Construction and visualization of optimal confidence sets for frequentist distributional forecasts

David Harris (), Gael Martin (), Indeewara Perera () and Donald Poskitt

No 9/17, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: The focus of this paper is on the quantification of sampling variation in frequentist probabilistic forecasts. We propose a method of constructing confidence sets that respects the functional nature of the forecast distribution, and use animated graphics to visualize the impact of parameter uncertainty on the location, dispersion and shape of the distribution. The confidence sets are derived via the inversion of a Wald test and are asymptotically uniformly most accurate and, hence, optimal in this sense. A wide range of linear and non-linear time series models - encompassing long memory, state space and mixture specifications - is used to demonstrate the procedure, based on artificially generated data. An empirical example in which distributional forecasts of both financial returns and its stochastic volatility are produced is then used to illustrate the practical importance of accommodating sampling variation in the manner proposed.

Keywords: probabilistic forecasts; asymptotically uniformly most accurate confidence regions; time series models; animated graphics; realized volatility; heterogeneous autoregressive model. (search for similar items in EconPapers)
JEL-codes: C13 C18 C53 (search for similar items in EconPapers)
Pages: 30
Date: 2017
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
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