Density forecasting: a survey

Tay, Anthony S.; Wallis, Kenneth F.

doi:10.1002/1099-131x(200007)19:4<235::aid-for772>3.0.co;2-l

Cited by 243 publications

(89 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A coefficient of less than one implies a superior performance compared to the naive prediction; calculated values were typically in the region of 0.4. There is no accepted method in the literature for evaluating multi-step-ahead forecasts [19]. However, the density function for an arbitrary time horizon is a mixture of Normal distributions, see Eq.…”

mentioning

confidence: 99%

Predictability of Large Future Changes in a Competitive Evolving Population

2001

View full text Add to dashboard Cite

The dynamical evolution of many economic, sociological, biological and physical systems tends to be dominated by a relatively small number of unexpected, large changes ('extreme events'). We study the large, internal changes produced in a generic multi-agent population competing for a limited resource, and find that the level of predictability actually increases prior to a large change. These large changes hence arise as a predictable consequence of information encoded in the system's global state.01.75.+m, 02.50. Le, 05.40.+j, 87.23.Ge Typeset using REVT E X 1

show abstract

mentioning

confidence: 99%

Predictability of Large Future Changes in a Competitive Evolving Population

2001

View full text Add to dashboard Cite

show abstract

“…Typically the raw data that provide the estimated density are a series of buckets breaking down and covering the expected range of outcomes together with the corresponding forecasted probability. A survey is provided by Tay and Wallis (2000) in the Journal of Forecasting together with extensions in the same issue (19:4). Taylor and Buizza (2006) present an interesting application to pricing weather derivatives (a financial instrument to protect against weather risk so the extreme outcomes are important).…”

Section: Evaluating Point Forecasts and Estimating Forecast Uncertaintymentioning

confidence: 99%

Forecasting and operational research: a review

Fildes

Νικολόπουλος

Crone

et al. 2008

Journal of the Operational Research Society

227

View full text Add to dashboard Cite

From its foundation, operational research (OR) has made many substantial contributions to practical forecasting in organizations. Equally, researchers in other disciplines have influenced forecasting practice. Since the last survey articles in JORS, forecasting has developed as a discipline with its own journals. While the effect of this increased specialization has been a narrowing of the scope of OR's interest in forecasting, research from an OR perspective remains vigorous. OR has been more receptive than other disciplines to the specialist research published in the forecasting journals, capitalizing on some of their key findings. In this paper, we identify the particular topics of OR interest over the past 25 years. After a brief summary of the current research in forecasting methods, we examine those topic areas that have grabbed the attention of OR researchers: computationally intensive methods and applications in operations and marketing. Applications in operations have proved particularly important, including the management of inventories and the effects of sharing forecast information across the supply chain. The second area of application is marketing, including customer relationship management using data mining and computer-intensive methods. The paper concludes by arguing that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied. The benefits of examining the system rather than its separate components are likely to be substantial.

show abstract

“…To date, almost all density forecasts have been for macroeconomic and finance variables. If relative page counts in the Tay and Wallis (2000) survey are a guide, financial forecasters are currently ahead, perhaps because users' desires to make a profit and to control risk provide a clear loss function, in contrast with government policymakers. Investors' need for risk assessment (measured, for example, by Value-at-Risk, with its unfortunately confusing contraction of VaR) is another motivation.…”

Section: The Futurementioning

confidence: 99%

“…Future developments of density forecasting are likely in areas of evaluation or calibration (Tay & Wallis, 2000). Questions of how closely the forecast distribution from one method matches the true probability distribution of the variable compared with the forecast distribution of another method raise complex questions.…”

Section: The Futurementioning

confidence: 99%

Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?

Allen

Morzuch

2006

International Journal of Forecasting

View full text Add to dashboard Cite

In the early 1940s, the Cowles Commission for Research (later, the Cowles Foundation) fostered the development of statistical methodology for application in economics and paved the way for large-scale econometric models to be used for both structural estimation and forecasting. This approach stood for decades. Vector autoregression (VAR), appearing in the 1980s, was a clear improvement over early Cowles Foundation models, primarily because it paid attention to dynamic structure. As a way of imposing long-run equilibrium restrictions on sets of variables, cointegration and error-correction modeling (ECM) gained popularity in the 1980s and 1990s, though ECMs have so far failed to deliver on their early promise. ARCH and GARCH modeling have been used with great success in specialized financial areas to model dynamic heteroscedasticity, though in mainstream econometrics, evidence of their value is limited and conflicting. Concerning misspecification tests, any model will inevitably fail some of them for the simple reason that there are many possible tests. Which failures matter? The root of the difficulty regarding all issues related to modeling is that we can never know the true data generating process. In the next 25 years, what new avenues will open up? With ever greater computational capacity, more complex models with larger data sets seem the way to the future. Will they require the automatic model selection methods that have recently been introduced? Preliminary evidence suggests that these methods can do well. The quality of aggregate data is no better than it was. Will greater use of more disaggregated data be sufficient to provide better forecasts? That remains an open question.

show abstract

Density forecasting: a survey

Cited by 243 publications

References 61 publications

Predictability of Large Future Changes in a Competitive Evolving Population

Predictability of Large Future Changes in a Competitive Evolving Population

Forecasting and operational research: a review

Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?

Contact Info

Product

Resources

About