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Efficient probabilistic forecasts for counts
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DOI：10.1111/j.1467-9868.2010.00762.x.png)
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摘要
En 中文
Efficient probabilistic forecasts of integer-valued random variables are derived. The optimality is achieved by estimating the forecast distribution non-parametrically over a given broad model class and proving asymptotic (non-parametric) efficiency in that setting. The method is developed within the context of the integer auto-regressive class of models, which is a suitable class for any count data that can be interpreted as a queue, stock, birth-and-death process or branching process. The theoretical proofs of asymptotic efficiency are supplemented by simulation results that demonstrate the overall superiority of the non-parametric estimator relative to a misspecified parametric alternative, in large but finite samples. The method is applied to counts of stock market iceberg orders. A subsampling method is used to assess sampling variation in the full estimated forecast distribution and a proof of its validity is given.
Keyword:
Iceberg stock market orders
Integer auto-regressive model class
Non-parametric efficiency
Non-parametric maximum likelihood estimation
Probabilistic forecasting
Subsampling
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期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.5W
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