bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hierarchical bayes forecasts of multinomial dirichlet data applied to coupon redemptions
Authors: Lenk Peter J. 
Published: July 2007

AbstractThis paper considers forecasting count data from a multinomial Dirichlet distribution. The forecasting procedure implements hierarchical Bayes methods in order to develop a prior distribution for a new series of data. The methodology is applied to the redemption of cents‐off promotional coupons. In a forecasting experiment, early forecasts of new series are similar to those from pooling all redemptions from previous coupon promotions. However, the hierarchical Bayes model provides realistic estimates of forecasting errors, while those for the pooled forecasts are consistently optimistic. As the current series evolves, the hierarchical Bayes forecasts adapt more rapidly and are more accurate than pooled forecasts.
To combine or not to combine? issues of combining forecasts
Authors: Palm Franz C. , Zellner Arnold 
Published: July 2007

AbstractThis paper addresses issues such as: Does it always pay to combine individual forecasts of a variable? Should one combine an unbiased forecast with one that is heavily biased? Should one use optimal weights as suggested by Bates and Granger over twenty years ago? A simple model which accounts for the main features of individual forecasts is put forward. Bayesian analysis of the model using noninformative and informative prior probability densities is provided which extends and generalizes results obtained by Winkler (1981) and compared with non‐Bayesian methods of combining forecasts relying explicitly on a statistical model for the individual forecasts. It is shown that in some instances it is sensible to use a simple average of individual forecasts instead of using Bates and Granger type weights. Finally, model uncertainty is considered and the issue of combining different models for individual forecasts is addressed.
Evaluating the role judgment plays in forecast accuracy
Authors: Donihue Michael R. 
Published: July 2007

AbstractThe majority of model‐based forecasting efforts today rely on relatively simple techniques of estimation and the subjective adjustment of the model's results to produce forecasts. Published forecasts reflect to a great extent the judgment of the forecaster rather than what the model by itself has to say about the future. This paper examines the role judgment plays in the process of producing a macroeconometric forecast. The debate over the use of adjustment constants to alter the statistical results of a model is outlined and an empirical analysis of forecasts generated by the Michigan Quarterly Econometric Model of the US economy is presented using a unique data set which isolates the role of judgment in the forecasting process.
The effectiveness of imprecise probability forecasts
Authors: George Benson P. , Whitcomb Kathleen M. 
Published: July 2007

AbstractIn this paper we investigate the feasibility of algorithmically deriving precise probability forecasts from imprecise forecasts. We provide an empirical evaluation of precise probabilities that have been derived from two types of imprecise probability forecasts: probability intervals and probability intervals with second‐order probability distributions. The minimum cross‐entropy (MCE) principle is applied to the former to derive precise (i.e. additive) probabilities; expectation (EX) is used to derive precise probabilities in the latter case. Probability intervals that were constructed without second‐order probabilities tended to be narrower than and contained in those that were amplified by second‐order probabilities. Evidence that this narrowness is due to motivational bias is presented. Analysis of forecasters' mean Probability Scores for the derived precise probabilities indicates that it is possible to derive precise forecasts whose external correspondence is as good as directly assessed precise probability forecasts. The forecasts of the EX method, however, are more like the directly assessed precise forecasts than those of the MCE method.
Fixed versus time‐varying transfer functions for modelling business cycles
Authors: Edlund Per‐Olov , T. Søgaards Henning 
Published: July 2007

AbstractTransfer function models can be used to model the relationship between leading indicators and the business cycle. The traditional transfer function approach assumes that the pure delay (lead) as well as the other parameters of the model are constant. Results from studies on Swedish business cycle data indicate that the relationship between the leading indicators and the business cycle may be time varying. In this paper the traditional approach is compared to a recursive estimation procedure that allows the model to change over time. The results show that the recursive procedure is useful and that the estimates of the model parameters indeed vary over time.
Insights or forecasts? An evaluation of a computable general equilibrium model of spain
Authors: Polo Clemente , Sancho Ferran 
Published: July 2007

AbstractComputable general equilibrium (CGE) models are widely used as an advanced tool to evaluate alternative economic strategies and policy measures. These models are well rooted in solid economic theory, yet a crucial question is hardly asked: how well do these models perform? We address this question by comparing the economic performance of the Spanish economy in 1988 with the simulation results drawn from a CGE model calibrated with a 1987 Social Accounting Matrix. The values of endogenous variables used in the comparison are the equilibrium values provided by the model after updating the values of exogenous variables such as labour and capital endowments, real exports and effective nominal exchange rates with the European Community and the rest of the world, real government expenditures, and various tax rates, government subsidies, and transfers. The comparison shows that the model captures adequately the major developments that occurred in the Spanish economy in 1988. This result increases our confidence in the quantitative estimates derived from the model in the usual simulation exercises.
On estimation and prediction procedures for ar(1) models with power transformation
Authors: Lee J. C. , Tsao S. L. 
Published: July 2007

AbstractThe power transformation of Box and Cox (1964) has been shown to be quite useful in short‐term forecasting for the linear regression model with AR(1) dependence structure (see, for example, Lee and Lu, 1987, 1989). It is crucial to have good estimates of the power transformation and serial. correlation parameters, because they form the basis for estimating other parameters and predicting future observations. The prediction of future observations is the main focus of this paper. We propose to estimate these two parameters by minimizing the mean squared prediction errors. These estimates and the corresponding predictions compare favourably, via revs and simulated data, with those obtained by the maximum likelihood method. Similar results are also demonstrated in the repeated measurements setting.
Diagnostic tracking and model specification in combined forecasts of U.K. inflation
Authors: Menezes Lilian De , Bunn Derek W. 
Published: July 2007

AbstractCombinations of several forecasts are now quite commonly used as inputs into business planning models. For example, capital budgeting generally involves a synthesis of several sources of economic forecasts. In such cases, where uncertainty and risk are also being explicitly considered, the statistical specification of the combined forecasts becomes particularly important. An investigation of the monthly forecasts of annual inflation from nine leading U.K. economic models was undertaken to examine the circumstances under which well‐specified and efficient combined forecasts could be produced. This has helped to refine the general guidelines for the practical use of combinations in planning models.
Bootstrapping forecast intervals: An application to AR(p) models
Authors: McCullough B. D. 
Published: July 2007

AbstractForecast intervals typically depend upon an assumption of normal forecast errors due to lack of information concerning the distribution of the forecast. This article applies the bootstrap to the problem of estimating forecast intervals for an AR(p) model. Box‐Jenkins intervals are compared to intervals produced from a naive bootstrap and a bias‐correction bootstrap. Substantial differences between the three methods are found. Bootstrapping an AR(p) model requires use of the backward residuals which typically are not i.i.d. and hence inappropriate for bootstrap resampling. A recently developed method of obtaining i.i.d. backward residuals is employed and was found to affect the bootstrap prediction intervals.
Time‐variable parameter and trend estimation in non‐stationary economic time series
Authors: Young Peter C. 
Published: July 2007

AbstractThe paper describes a general approach to the modelling of nonlinear and nonstationary economic systems from time‐series data. This method exploits recursive state space filtering and fixed interval smoothing algorithms to decompose the time‐series into long term trend and short term ‘small perturbational’ components, each of which are then modelled by linear stochastic models which may be characterised by time variable parameters. The approach is illustrated by an example which explores the relationship between the variations in quarterly GNP and Unemployment in the USA over the period 1948 to 1988 and questions previous claims about the changes in the slope of the long term trend in loge(GNP) over this same period. The paper also points out that the recursive approach to estimation facilitates the use of these methods in the development of adaptive forecasting and control systems.
Predicting cyclical turning points with leading index in a markov switching model
Authors: Lahiri K. , Wang J. G. 
Published: July 2007

AbstractWe have evaluated the Commerce Department's Composite Index of Leading Indicators as a predictor of business cycle turning points using the two‐state Markov switching model as the filter. Contrary to some recent studies, we found that the predictive performance of CLI is quite good and, with an exception of the 1973:11 peak, it made very little difference to the prediction of turning points whether real‐time data are used instead of the revised series. We found, however, that imposing any degree of autoregression in the errors on the simple regime‐shift model caused the filter to signal turning points inappropriately. Also, we found no evidence of duration dependence in post‐war U.S. business cycles.
Time‐series forecasting using fractional differencing
Authors: Sutcliffe Andrew 
Published: July 2007

AbstractThe main failure of ARIMA modelling as used in practice are the limiting constraints imposed by differencing to achieve stationarity. The use of fractional differencing opens up a much wider and realistic behaviour for the trend and seasonal components than traditional integer differencing. This paper shows several advantages of using fractional differencing for forecasting monthly data. These advantages are illustrated using a sample of previously modelled time‐series data selected from the literature.
Modeling and forecasting time series with a general non‐normal distribution
Authors: Swift A. Louise 
Published: July 2007

AbstractWe propose a model for time series with a general marginal distribution given by the Johnson family of distributions. We investigate for which Johnson distributions forecasting using the model is likely to be most effective compared to using a linear model. Monte Carlo simulation is used to assess the reliability of methods for determining which of the three Johnson forms is most appropriate for a given series. Finally, we give model fitting and forecasting results using the modeling procedure on a selection of simulated and real time series.
Testing cumulative prediction errors in event study methodology
Authors: Coutts J. Andrew , Mills Terence C. , Roberts Jennifer 
Published: July 2007

AbstractThis paper reconsiders event study methodology, a very popular technique in the applied finance literature, within the context of testing cumulative prediction errors. It extends the conventional test statistics in two directions. First, it accounts fully for the increased variance of prediction errors outside of the estimation period and for the cumulation of these errors across different event windows. Second, it also takes account of the fact that market model residuals are typically serially correlated, heteroscedastic and non‐normal. The statistics are compared with the conventional approach by reassessing a previous application of the methodology to the impact of management buyouts.
BVAR as a category management tool: An illustration and comparison with alternative techniques
Authors: Curry David J. , Divakar Suresh , Mathur Sharat K. , Whiteman Charles H. 
Published: July 2007

AbstractCategory management—a relatively new function in marketing—involves large‐scale, real‐time forecasting of multiple data series in complex environments. In this paper, we illustrate how Bayesian Vector Auto regression (BVAR) fulfils the category manager's decision‐support requirements by providing accurate forecasts of a category's state variables (prices, volumes and advertising levels), incorporating management interventions (merchandising events such as end‐aisle displays), and revealing competitive dynamics through impulse response analyses. Using 124 weeks of point‐of‐sale scanner data comprising 31 variables for four brands, we compare the out‐of‐sample forecasts from BVAR to forecasts from exponential smoothing, univariate and multivariate Box‐Jenkins transfer function analyses, and multivariate ARMA models. TheilU'sindicate that BVAR forecasts are superior to those from alternate approaches. In large‐scale forecasting applications, BVAR's ease of identification and parsimonious use of degrees of freedom are particularly valuable.
An integrated bayesian vector auto regression and error correction model for forecasting electricity consumption and prices
Authors: Joutz Frederick L , Maddala G. S. , Trost Robert P. 
Published: July 2007

AbstractThe analysis and forecasting of electricity consumption and prices has received considerable attention over the past forty years. In the 1950s and 1960s most of these forecasts and analyses were generated by simultaneous equation econometric models. Beginning in the 1970s, there was a shift in the modeling of economic variables from the structural equations approach with strong identifying restrictions towards a joint time‐series model with very few restrictions. One such model is the vector auto regression (VAR) model. It was soon discovered that the unrestricted VAR models do not forecast well. The Bayesian vector auto regression (BVAR) approach as well the error correction model (ECM) and models based on the theory of co integration have been offered as alternatives to the simple VAR model. This paper argues that the BVAF., ECM, and co integration models are simply VAR models with various restrictions placed on the coefficients. Based on this notion of a restricted VAR model, a four‐step procedure for specifying VAR forecasting models is presented and then applied to monthly data on US electricity consumption and prices.
1H and 13C NMR signal assignments of Paecilin A and B, two new chromone derivatives from mangrove endophytic fungus Paecilomyces sp. (tree 1–7)
Authors: Guo Zhiyong , She Zhigang , Shao Changlun , Wen Lu , Liu Fan , Zheng Zhonghui , Lin Yongcheng 
Published: July 2007

AbstractTwo new natural products, named paecilin A (1) and B (2), together with two known compounds secalonic acid D (3) and (11)‐cytochalasa‐6(12),13‐diene‐1,21‐dione‐16,18‐dimethyl‐7‐hydroxy‐l0‐phenyl‐(7S*,13E,16S*,18S*) (4), were isolated from the mangrove endophytic fungus, Paecilomyces sp. (tree 1–7) from the South China Sea. 1D and 2D NMR experiments including COSY, HMQC, and HMBC were used for the determination of their structures. In our cytotoxicity assays, secalonic D (3) showed cytotoxicity toward KB cells with IC50 < 1 µg ml−1 and inhibiting human topoisomerase I with IC50 at 0.16 µmol ml−1. 1, 2, and 4 showed no activity to KB cells. Copyright © 2007 John Wiley & Sons, Ltd.
Search operation methods in the relational database machine RINDA
Authors: Hayami Haruo , Satoh Tetsuji , Nakamura Toshio , Kuroiwa Junichi , Takeda Hideaki 
Published: July 2007

AbstractNonindexed queries in a relational database are performed by searching operations, i.e., read out all rows in a table from disks and repetitive condition judgment for each row. Therefore, it takes a long response time on software‐only systems.This paper discusses a searching architecture implemented in a RINDA system which is an attached processor of relational database management systems (DBMS). Performance evaluation indicates that RINDA enhances the response of nonindexed queries by as much as 100 times compared to software‐only systems.The proposed main features are as follows:(1) The effective functions to achieve a short response time are selected and then implemented as a CSP hardware; (2) for a cooperative process with both CSP and software, the internal format of searching conditions is translated in conjunctive canonical form; (3) by executing NOT operator in a logical equation directly in predicates, the SQL three‐value logic degenerates into two‐value; and (4) by using multiple CSPs, parallel search processing is performed.
Proposal for an intermediary shape representation using dots on minimal rectangles
Authors: Yaginuma Yoshitomo , Sakauchi Masao 
Published: July 2007

AbstractIn handling the graphic data of a finite size, the minimal rectangle, which is the minimal rectangle surrounding the graphic data, or the minimal sphere, which is the minimal sphere surrounding the data, often is employed as the intermediary representation of the data to speed‐up the retrieval and manipulation of the graphic data. However, when most of the retrieval time is occupied by the check of the intersection rather than by the tracing of the data structure, as in the case of complex data or data placed on the external memory with a lower speed, a more efficient intermediary representationof the graphic data is required. This can reduce the number of intersection checks and speed‐up the retrieval, compared to the method based on the minimal rectangle or the minimal sphere.From such a viewpoint, this paper proposes an intermediary representation of the graphic data using dots on the minimal rectangle as one such intermediary representation of the data. Theoretical as well as experimental evaluations are presented. It is seen as a result of the theoretical calculation and simulation experiment that compared to the method based on the minimal rectangle, the proposed method can reduce drastically the number of intersection checks which helps to reduce the actual retrieval time. In the case of straight‐line data, for example, of length less than the edge length of the retrieval area, the number of intersection checks can be reduced to one‐fourth that of the method based on the minimal rectangle, even in the worst case.
Sequential circuit test generation by real number simulation
Authors: Hikone Kazunori , Ikeda Mitsuji , Hatayama Kazumi , Hayashi Terumine 
Published: July 2007

AbstractThis paper presents a test generation method using an optimization technique for a single stuck‐at fault in synchronous sequential circuits. This method utilizes a new real number simulation for defining the cost of an input pattern for a given fault and leads an input pattern to a test pattern by changing the input repeatedly to minimize its cost.Since a sequential circuit has internal states, a test pattern for a fault is a sequence of input vectors for several time frames. For generating the whole test pattern, the concept of forward test generation, which performs the convergence calculation process at multiple time frames, is introduced to avoid the problem of local optimum.Experimental results for ISCAS '89 benchmark sequential circuits show the effectiveness of the proposed method for sequential circuit test generation.
Predictor codebook for speaker‐independent speech recognition
Authors: Kawabata Takeshi 
Published: July 2007

AbstractThis paper discusses a method to handle the diversified dynamic features of speech by representing the dynamic features of speech by spectrum predictors and constructing the codebook containing predictors as the elements. The effectiveness of the method for speaker‐independent speech recognition is examined. Three kinds of predictor structures, i.e., the forward predictor, the backward predictor and the interpolator, are examined. The predictor codebook is constructed by the predictor quantization procedure, which is a small modification of the LBG algorithm. For the evaluation of the phoneme recognition level, two kinds statistical evaluation quantities and the phoneme recognition rate have been considered. It is seen as a result that the predictor codebook can realize a high phoneme separation capability and the robustness against the speaker variation. By combining the process actually into the phrase recognition system, the performance at the continuous speech recognition level is evaluated. In either case, the codebook with the backward predictor as the elements exhibited the highest performance.
A randomized algorithm for finding a near‐maximum clique and its experimental evaluations
Authors: Yamada Yoshiaki , Tomita Etsuji , Takahashi Haruhisa 
Published: July 2007

AbstractThe so‐called Maximum Clique Problem is one of the most famous NP‐complete problems for which it is difficult to find a solution. Given an indirected graph, we present here a polynomial‐time randomized algorithm RaCLIQUE for finding a near‐maximum clique. While the basic idea of the algorithm comes from Boltzmann machines, it employs no simulated annealing at all and hence it is simple to control its execution. We have confirmed in experiments for several random and nonrandom graphs with up to 400 nodes that very good solutions can be found efficiently compared with the other conventional algorithms.
Motion Analysis of Nonrigid Objects by Active Contour Models Using Multiscale Images
Authors: Fujimura Kouta , Yokoya Naokazu , Yamamoto Kazuhiko 
Published: July 2007

AbstractThis paper considers the approach to dynamic image processing, which is one of the important problems in the future medical image processing. The tracking of the ojbect and the analysis of the motion are discussed for the dynamic images of a nonrigid object with smooth shape, motion and deformation, which is the case in most medical images. This approach is based on an active contour model defined by an energy function in terms of both intra‐ and interframe constraints for the contour of the object. The contour of the target object is extracted and tracked by minimizing the energy function using multiscale dynamic programming and the motion is analyzed.The dynamic programming in multiscale proposed in this paper is to adjust the search neighborhood of the dynamic programming according to the scale. The coarse or fine neighborhood is defined for the coarse and fine scales, respectively, and the nergy is minimized starting from the coarse scale and shifting to the fine scale. By this scheme, a large motion an deformation of the object can be handled. The proposed motion tracking method has been applied successfully to the dynamic image in the “behavioral analysis of a slug aiming at the analysis of the neural mechanism of learning and memory formation in slugs,” as well as to dynamic echocardiographic images.In the first application, the positive maximum of the curvature along the contour is extracted in the motion analysis as a characteristic point invariant to the deformation of the object. Then the shift of that point is traced. By this approach, the rough motion of the object can be estimated.