Consistent forecast intervals when the forecast-period exogenous variables are stochastic
Article Abstract:
Researchers may encounter difficulties in deriving prediction intervals when the values of the exogenous variables are uncertain, or when estimates are used in place of true values. This is also true even in the presence of a linear k-variable regression model, a normal dependent variable and jointly normal exogenous variables since the distribution of the forecast error remains non-normal. A bootstrap method shows promise as an alternative to the traditional asymptotic normal theory in the face of stochastic forecast-period exogenous variables.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 1996
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Bayesian modeling of ARFIMA processes by Markov chain Monte Carlo methods
Article Abstract:
The application of a sampling-based Bayesian approach using Markov chain Monte Carlo methods for autoregressive fractionally integrated moving average (ARFIMA) models results in a viable and more relatively more accurate solution than those created by other statistical methods. This is done by using a partial linear regression coefficients of the ARFIMA process to obtain the posterior distribution of the model parameters corresponding to the likelihood function.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 1996
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Outlier detection in regression models with ARIMA errors using robust estimates
Article Abstract:
A new outlier detection procedure is evaluated. The detection procedure works better when there are a large number of outliers in a regression model.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 2001
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