Can cointegration‐based forecasting outperform univariate models? An application to Asian exchange rates

Conventional wisdom holds that restrictions on low‐frequency dynamics among cointegrated variables should provide more accurate short‐ to medium‐term forecasts than univariate techniques that contain no such information; even though, on standard accuracy measures, the information may not improve long‐term forecasting. But inconclusive empirical evidence is complicated by confusion about an appropriate accuracy criterion and the role of integration and cointegration in forecasting accuracy. We evaluate the short‐ and medium‐term forecasting accuracy of univariate Box–Jenkins type ARIMA techniques that imply only integration against multivariate cointegration models that contain both integration and cointegration for a system of five cointegrated Asian exchange rate time series. We use a rolling‐window technique to make multiple out of sample forecasts from one to forty steps ahead. Relative forecasting accuracy for individual exchange rates appears to be sensitive to the behaviour of the exchange rate series and the forecast horizon length. Over short horizons, ARIMA model forecasts are more accurate for series with moving‐average terms of order >1. ECMs perform better over medium‐term time horizons for series with no moving average terms. The results suggest a need to distinguish between ‘sequential’ and ‘synchronous’ forecasting ability in such comparisons. Copyright © 2002 John Wiley & Sons, Ltd.     

Forecasting from fallible data: Correcting prediction bias with stein-rule least squares

In the presence of fallible data, standard estimation and forecasting techniques are biased and inconsistent. Surprisingly, the magnitude of this bias tends to increase, and not diminish, in time series applications as more observations become available. A solution to this ever-present problem, Stein-rule least squares (SRLS), is offered. It corrects for the bias and inconsistency of traditional estimators and provides a means for significantly improving the predictive accuracy of regression-based forecasting techniques. A Monte Carlo study of the forecasting accuracy of SRLS, compared to its alternatives reveals its practical significance and small sample behaviour.     

Evidence for the selection of forecasting methods

Reid (1972) was among the first to argue that the relative accuracy of forecasting methods changes according to the properties of the time series. Comparative analyses of forecasting performance such as the M‐Competition tend to support this argument. The issue addressed here is the usefulness of statistics summarizing the data available in a time series in predicting the relative accuracy of different forecasting methods. Nine forecasting methods are described and the literature suggesting summary statistics for choice of forecasting method is summarized. Based on this literature and further argument a set of these statistics is proposed for the analysis. These statistics are used as explanatory variables in predicting the relative performance of the nine methods using a set of simulated time series with known properties. These results are evaluated on observed data sets, the M‐Competition data and Fildes Telecommunications data. The general conclusion is that the summary statistics can be used to select a good forecasting method (or set of methods) but not necessarily the best. Copyright © 2000 John Wiley & Sons, Ltd.     

Forecasting technique familiarity,satisfaction, usage,and application

A ten-year retrospective study of Mentzer and Cox (1984) was undertaken to answer the question 'Have sales forecasting practices changed over the past ten years?' A mail survey of 207 forecasting executives was employed to investigate this important question. Findings revealed both discrepancies and similarities between today's sales forecasting practices and those of ten years ago. One particular finding indicated greater reliance on and satisfaction with quantitative forecasting techniques today versus ten years ago. Another indicated that forecasting accuracy has not improved over the past ten years, even though the familiarity and usage of various sophisticated sales forecasting techniques have increased. Future research and managerial implications are discussed based on these and other findings.     

Tourism forecasting: A review of empirical research

Many 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.     

Impact of corrections for dynamic selection bias on forecasts of retention behavior

We investigate the impact of corrections for dynamic selection bias on forecasting accuracy in a multi‐period stay/leave model. While corrections for selection bias are needed for consistent coefficient estimates, they do not necessarily produce more accurate forecasts than uncorrected techniques. Theorem 1 shows that, apart from estimation errors, a shrinkage principle applies: the heterogeneity restriction imposed by uncorrected and combination techniques improves accuracy for forecasting individuals that leave, and hurts accuracy for forecasting individuals that stay. This has important implications for decision making because of the potential for asymmetric losses. We also present an illustrative empirical application and results from Monte Carlo experiments. We find that differences in relative accuracy vary directly with the degree of selection bias and inversely with the percentage of the initial population that stays. Copyright © 2007 John Wiley & Sons, Ltd.     

The evolution of sales forecasting management: a 20‐year longitudinal study of forecasting practices

This paper presents results of a survey designed to discover how sales forecasting management practices have changed over the past 20 years as compared to findings reported by Mentzer and Cox (1984) and Mentzer and Kahn (1995). An up‐to‐date overview of empirical studies on forecasting practice is also presented. A web‐based survey of forecasting executives was employed to explore trends in forecasting management, familiarity, satisfaction, usage, and accuracy among companies in a variety of industries. Results revealed decreased familiarity with forecasting techniques, and decreased levels of forecast accuracy. Implications for managers and suggestions for future research are presented. Copyright © 2006 John Wiley & Sons, Ltd.     

A Comparison of Box—Jenkins and objective methods for determining the order of a non-seasonal ARMA Model

This paper evaluates different procedures for selecting the order of a non-seasonal ARMA model. Specifically, it compares the forecasting accuracy of models developed by the personalized Box-Jenkins (BJ) methodology with models chosen by numerous automatic procedures. The study uses real series modelled by experts (textbook authors) in the BJ approach. Our results show that many objective selection criteria provide structures equal or superior to the time-consuming BJ method. For the sets of data used in this study, we also examine the influence of parsimony in time-series forecasting. Defining what models are too large or too small is sensitive to the forecast horizon. Automatic techniques that select the best models for forecasting are similar in size to BJ models although they often disagree on model order.     

Multi‐step forecasting for long‐memory processes

In this paper we present results of a simulation study to assess and compare the accuracy of forecasting techniques for long‐memory processes in small sample sizes. We analyse differences between adaptive ARMA(1,1) L‐step forecasts, where the parameters are estimated by minimizing the sum of squares of L‐step forecast errors, and forecasts obtained by using long‐memory models. We compare widths of the forecast intervals for both methods, and discuss some computational issues associated with the ARMA(1,1) method. Our results illustrate the importance and usefulness of long‐memory models for multi‐step forecasting. Copyright © 1999 John Wiley & Sons, Ltd.     

The teachers/practitioners corner forecasting in business schools: A survey

The study of forecasting techniques has received increased attention in recent years. How to incorporate this topic into the business school curriculum is a frequent subject of discussion. The purpose of this study was to determine whether forecasting is being taught in business schools and how it is incorporated into the curriculum. The survey instrument was sent to 622 member institutions of the American Assembly of Collegiate schools of Business. The importance of teaching forecasting techniques at both the undergraduate and graduate level was investigated.     

Volatility Forecasting via MIDAS,HAR and their Combination: An Empirical Comparative Study for IBOVESPA

In this paper we compare several multi‐period volatility forecasting models, specifically from MIDAS and HAR families. We perform our comparisons in terms of out‐of‐sample volatility forecasting accuracy. We also consider combinations of the models' forecasts. Using intra‐daily returns of the BOVESPA index, we calculate volatility measures such as realized variance, realized power variation and realized bipower variation to be used as regressors in both models. Further, we use a nonparametric procedure for separately measuring the continuous sample path variation and the discontinuous jump part of the quadratic variation process. Thus MIDAS and HAR specifications with the continuous sample path and jump variability measures as separate regressors are estimated. Our results in terms of mean squared error suggest that regressors involving volatility measures which are robust to jumps (i.e. realized bipower variation and realized power variation) are better at forecasting future volatility. However, we find that, in general, the forecasts based on these regressors are not statistically different from those based on realized variance (the benchmark regressor). Moreover, we find that, in general, the relative forecasting performances of the three approaches (i.e. MIDAS, HAR and forecast combinations) are statistically equivalent. Copyright © 2014 John Wiley & Sons, Ltd.     

Traditional versus novel forecasting techniques: how much do we gain?

This article applies two novel techniques to forecast the value of US manufacturing shipments over the period 1956–2000: wavelets and support vector machines (SVM). Wavelets have become increasingly popular in the fields of economics and finance in recent years, whereas SVM has emerged as a more user‐friendly alternative to artificial neural networks. These two methodologies are compared with two well‐known time series techniques: multiplicative seasonal autoregressive integrated moving average (ARIMA) and unobserved components (UC). Based on forecasting accuracy and encompassing tests, and forecasting combination, we conclude that UC and ARIMA generally outperform wavelets and SVM. However, in some cases the latter provide valuable forecasting information that it is not contained in the former. Copyright © 2008 John Wiley & Sons, Ltd.     

The use of non-time series information in sales forecasting: A case study

The contribution of product and industry knowledge to the accuracy of sales forecasting was investigated by examining the company forecasts of a leading manufacturer and marketer of consumable products. The company forecasts of 18 products produced by a meeting of marketing, sales, and production personnel were compared with those generated by the same company personnel when denied specific product knowledge and with the forecasts of selected judgemental and statistical time series methods. Results indicated that product knowledge contributed significantly to forecast accuracy and that the forecast accuracy of company personnel who possessed industry forecasting knowledge (but not product knowledge) was not significantly different from the time series based methods. Furthermore, the company forecasts were more accurate than averages of the judgemental and statistical time series forecasts. These results point to the importance of specific product information to forecast accuracy and accordingly call into question the continuing strong emphasis on improving extrapolation techniques without consideration of the inclusion of non-time series knowledge.     

Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?

Including disaggregate variables or using information extracted from the disaggregate variables into a forecasting model for an economic aggregate may improve forecasting accuracy. In this paper we suggest using the boosting method to select the disaggregate variables, which are most helpful in predicting an aggregate of interest. We conduct a simulation study to investigate the variable selection ability of this method. To assess the forecasting performance a recursive pseudo‐out‐of‐sample forecasting experiment for six key euro area macroeconomic variables is conducted. The results suggest that using boosting to select relevant predictors is a feasible and competitive approach in forecasting an aggregate. Copyright © 2016 John Wiley & Sons, Ltd.     

Method and situational factors in sales forecast accuracy

In this study the interaction of forecasting method (econometric versus exponential smoothing) and two situational factors are evaluated for their effects upon accuracy. Data from two independent sets of ex ante quarterly forecasts for 19 classes of mail were used to test hypotheses. Counter to expectations, the findings revealed that forecasting method did not interact with the forecast time horizon (short versus long term). However, as hypothesized, forecasting method interacted significantly with product/market definition (First Class versus other mail), an indicator of buyer sensitivity to marketing/environmental changes. Results are discussed in the context of future research on forecast accuracy.     

Why do EMD‐based methods improve prediction? A multiscale complexity perspective

Empirical mode decomposition (EMD)‐based ensemble methods have become increasingly popular in the research field of forecasting, substantially enhancing prediction accuracy. The key factor in this type of method is the multiscale decomposition that immensely mitigates modeling complexity. Accordingly, this study probes this factor and makes further innovations from a new perspective of multiscale complexity. In particular, this study quantitatively investigates the relationship between the decomposition performance and prediction accuracy, thereby developing (1) a novel multiscale complexity measurement (for evaluating multiscale decomposition), (2) a novel optimized EMD (OEMD) (considering multiscale complexity), and (3) a novel OEMD‐based forecasting methodology (using the proposed OEMD in multiscale analysis). With crude oil and natural gas prices as samples, the empirical study statistically indicates that the forecasting capability of EMD‐based methods is highly reliant on the decomposition performance; accordingly, the proposed OEMD‐based methods considering multiscale complexity significantly outperform the benchmarks based on typical EMDs in prediction accuracy.     

Familiarity,application, and performance of sales forecasting techniques

This article presents the results of a survey to determine the degree of familiarity and usage, accuracy obtained, and evaluation of different forecasting techniques. It was found that regression analysis, subjective techniques, exponential smoothing, and moving average were well known and used for specific situations. Accuracy was relatively high for aggregate short range forecasts, but decreased for longer range and product level forecasts.     

Forecasting carbon price using a multi-objective least squares support vector machine with mixture kernels

For improving forecasting accuracy and trading performance, this paper proposes a new multi-objective least squares support vector machine with mixture kernels to forecast asset prices. First, a mixture kernel function is introduced into taking full use of global and local kernel functions, which is adaptively determined following a data-driven procedure. Second, a multi-objective fitness function is proposed by incorporating level forecasting and trading performance, and particle swarm optimization is used to synchronously search the optimal model selections of least squares support vector machine with mixture kernels. Taking CO₂ assets as examples, the results obtained show that compared with the popular models, the proposed model can achieve higher forecasting accuracy and higher trading performance. The advantages of the mixture kernel function and the multi-objective fitness function can improve the forecasting ability of the asset price. The findings also show that the models with a high-level forecasting accuracy cannot always have a high trading performance of asset price forecasting. In contrast, high directional forecasting usually means a high trading performance.     

Comparison of forecasting performances: Does normalization and variance stabilization method beat GARCH(1,1)‐type models? Empirical evidence from the stock markets

In this paper, we present a comparison between the forecasting performances of the normalization and variance stabilization method (NoVaS) and the GARCH(1,1), EGARCH(1,1) and GJR‐GARCH(1,1) models. Hence the aim of this study is to compare the out‐of‐sample forecasting performances of the models used throughout the study and to show that the NoVaS method is better than GARCH(1,1)‐type models in the context of out‐of sample forecasting performance. We study the out‐of‐sample forecasting performances of GARCH(1,1)‐type models and NoVaS method based on generalized error distribution, unlike normal and Student's t‐distribution. Also, what makes the study different is the use of the return series, calculated logarithmically and arithmetically in terms of forecasting performance. For comparing the out‐of‐sample forecasting performances, we focused on different datasets, such as S&P 500, logarithmic and arithmetic B?ST 100 return series. The key result of our analysis is that the NoVaS method performs better out‐of‐sample forecasting performance than GARCH(1,1)‐type models. The result can offer useful guidance in model building for out‐of‐sample forecasting purposes, aimed at improving forecasting accuracy.     

Cholesky–ANN models for predicting multivariate realized volatility

Accurately forecasting multivariate volatility plays a crucial role for the financial industry. The Cholesky–artificial neural networks specification here presented provides a twofold advantage for this topic. On the one hand, the use of the Cholesky decomposition ensures positive definite forecasts. On the other hand, the implementation of artificial neural networks allows us to specify nonlinear relations without any particular distributional assumption. Out-of-sample comparisons reveal that artificial neural networks are not able to strongly outperform the competing models. However, long-memory detecting networks, like nonlinear autoregressive model process with exogenous input and long short-term memory, show improved forecast accuracy with respect to existing econometric models.