Applied Forecasting

In this issue we have Predicting Elections from Biographical Information about Candidates, Forecasting electricity spot market prices with a k-factor GIGARCH process, Does money matter in inflation forecasting? Forecasting VaR and Expected Shortfall using Dynamical Systems, and more.

SunGard's Fame is integrating its historical, global market data with FINALYZ AG's modeling tools to help financial services institutions enhance their ability to accurately analyze and forecast pricing and valuation on global instruments.

You don't need a crystal ball to tell you what is going to happen next in the economy. You need a statistical model. A new method from North Carolina State University can help researchers determine which economic variables they should focus on by identifying whether a variable can be predicted.

In this issue we have Forecasting with high frequency based volatility (HEAVY) models, Forecasting Inflation Using Dynamic Model Averaging, Forecasting Performance for US Output Growth and Inflation, Evaluating German Business Cycle Forecasts, Latent Variable Approach to Forecasting the Unemployment Rate and more.

Multivariate time series may contain outliers of different types. In presence of such outliers, applying standard multivariate time series techniques becomes unreliable. A robust version of multivariate exponential smoothing is proposed. The method is...

We argue that the next generation of macro modellers at Inflation Targeting central banks should adapt a methodology from the weather forecasting literature known as `ensemble modelling\\\'. In this approach, uncertainty about model specifications (e.g.,...

The Summer 2009 issue of Foresight is now available, and features a section on “Rethinking the Ways We Forecast.” Here is Editor Len Tashman’s preview:

Are traditional forecasting tools suitable for predicting complex systems like the economy and the global climate? Basically, no, argue David Orrell and Patrick McSharry: such tools are based on equations that model a system’s components but ignore its emergent properties, the global effects arising from those components. They call this the reductionist approach. All models, they assert, make simplifying assumptions, but the reductionist
approach makes the wrong assumptions.

David and Patrick then describe key elements for more effective modeling of complex systems, including agent-based models, network analysis, nonlinear dynamics, and scenarios. These models shift the emphasis from the point forecasts most often demanded by business decision makers to the assessment of risks in what the future may bring.

The section continues with two commentaries. Roy Batchelor illustrates the difference between a simple macroeconomic-forecasting model (representing the reductionist approach) and a complex-system model, and compares the virtues of the two viewpoints. Roy’s concern is that specific complex-systems models for the economy may be unwieldy and unstable, complexifying without improving forecasting.

Robert Fildes and Paul Goodwin note that the complex-systems models are untried in the arena of economics and that we need to explore whether these models are better applied individually or together. They reinforce the Orrell-McSharry thesis that point forecasts are overemphasized and misapplied but argue that they are unavoidable in the business world. Robert and Paul conclude with their own scenario on the future of forecast modeling.

This special feature wraps up with David and Patrick’s responses to the commentaries. They emphasize that complex-systems models need not be complex and that many biological applications of these models are relatively simple, indeed simpler than many current models in use.

In your editor’s view, the section reveals essential agreement among forecasters that there is much room for rethinking and refining our current approaches to forecasting, placing greater emphasis on risk assessment and preparation for uncertain futures.