In-sample forecasting: A brief review and new algorithms

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4
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SCOPUS

4

초록

Statistical methods often distinguish between in-sample and out-of-sample approaches. In particular this is the case when time is involved. Then often time series methods are proposed that extrapolate past patterns into the future via complicated recursion formulas. Standard statistical inference is on the other hand concerned with estimating parameters within the given sample. This review paper is about a statistical methodology, where all parameters are estimated in-sample while producing a forecast out-of-sample without recursion or extrapolation. A new super-simulation algorithm ensures a faster implementation of the simplest and perhaps most important version of in-sample forecasting.

키워드

Structured nonparametric models; age-cohort models; density estimation; kernel smoothing; backfitting; Chain Ladder; UK mesothelioma mortality; PERIOD-COHORT MODELS; DOUBLE-CHAIN LADDER; MESOTHELIOMA MORTALITY; SELF-CONSISTENT; CLAIM AMOUNTS; AGE-PERIOD; TIME; REDISTRIBUTION; ESTIMATORS; PROJECTION
제목
In-sample forecasting: A brief review and new algorithms
저자
Lee, Y. K.; Mammen, E.; Nielsen, J. P.; Park, B. U.
DOI
10.30757/ALEA.v15-33
발행일
2018
유형
Review
저널명
Alea
권
15
호
2
페이지
875 ~ 895