Forecasting discrete valued low count time series

By R. Freeland, Brendan McCabe. 2004

209 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/0f6be2f9289817cd571e3f1e2c731c9475407da3

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Citations in the corpus

MCMC for Integer‐Valued ARMA processes (2007, unknown): “Therefore these standard models are wholely inappropriate for integer valued time-series, especially for low frequency count data where continuous approximations of the discrete process are particularly unsuitable (see, for example, Freeland and McCabe (2004) and McCabe and Martin (2005).”
Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models (2011, unknown): “Finally, our study is related to count data predictions as in Sutradahar (2008), Jung et al. (2006) and Freeland and McCabe (2004).”
25 years of time series forecasting (2006, methodology): “Freeland & McCabe (2004) proposed using the median of the forecast distribution, and gave a method for computing confidence intervals for the entire forecast distribution in the case of integer-valued autoregressive (INAR) models of order 1.”
A bivariate INAR(1) process with application (2011, methodology): “Freeland and McCabe (2004b) suggest Statistical Modelling 2011; 11(4): 325–349 instead the use of the median of this distribution which always lies in the support of the series and is therefore coherent.”
Bayesian predictions of low count time series (2005, methodology): “The Maximum Likelihood estimates of α and λ in the PAR analysis of Freeland and McCabe (2004a) are equal to 0.240 and 0.134 respectively, with associated 95% confidence intervals of (0.007, 0.472) and (0.064, 0.204).”
25 Years of IIF Time Series Forecasting: A Selective Review (2005, methodology): “Freeland & McCabe (2004) proposed using the median of the forecast distribution, and gave a method for computing confidence intervals for the entire forecast distribution in the case of integer-valued autoregressive (INAR) models of order 1.”
Model Selection, Estimation and Forecasting in INAR(p) Models: (2008, methodology): “So, generalising Freeland and McCabe (2004a), let k Xt k kXt k, t = p+1; :::; T , be the set of residuals for the kth thinning process and let "t be residual set for the arrivals component.”
Estimation in conditional first order autoregression with discrete support (2005, background): “and m* = min(x0, Xz) for different values of x 0. The value with the highest probability mass is chosen as Xo ( Freeland and McCabe, 2003a, call this procedure point mass forecasting.)”
A geometric time series model with dependent Bernoulli counting series (2013, unknown): “…and Alzaid (1987), Alzaid and Al-Osh (1988, 1990), Du and Li (1991), Al-Osh and Aly (1992), Franke and Subba Rao (1995), Latour (1997), Silva and Oliveira (2004), Freeland and McCabe (2004a, 2004b, 2005), Jung and Tremayne (2006), Weiß (2008a), Cui and Lund (2010) and Bakouch and Ristić (2010).”