Publication

Linear Autoregression for Prediction: Small Sample Inference

1993
Reports

Abstract

This paper describes inferences based on linear predictors for stationary time series. These methods are flexible, since relatively few assumptions are needed to fit a linear predictor. A confidence interval for the resulting predicted value, which takes account of the variance of the estimated parameters, is discussed. The possible non-parsimony of the linear prediction compared to the classical ARMA forecasting method is a drawback often mentioned in the literature. On the other hand, as we show in a small simulation study, the usual predictive inference based on an ARMA modelling is overoptimistic in small samples, whereas the coverage rate of our confidence interval is close to the nominal value even for small series.

Official source

https://infoscience.epfl.ch/entities/publication/51e476c2-f5e5-4f09-8a3c-b1ef34e10ef1

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1993
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/51e476c2-f5e5-4f09-8a3c-b1ef34e10ef1

About this result

Related concepts (32)

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Generalized linear model

In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value. Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression.

Prediction

A prediction (Latin præ-, "before," and dicere, "to say"), or forecast, is a statement about a future event or data. They are often, but not always, based upon experience or knowledge. There is no universal agreement about the exact difference from "estimation"; different authors and disciplines ascribe different connotations. Future events are necessarily uncertain, so guaranteed accurate information about the future is impossible. Prediction can be useful to assist in making plans about possible developments.

Related publications (20)

Linear Autoregression for Prediction: Small Sample Inference

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