Using the Residual White Noise Autoregressive Order Determination Criterion to Identify Unit Roots in Arima Models
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Publication:4490158
DOI10.1080/03610910008813613zbMath0968.62539OpenAlexW2133221379MaRDI QIDQ4490158
Sergio G. Koreisha, Tarmo M. Pukkila
Publication date: 10 July 2000
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610910008813613
identificationtime seriesunit rootsARIMA modelsdifferencingresidual white noise autoregressive criterion
Cites Work
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- New approaches for determining the degree of differencing necessary to induce stationarity in ARIMA models
- The estimation of the order of an ARMA process
- Deciding between I(1) and I(0)
- Consistent Estimates of Autoregressive Parameters and Extended Sample Autocorrelation Function for Stationary and Nonstationary ARMA Models
- GENERALIZED LEAST SQUARES ESTIMATION OF ARMA MODELS
- A method for autoregressive-moving average estimation
- Testing for unit roots in autoregressive-moving average models of unknown order
- Hypothesis Testing in ARIMA(p, 1, q) Models
- Testing for a unit root in time series regression
- Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root
- Modeling Multiple Times Series with Applications
- Consistent Recursive Estimation of the Order of an Autoregressive Moving Average Process
- On a measure of lack of fit in time series models
- Time Series Regression with a Unit Root
- ON THE RELATIONSHIP BETWEEN GENERALIZED LEAST SQUARES AND GAUSSIAN ESTIMATION OF VECTOR ARMA MODELS
- Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models
