Properties of Predictors in Overdifferenced Nearly Nonstationary Autoregression

Ismael Sanchez and Daniel Peña

Journal of Time Series Analysis, 2001, vol. 22, issue 1, 45-66

Abstract: We analyze the effect of overdifferencing a stationary AR(p+1) process whose largest root is near unity. It is found that, if the process is nearly nonstationary, the estimators of the overdifferenced model ARIMA(p,1,0) are root‐T consistent. It is also found that this misspecified ARIMA(p,1,0) has lower predictive mean squared error, to terms of small order, than the properly specified AR(p+1) model due to its parsimony. The advantage of the overdifferenced predictor depends on the remaining roots, the prediction horizon and the mean of the process.

Date: 2001
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https://doi.org/10.1111/1467-9892.00211

Related works:
Working Paper: PROPERTIES OF PREDICTORS IN OVERDIFFERENCED NEARLY NONSTATIONARY AUTOREGRESSION (1999)
Working Paper: Properties of predictors in overdifferenced nearly nonstationary autoregression (1995)
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:22:y:2001:i:1:p:45-66

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