Density Forecasting: A Survey

By Anthony S. Tay, K. Wallis. 2000

389 citations recorded by the source corpus.

A density forecast of the realization of a random variable at some future time is an estimate of the probability distribution of the possible future values of that variable. This article presents a selective survey of applications of density forecasting in macroeconomics and finance, and discusses some issues concerning the production, presentation and evaluation of density forecasts.

Subfields: Economics and Econometrics, Finance, Global and Planetary Change

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Citations in the corpus

The Tourism Forecasting Competition (2011, methodology): “The three major literature review articles that attempt to summarise these are Witt and Witt (1995), Li et al.”
Chi-Squared Tests of Interval and Density Forecasts, and the Bank of England's Fan Charts (2003, neutral): “Diebold, Tay and Wallis (1999) present a histogram of z, with 5 k = equiprobable classes, for the SPF density forecasts of inflation ( 28) n = shown in Figure 1.”
THE PERFORMANCE OF SETAR MODELS: A REGIME CONDITIONAL EVALUATION OF POINT, INTERVAL AND DENSITY FORECASTS (2004, methodology): “To supplement these graphical devices, the Kolmogorov-Smirnov test9 can be used on the sample distribution function of the zt series (see Diebold et al., 1999, and Tay and Wallis, 2000).”
Combining inflation density forecasts (2010, background): “Winkler (1968) points out that the logarithmic opinion pool has a naturalconjugate interpretation.”
25 years of time series forecasting (2006, methodology): “A review of density forecasting is provided by Tay & Wallis (2000), along with several other articles in the same special issue of the JoF.”
25 Years of IIF Time Series Forecasting: A Selective Review (2005, methodology): “A review of density forecasting is provided by Tay & Wallis (2000), along with several other articles in the same special issue of the JoF.”
Very‐short‐term probabilistic forecasting of wind power with generalized logit–normal distributions (2012, background): “This has been so in economics and finance (Tay and Wallis, 2000; Timmermann, 2000), earthquake prediction (Kagan and Jackson, 2000) and meteorology (Leutbecher and Palmer, 2008), as well as for weather-related processes like renewable energy production (Bacher et al., 2009; Pinson et al., 2007) or…”
Bayesian Nonparametric Calibration and Combination of Predictive Distributions (2015, background): “…in various fields, including macroeconomics with routine publications of fancharts from central banks, finance with asset allocation strategies based on higher-order moments, andmeteorology with operational ensemble forecasts of future weather (Tay and Wallis 2000; Gneiting and Katzfuss 2014).”
Judgmental forecasting: A review of progress over the last 25 years (2006, background): “Especially in domains like economics and finance, users of forecasts may specifically demand information about providers’ uncertainties surrounding the given predictions (Tay & Wallis, 2002).”
Discrimination ability of the Energy score (2013, background): “This phenomenon touches a wide range of applications, from economics and finance [1, 2], to earthquake prediction [3], while it also has wide appeal in meteorology [4], and for weather-related processes like renewable energy production [5, 6] and floods [7].”