Improved autoregressive forecasts in the presence of non-normal errors

DOI10.1080/00949655.2014.945930zbMath1457.62268OpenAlexW2035223994MaRDI QIDQ5220925

Jing Li, Junsoo Lee

Publication date: 27 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2014.945930

zbMATH Keywords

forecastautoregressive modelnon-normalityleast absolute deviations estimatorresidual augmented least squares

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (3)

Bootstrap choice of non-nested autoregressive model with non-normal innovations ⋮ RALS-LM unit root test with trend breaks and non-normal errors: application to the Prebisch-Singer hypothesis ⋮ Testing for stationarity with covariates: more powerful tests with non-normal errors

Cites Work

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• More efficient estimation under non-normality when higher moments do not depend on the regressors, using residual augmented least squares
• Threshold models in non-linear time series analysis
• Redundancy of moment conditions
• Estimation Bias in the First-Order Autoregressive Model and Its Impact on Predictions and Prediction Intervals
• The Bias of Autoregressive Coefficient Estimators
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• NOTE ON BIAS IN THE ESTIMATION OF AUTOCORRELATION

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