Likelihood and other approaches to prediction in dynamic models
type: Item
instance of: scholarly article
title: Likelihood and other approaches to prediction in dynamic models
zbMATH Open document ID: 0649.62107
DOI: 10.1016/0304-4076(87)90084-4
author: Thomas F. Cooley
author: William R. Parke
published in: Journal of Econometrics
publication date: 1987-01-01T00:00:00Z
review text: We consider the problem of generating multi-period predictions from two simple dynamic models, an autoregressive model and a geometric random walk. The autoregressive model constitutes a useful paradigm for many of the practical problems of prediction because it possesses a number of features that differentiate it sharply from the standard linear regression model. The geometric random walk model is widely used in macroeconomics and finance and is fundamentally non-normal.
Mathematics Subject Classification ID: 62P20
Mathematics Subject Classification ID: 62M20
zbMATH DE Number: 4060606
zbMATH Keywords: prediction functions
zbMATH Keywords: approximations
zbMATH Keywords: likelihood prediction function
zbMATH Keywords: mean-squared error prediction functions
zbMATH Keywords: Monte-Carlo study
zbMATH Keywords: conditional predictions
zbMATH Keywords: generating multi-period predictions
zbMATH Keywords: dynamic models
zbMATH Keywords: autoregressive model
zbMATH Keywords: geometric random walk model
zbMATH Keywords: macroeconomics
zbMATH Keywords: finance
zbMATH Keywords: non-normal
MaRDI profile type: Publication
full work available at URL: 0304-4076(87)90084-4
OpenAlex ID: W2106144979
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description: scientific article; zbMATH DE number 4060606