X11与联立方程组模型在宏观经济月度预测中的应用

宏观经济计量模型是当今世界各国普遍采用的一种研究国民经济运行情况的有效工具,主要应用于结构分析、经济政策评价和经济发展预测。本论文探索建立一个宏观经济月度计量模型,运用建立的月度计量模型进行短期预测,为经济决策提供依据。     

Efficiency of the independence assumption in the combination of forecasts

In seeking to minimise the composite forecast error variance of a linear combination of forecasts, contradictory suggestions have been reported concerning the practice of making the assumption of independence between forecast errors. This assumption can introduce robustness though its avoidance of sampling errors in the estimation of correlation coefficient(s), although it does render the composite forecast theoretically suboptimal. By means of theory and experimental simulation, this paper examines the circumstances whereby the independence assumption may produce more efficient composite forecasts. Its applicability is shown to depend both upon the underlying correlation structure and relative size of forecast errors as well as the observation base available for estimation.     

Long-Term Forecasts of Optimal Approximations to the Load Duration Curve

Little has been done by way of developing an objective technique for long-term forecasting of a utility load duration curve. This paper endeavours to rectify this situation by developing a methodology to provide forecasts of an economically optimal approximation to the load duration curve. A dynamic programming algorithm serves as the basis of the optimal approximations over a known horizon. These approximations are then forecast using time series analysis and an econometric model. The approach is implemented and the results are encouraging.     

Estimating a retailer's base stock level: an optimal distribution center order forecast policy

Errors in order forecasts are a salient source of inefficiencies in retail supply chains. Many operational decisions made by suppliers hinge on order forecasts, which typically are based solely on either order or point-of-sale (POS) history. Using a discrete-time formulation, this research demonstrates that if a supplier knows that a retailer is using a base stock policy, it should use that knowledge to forecast the retailer's orders, even if the supplier does not know the base stock level and/or have access to POS data.     

Individual forecasting behavior

Participants of a laboratory experiment judgmentally forecast a time series. In order to support their forecasts they are given a highly correlated indicator with a constant lead period of one. The subjects are not given any other information than the time series realizations and have to base their forecasts on pure eyeballing/chart-reading. Standard economic models do not appropriately account for the features of individual forecasts: These are typically affected by intra- and inter-individual instability of behavior. We extend the scheme theory by Otwin Becker for the explanation of individual forecasts by simple schemes based on visually perceived characteristics of the time series. We find that the forecasts of most subjects can be explained very accurately by only a few schemes.     

Determination of an Optimal Forecast Model for Ambulance Demand Using Goal Programming

A multistep approach to determining the optimal parameters of an exponential smoothing model was used to forecast emergency medical service (E.M.S.) demand for four counties of South Carolina. Daily emergency and routine (non-emergency) demand data were obtained and forecast statistics generated for each county sampled, using Winters' exponential smoothing model. A goal programme was formulated to combine forecast results for emergency calls with routine call forecasts. The goal programme gave a higher priority to accurate forecasting of emergency demand. The forecast model generated implicitly weights demand by severity and provides a reliable estimate of demand overall. The optimal parameter values for the smoothing model were obtained by minimizing the objective function value of the goal programming problem. The parameter values obtained were used to forecast demand for E.M.S. in the selected counties. The results of the model were compared to those using a multiple linear regression model and a single-objective-based exponential smoothing model for 2 months of data. When compared with two single-objective forecast models, the multiple-objective approach yielded more accurate forecasts and, therefore, was more cost-effective for the planner. The model presents and demonstrates a theoretical approach to improving the accuracy of ambulance demand forecasts. The possible impact of this approach on planning efficiency is discussed.     

Using combined forecasts with changing weights for electricity demand profiling

Day-ahead half-hourly demand forecasts are required for scheduling and for calculating the daily electricity pool price. One approach predicts turning points on the demand curve and then produces half-hourly forecasts by a heuristic procedure, called profiling, which is based on a past demand curve. This paper investigates possible profiling improvements. Using a cubic smoothing spline in the heuristic leads to a slight improvement. Often, several past curves could reasonably be used in the profiling method. Consequently, there are often several demand curve forecasts available. Switching and smooth transition forecast combination models are considered. These models enable the combining weights to vary across the 48 half-hours, which is appealing as different forecasts may be more suitable for different periods. Several criteria are used to control the changing weights, including weather, and the methodology is extended to the case of more than two forecasts. Empirical analysis gives encouraging results.     

Using clustering to improve sales forecasts in retail merchandising

Given sales forecasts for a set of items along with the standard deviation associated with each forecast, we propose a new method of combining forecasts using the concepts of clustering. Clusters of items are identified based on the similarity in their sales forecasts and then a common forecast is computed for each cluster of items. On a real dataset from a national retail chain we have found that the proposed method of combining forecasts produces significantly better sales forecasts than either the individual forecasts (forecasts without combining) or an alternate method of using a single combined forecast for all items in a product line sold by this retailer.     

News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions

The macroeconomic climate influences operations with regard to, e.g., raw material prices, financing, supply chain utilization and demand quotas. In order to adapt to the economic environment, decision-makers across the public and private sectors require accurate forecasts of the economic outlook. Existing predictive frameworks base their forecasts primarily on time series analysis, as well as the judgments of experts. As a consequence, current approaches are often biased and prone to error. In order to reduce forecast errors, this paper presents an innovative methodology that extends lag variables with unstructured data in the form of financial news: (1) we apply a variety of models from machine learning to word counts as a high-dimensional input. However, this approach suffers from low interpretability and overfitting, motivating the following remedies. (2) We follow the intuition that the economic climate is driven by general sentiments and suggest a projection of words onto latent semantic structures as a means of feature engineering. (3) We propose a semantic path model, together with estimation technique based on regularization, in order to yield full interpretability of the forecasts. We demonstrate the predictive performance of our approach by utilizing 80,813 ad hoc announcements in order to make long-term forecasts of up to 24 months ahead regarding key macroeconomic indicators. Back-testing reveals a considerable reduction in forecast errors.     

Short-run electricity load forecasting with combinations of stationary wavelet transforms

Short-term forecasting of electricity load is an essential issue for the management of power systems and for energy trading. Specific modeling approaches are needed given the strong seasonality and volatility in load data. In this paper, we investigate the benefit of combining stationary wavelet transforms to produce one day-ahead forecasts of half-hourly electric load in France. First, we assess the advantage of decomposing the aggregate load into several subseries with a wavelet transform. Each component is predicted separately and aggregated to get the final forecast. One innovation of this paper is to propose several approaches to deal with the boundary problem which is particularly detrimental in electricity load forecasting. Second, we examine the benefit of combining forecasts over individual models. An extensive out-of-sample evaluation shows that a careful treatment of the border effect is required in the multiresolution analysis. Combinations including the wavelet predictions provide the most accurate forecasts. This result is valid with several assumptions about the forecast error in temperature and for different types of hours (peak, normal, off-peak), different days of the week and various forecasting periods.     

Forecast Accuracy: The Financial Benefits to a Small Airline

This paper assesses possible gains to be made from increasing forecast accuracy. It examines the financial return from improving passenger revenue forecasts for a small airline, both in theory assuming ‘optimal’ cash management and in practice using policies currently in operation in the firm. It concludes that the gains are unlikely to outweigh the costs, that greater returns are likely to be available through better cash management and that the nature of forecast errors must be considered along with their size.     

A Method for Pooling Forecasts

A group of experts is to produce a joint forecast of a set of unknowns. Each expert is asked to distribute subjectively a given sum of confidence weights over his own forecasts. A joint forecast is computed as the product sum of the individual forecasts and weights deduced from the individual's weights. A probabilistic interpretation of this procedure is provided and a measure of the reliability of the joint forecasts is suggested. A Bayesian variant can be constructed by introducing sample information.     

A data-integrated simulation model to forecast ground-level ozone concentration

Elevated ground-level ozone is hazardous to people’s health and destructive to the environment. This research develops a novel data-integrated simulation to forecast ground-level ozone (SIMGO) concentration based on a real data set collected from seven monitoring sites in the Dallas-Fort Worth area between January 1, 2005 and December 31, 2007. Tree-based models and kernel density estimation (KDE) were utilized to extract important knowledge from the data for building the simulation. Classification and Regression Trees (CART), data mining tools for prediction and classification, were used to develop two tree structures in order to forecast ground-level ozone based on factors such as solar radiation and outdoor temperature. Kernel density estimation is used to estimate continuous distributions for the ground-level ozone concentration for seven days in advance. One week forecasts obtained from SIMGO for different months of a year is presented.     

A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction

Forecasting methods are routinely employed to predict the outcome of competitive events (CEs) and to shed light on the factors that influence participants’ winning prospects (e.g., in sports events, political elections). Combining statistical models’ forecasts, shown to be highly successful in other settings, has been neglected in CE prediction. Two particular difficulties arise when developing model-based composite forecasts of CE outcomes: the intensity of rivalry among contestants, and the strength/diversity trade-off among individual models. To overcome these challenges we propose a range of surrogate measures of event outcome to construct a heterogeneous set of base forecasts. To effectively extract the complementary information concealed within these predictions, we develop a novel pooling mechanism which accounts for competition among contestants: a stacking paradigm integrating conditional logit regression and log-likelihood-ratio-based forecast selection. Empirical results using data related to horseracing events demonstrate that: (i) base model strength and diversity are important when combining model-based predictions for CEs; (ii) average-based pooling, commonly employed elsewhere, may not be appropriate for CEs (because average-based pooling exclusively focuses on strength); and (iii) the proposed stacking ensemble provides statistically and economically accurate forecasts. These results have important implications for regulators of betting markets associated with CEs and in particular for the accurate assessment of market efficiency.     

Group forecasting accuracy in hotels

Yield management helps hotels more profitably manage the capacity of their rooms. Hotels tend to have two types of business: transient and group. Yield management research and systems have been designed for transient business in which the group forecast is taken as a given. In this research, forecast data from approximately 90 hotels of a large North American hotel chain were used to determine the accuracy of group forecasts and to identify factors associated with accurate forecasts. Forecasts showed a positive bias and had a mean absolute percentage error (MAPE) of 40% at two months before arrival; 30% at one month before arrival; and 10–15% on the day of arrival. Larger hotels, hotels with a higher dependence on group business, and hotels that updated their forecasts frequently during the month before arrival had more accurate forecasts.     

Experts' adjustment to model-based SKU-level forecasts: does the forecast horizon matter?

Experts (managers) may have domain-specific knowledge that is not included in a statistical model and that can improve short-run and long-run forecasts of SKU-level sales data. While one-step-ahead forecasts address the conditional mean of the variable, model-based forecasts for longer horizons have a tendency to convert to the unconditional mean of a time series variable. Analysing a large database concerning pharmaceutical sales forecasts for various products and adjusted by a range of experts, we examine whether the forecast horizon has an impact on what experts do and on how good they are once they adjust model-based forecasts. For this, we use regression-based methods and we obtain five innovative results. First, all horizons experience managerial intervention of forecasts. Second, the horizon that is most relevant to the managers shows greater overweighting of the expert adjustment. Third, for all horizons the expert adjusted forecasts have less accuracy than pure model-based forecasts, with distant horizons having the least deterioration. Fourth, when expert-adjusted forecasts are significantly better, they are best at those distant horizons. Fifth, when expert adjustment is down-weighted, expert forecast accuracy increases.     

N-tuple S&P patterns across decades, 1950–2011

Numerous studies have analyzed the movements of the S&P 500 index using several methodologies such as technical analysis, econometric modeling, time series techniques and theories from behavioral finance. In this paper we take a novel approach. We use daily closing prices for the S&P 500 index for a very long period from 1/3/1950 to 7/19/2011 for a total of 15,488 daily observations. We then investigate the up and down movements and their combinations for 1–7 days giving us multiple possible patterns for over six decades. Some patterns of each type are more dominant across decades. We split the data into training and validation sets and then select the dominant patterns to build conditional forecasts in several ways, including using a decision tree methodology. The best model is correct 51 % of the time on the validation set when forecasting a down day, and 61 % when forecasting an up day. We show that certain conditional forecasts outperform the unconditional random walk model.     

A data mining approach to forecast behavior

This study presents a data mining analysis of forecasting patterns in a supply chain. Multiple customers who are auto manufacturers order from a large auto parts supplier. The auto manufacturers provide forecasts for future orders and update them before the due date. The supplier uses these forecasts to plan production in advance. The accuracy of the forecasts varies from customer to customer. We provide a framework to analyze the forecast performance of the customers. There are different complexities in forecasts that are captured from our data set. Daily flow analysis helps to transform data and obtain accuracy ratios of forecasts. Customers are then classified based on their forecast performances. We demonstrate the application of some recent developments in clustering and pattern recognition analysis to performance analysis of customers.     

Combining forecasts

This paper reviews current theories and practice of combining forecasts from a wide perspective. The various motivations for combining are discussed, with particular attention to model credibility as well as forecast accuracy. Issues which affect the performance of composite forecasts are identified. A concluding section reflects upon the limitations of the extreme perspective of pragmatism which fosters this approach to forecasting.