A comparison of the forecasting performance of WEFA and ARIMA time series methods

This paper examines the forecasting performance of the Wharton model (MARK III) over the period 1973 through 1975 and compares it with that of ARIMA models' performance over the same period. Despite strong intimation in the literature to the contrary, we find that this econometric model, at least, exhibits greater accuracy in every respect relative to ARIMA methods, in terms of its forecasts cum constant adjustments. When constant adjustments are disallowed then its forecasts are still more accurate than ARIMA forecasts over a 4- and 8-quarter forecasting horizon, but less accurate over a 1-quarter horizon. The comparison was carried out over twenty three macrovariables, under a slight handicap for the Wharton Model, in that the latter's parameters were estimated over a sample ending in 1969.3 while the ARIMA models were reidentified and reestimated as of the quarter immediately preceding the forecast.     

On the forecasting performance of a small-scale DSGE model

Dynamic stochastic general equilibrium (DSGE) models have recently become standard tools for policy analysis. Nevertheless, their forecasting properties have still barely been explored. In this article, we address this problem by examining the quality of forecasts of the key U.S. economic variables: the three-month Treasury bill yield, the GDP growth rate and GDP price index inflation, from a small-size DSGE model, trivariate vector autoregression (VAR) models and the Philadelphia Fed Survey of Professional Forecasters (SPF). The ex post forecast errors are evaluated on the basis of the data from the period 1994–2006. We apply the Philadelphia Fed “Real-Time Data Set for Macroeconomists” to ensure that the data used in estimating the DSGE and VAR models was comparable to the information available to the SPF.Overall, the results are mixed. When comparing the root mean squared errors for some forecast horizons, it appears that the DSGE model outperforms the other methods in forecasting the GDP growth rate. However, this characteristic turned out to be statistically insignificant. Most of the SPF's forecasts of GDP price index inflation and the short-term interest rate are better than those from the DSGE and VAR models.     

A comparison of the accuracy of the Box-Jenkins method with that of automated forecasting methods

The focus of the research described in this paper is on presenting an automated forecasting system that encompasses an objective ARIMA method with the Holt-Winters procedure in a weighted averaging scheme. The system is applied to M-Competition data and the results are compared to the subjective Box-Jenkins forecasts as well as to results from two other automated methods, CAPRI and SIFT. The comparison reveals that especially for one-step ahead forecasting, the automated system competes favorably with both automated methods and the individualized Box-Jenkins analysis.     

Relative performance of judgmental methods for forecasting the success of megaprojects

Forecasting the success of megaprojects, such as the Olympic Games or space exploration missions, is a very difficult but important task, due to their complexity and the large capital investment they require. Typically, megaproject stakeholders do not employ formal forecasting methods, but instead rely on impact assessments and/or cost–benefit analysis; however, as these tools do not necessarily include forecasts, there is no accountability. This study evaluates the effectiveness of judgmental methods for successfully forecasting the accomplishment of specific megaproject objectives, where the measure of success is the collective accomplishment of such objectives. We compare the performances of three judgmental methods used by a group of 69 semi-experts: unaided judgement (UJ), semi-structured analogies (s-SA), and interaction groups (IG). The empirical evidence reveals that the use of s-SA leads to accuracy improvements relative to UJ. These improvements are amplified further when we introduce the pooling of analogies through teamwork in IG.     

A comparison of MRP lot sizing methods considering capacity change costs

Capacity management is the planning and leveling of resources required (load) against the resources available (capacity). In this study, the lot size models used by Material Requirements Planning (MRP) had a major effect on the work center load profiles generated by Capacity Requirements Planning (CRP). Therefore, the selection of lot size models for MRP systems is an important decision for capacity management as well as materials management.The results of this study highlight the operating characteristics of specific lot size models considering setup, inventory carrying, and capacity associated costs. For example, the Economic Order Quantity model and the Lot-For-Lot model in certain situations can help level load. The Periodic Order Quantity and Least Total Cost models especially for high cost structure ratios tend to result in erratic and lumpy work center load profiles. The reasons for such operating behavior are examined. Other concepts and relationships important to capacity management are discussed.     

A univariate analysis of EMS exchange rates using a target zone model

The models in the literature on exchange-rate target zones imply a non-linear time series model for the exchange rate. We show how the parameters of such models can be estimated and develop Maximum Likelihood and Method of Simulated Moments estimators for the target zone model of Krugman (1991). The Maximum Likelihood estimator is based on a computationally attractive approximation to the exact predictive density of the continuous time model. Monte Carlo experiments are used to assess the properties of this estimator. In the empirical part we estimate the model with data on recent EMS exchange rates. We find that the Krugman (1991) target zone model is not able to explain the full observed kurtosis and conditional heteroscedasticity of the exchange-rate returns.     

A model of vertical integration and investment in generation capacity in electricity markets: The case of the bidding game

The aim of this work is to assess the impact of (partial) vertical integration between generators and retailers on generation capacity choice and its subsequent welfare consequences. We present a framework in which final demand is perfectly inelastic and stochastic. Nevertheless, wholesale demand is elastic because of the existence of outside opportunities (mainly international transmission capacity). The model is a three-stage game. Neither transmission nor retail costs are taken into account.

In the first stage of the game, generators choose capacity only knowing distribution of demand and thus maximizing their expected profit. The second stage of the game represents the competition for market share between retailers in a market where consumers have switching costs. The former face unknown demand and maximize their utility based on two factors: the expected profit and a risk element. Finally, generators submit bid functions to the system operator given known demand and maximizing their profit during the last stage of the game. Retailers and generators interact in the wholesale market, which is cleared by the system operator whose function is to match supply (represented by the bids of the generators) and demand through a system of single price auctions. The wholesale market is the only means to buy and sell energy; there are no bilateral contracts between firms, except if they are vertically integrated.

We compare fully disintegrated and partially vertically integrated structures using a comparative statics approach. In this paper, the analysis will focus on the last stage of the game: the bidding game. We find that partial vertical integration between generators and retailers tends to lower wholesale prices but not unambiguously. Depending on which firm (vertically integrated or disintegrated generator) has installed the higher capacity and depending on level of demand, prices can stay unchanged or even rise.     

The effect of external supply knowledge acquisition,development activities and organizational status on the supply performance of SMEs

Given the increasingly strategic role of external resources, acquiring knowledge about current suppliers and the broader supply market is an important and demanding task for the purchasing and supply management (PSM) function of a firm. Performance-improvement-oriented application of external supply knowledge present further challenges for the function. To examine this, we draw on the knowledge-based view and develop a hypothesized model in which supply knowledge acquisition drives PSM exploration and exploitation orientations which in turn mediate the organizational status of PSM function in terms of supply performance. We test the model on an SME-focused and survey-based dataset, using structural equation modelling. Our results indicate that an exploitative orientation is associated with knowledge gained from the supply base, whereas an explorative orientation is predominantly associated with supply market knowledge and less with supply base knowledge, suggesting natural pairings. The findings also show how an exploitative development orientation mediates the positive association of the PSM function's organizational status with supply performance. Driven by supply base knowledge, a status-empowered exploitative PSM orientation may suppress supply market based explorative orientation in resource-scarce SMEs, thus appearing to serve as the sole path to supply performance. Our research contributes by pointing out the significance of the knowledge-resource, and the knowledge-based view, in understanding performance in PSM.     

Stability in the inefficient use of forecasting systems: A case study in a supply chain company

Computer-based demand forecasting systems have been widely adopted in supply chain companies, but little research has studied how these systems are actually used in the forecasting process. We report the findings of a case study of demand forecasting in a pharmaceutical company over a 15-year period. At the start of the study, managers believed that they were making extensive use of their forecasting system that was marketed based on the accuracy of its advanced statistical methods. Yet most forecasts were obtained using the system’s facility for judgmentally overriding the automatic statistical forecasts. Carrying out the judgmental interventions involved considerable management effort as part of a sales & operations planning (S&OP) process, yet these often only served to reduce forecast accuracy. This study uses observations of the forecasting process, interviews with participants and data on the accuracy of forecasts to investigate why the managers continued to use non-normative forecasting practices for many years despite the potential economic benefits that could be achieved through change. The reasons for the longevity of these practices are examined both from the perspective of the individual forecaster and the organization as a whole.     

The inventory performance of forecasting methods: Evidence from the M3 competition data

Forecasting competitions have been a major driver not only of improvements in forecasting methods’ performances, but also of the development of new forecasting approaches. However, despite the tremendous value and impact of these competitions, they do suffer from the limitation that performances are measured only in terms of the forecast accuracy and bias, ignoring utility metrics. Using the monthly industry series of the M3 competition, we empirically explore the inventory performances of various widely used forecasting techniques, including exponential smoothing, ARIMA models, the Theta method, and approaches based on multiple temporal aggregation. We employ a rolling simulation approach and analyse the results for the order-up-to policy under various lead times. We find that the methods that are based on combinations result in superior inventory performances, while the Naïve, Holt, and Holt-Winters methods perform poorly.     

A labor-oriented quarterly econometric forecasting model of the youngstown-Warren SMSA

Our model contains forty-two stochastic equations and thirty-two identities designed to explain variation in manhours of work, employment, weekly wages and the wage bill by sector of activity for the Youngstown-Warren SMSA. Methodology in the development of these equations is discussed and the regression results shown. The paper evaluates the simulation results of the model by showing the root mean squares error and percent root mean squares error for the major aggregates using both dynamic and historic system simulations.     

A Monte Carlo study of the forecasting performance of empirical SETAR models

In this paper we investigate the multi-period forecast performance of a number of empirical self-exciting threshold autoregressive (SETAR) models that have been proposed in the literature for modelling exchange rates and GNP, among other variables. We take each of the empirical SETAR models in turn as the DGP to ensure that the ‘non-linearity’ characterizes the future, and compare the forecast performance of SETAR and linear autoregressive models on a number of quantitative and qualitative criteria. Our results indicate that non-linear models have an edge in certain states of nature but not in others, and that this can be highlighted by evaluating forecasts conditional upon the regime. Copyright © 1999 John Wiley & Sons, Ltd.     

Macroeconomic forecasting with a vector arima model: A case study of the finnish economy

The vector ARIMA (VARIMA) model is a multivariate generalization of the univariate ARIMA model. VARIMA can accomodate assumptions on exogeneity and on contemporaneous relationships. Exogeneous forecasts and non-zero future shocks make it possible to generate alternative forecasts. In a case study VARIMA well describes developments in the 1970's and successfully competes with judgemental methods and ARIMA in providing a general outlook of the early 1980's.     

A logistics model for emergency supply of critical items in the aftermath of a disaster

This paper proposes a logistics model for delivery of prioritized items in disaster relief operations. It considers multi-items, multi-vehicles, multi-periods, soft time windows, and a split delivery strategy scenario, and is formulated as a multi-objective integer programming model. To effectively solve this model we limit the number of available tours. Two heuristic approaches are introduced for this purpose. The first approach is based on a genetic algorithm, while the second approach is developed by decomposing the original problem. We compare these two approaches via a computational study. The multi-objective problem is converted to a single-objective problem by the weighted sum method. A case study is presented to illustrate the potential applicability of our model. Also, presented is a comparison of our model with that proposed in a recent paper by Balcik et al. [6]. The results show that our proposed model outperforms theirs in terms of delivering prioritized items over several time periods.     

On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks

Daily and weekly seasonalities are always taken into account in day-ahead electricity price forecasting, but the long-term seasonal component has long been believed to add unnecessary complexity, and hence, most studies have ignored it. The recent introduction of the Seasonal Component AutoRegressive (SCAR) modeling framework has changed this viewpoint. However, this framework is based on linear models estimated using ordinary least squares. This paper shows that considering non-linear autoregressive (NARX) neural network-type models with the same inputs as the corresponding SCAR-type models can lead to yet better performances. While individual Seasonal Component Artificial Neural Network (SCANN) models are generally worse than the corresponding SCAR-type structures, we provide empirical evidence that committee machines of SCANN networks can outperform the latter significantly.     

An engineering approach to LSE modelling of experience curves in the electricity supply industry

This review is a supplement to the paper by Sharp and Price (1990) and should be regarded as an alternative engineering approach to the modelling and forecasting of experience, or learning, curves. It highlights the problems associated with accurately defining a model to time series that show a combination of a continuous trend and a cyclical component, as detected by the authors in the Sharp and Price data. The authors give a number of alternative perspectives of the same time series, in this case average thermal efficiency data from the U.K. electricity supply industry, with the corresponding conclusions associated with each approach. Particular attention is drawn to the use of the “time constant learning curve” quoted by Sharp and Price which the authors show is a reasonable predictor of the average thermal efficiency. However, a tremendous improvement results from selecting the “ripple” model as a thermal efficiency predictor.     

航空快递公司业务量的预测方法

二十世纪九十年代以来，航空快递市场飞速发展。一些航空快递公司正抓住这一黄金时期，加速发展。发展离不开基础设施建设．而正确预测出将来的业务量是确定基础设施规模关键。本文以北京某航空快递公司业务量预测为例，介绍几种业务量预测常用的方法。     

A two-level electricity demand model : Evaluation of the connecticut time-of-day pricing test

A two-stage budgeting model is developed for electricity demand where comsumption in each period is treated as a different commodity. A relative household demand model is first estimated, a consistent price index for electricity is constructed, and then a total electricity consumption model is estimated. Economic procedures are derived which permit application of the model to both time-of-day price situations and also declining vlock price situatiions which result in non-linear budget sets. The model is applied to both types of situations- the data from the Connecticut time-of-day pricing test as well as data from the declining block rate situation of the prevoius year. The model is also tested in a forecasting application to time-of-day customers.     

Influence of the entrepreneur's capacity in business performance

The article presents a theoretical model of the entrepreneur's capacities and their influence on business performance. Starting from the traditional theory of dynamic capacities, those related to entrepreneurship and/or that influence the entrepreneur's capacities are identified, determining factors such as training, experience and confidence. In addition, other factors such as the environmental and institutional environment have been taken into account as they affect the entrepreneurship. For this purpose, a study was carried out with the Smart PLS software in a sample of companies in the tourism sector in the Mar Menor Region (Spain). An essential factor in the development of entrepreneurship in the region is the sustainability of the coastal lagoon. Some considerations are obtained from the study of the factors that determine the model of capacities, in which the confidence of the entrepreneur and the environmental sustainability, exert a positive and significant influence in the business performance. Furthermore, among the mentioned capacities of the entrepreneurial influence, experience and trust are the ones that most influence the success of the tourism business.