City Research Online

In-sample forecasting: A brief review and new algorithms

Lee, Y. K., Mammen, E., Nielsen, J. P. ORCID: 0000-0002-2798-0817 and Park, B. P. (2018). In-sample forecasting: A brief review and new algorithms. ALEA - Latin American Journal of Probability and Mathematical Statistics, 15, pp. 875-895.

Abstract

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.

Publication Type: Article
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Departments: Cass Business School > Actuarial Science & Insurance
URI: http://openaccess.city.ac.uk/id/eprint/20227
[img]
Preview
Text - Accepted Version
Download (334kB) | Preview

Export

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login