Coherent Predictions of Low Count Time Series

Brendan McCabe and Gael Martin

No 8/03, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: The application of traditional forecasting methods to discrete count data yields forecasts that are non-coherent. That is, such methods produce non-integer point and interval predictions which violate the restrictions on the sample space of the integer variable. This paper presents a methodology for producing coherent forecasts of low count time series. The forecasts are based on estimates of the p-step ahead predictive mass functions for a family of distributions nested in the integer-valued first-order autoregressive (INAR(1)) class. The predictive mass functions are constructed from convolutions of the unobserved components of the model, with uncertainty associated with both parameter values and model specifcation fully incorporated. The methodology is used to analyse two sets of Canadian wage loss claims data.

Keywords: Forecasting; Discrete Time Series; INAR(1); Bayesian Prediction; Bayesian Model Averaging. (search for similar items in EconPapers)
JEL-codes: C11 C22 C53 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2003-04
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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