theoretic approach for analysis and development of composite leading indicators. The methods used here are derived from work by Zellneret al.(1988) and Zellner and Hong (1988) aimed at forecasts time series turning points, and the multi‐process mixture models first described by Harrison and Stevens (1976) and more recently in West and Harrison (1989). Here, these methods are used to develop composite leading indicators formed by using the posterior probabilities derived from predictive relations between the individual indicator variables and the state of the economy measure as weights. This study, like those of Wecker (1979), Kling (1987) and Diebold and Rudebusch (1989), uses the time series observations on the measure of economic activity which we wish to predict along with an explicit definition of a turning point, either a downturn or upturn. Unlike those studies, we then establish a predictive relation between the individual component indicator series and the variable measuring economic activity which allows a Bayesian computation of probabilities associated with the turning point events. This parallels the developments in Zellneret al.(1988), where the focus was on forecasting turning points in economic times series. These probabilities are conditioned on the past data and the predictive probability density function (pdf) for future observations. A composite indicator is devised using a Class I, multi‐process mixture model suggested in West and Harrision (1989). The composite indicator arising from this approach is an average of the individual component series, where the averaging is done over the posterior probabilities of the individual component series predictive relations. An example of the procedure is provided using the national composite leading indicator (CLI).
Exponential smoothing: Behavior of theex‐postsum of squares near 0 and 1
Authors: Farnum Nicholas R. 
Published: July 2007

AbstractWhen using simple exponential smoothing on a given time series the nature of the relationship between the optimal smoothing constant and the autocorrelation structure of the series remains an unresolved question. Although numerical search routines can easily be used to find optimal values of the smoothing constant, they offer little insight into the nature of the relationship between the estimated smoothing constant and the structure of the underlying time series. We suggest that renewed investigations of theex‐postsum of squares function may prove helpful in this pursuit. Results are presented that illustrate how the optimal smoothing constant depends upon the value used to initialize the smoothing and upon the sample autocorrelation coefficients of the observed series. These results are based on a new formula for the derivative of theex‐postsum of squares function. In particular, the derivative is examined near 0 and 1, where great simplifications occur in its form, thereby facilitating investigations near these points. A necessary and sufficient condition is stated for when theex‐postsum of squares must have a positive derivative at 0 and the autocorrelation coefficients of the differenced series are shown to affect the sign of the derivative near 1. Based on these results, a general algorithm is presented as an alternative to grid search routines for minimizing theex‐postsum of squares.
Time‐series forecasting of the German unemployment rate
Authors: Funke Michael 
Published: July 2007

AbstractThe purpose of the paper is to investigate the accuracy of forecasts derived from univariate and multivariate time‐series models. An iterative method to adjust for impact assessment in univariate ARIMA models is discussed and illustrated for the German unemployment rate. Finally, we also examine the pros and cons of the impact assessment model in comparison with VAR models.
Nonlinear predictor models
Authors: Ameen Jamal R. M. 
Published: July 2007

AbstractA complete dynamic model is introduced within the Bayesian framework. This includes the dynamic linear model and the normal discount Bayesian model as special cases and extends to some well‐known models with nonlinear predictors. A number of practically important models are formulated and simple recurrence formulas, similar to those of Kalman, are used in the sequential estimation of the parameters. Finally, a number of practical examples and applications are given.