Some weighted mixed portmanteau tests for diagnostic checking in linear time series models
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Publication:4960736
DOI10.1080/00949655.2018.1498094OpenAlexW2884968182WikidataQ129559370 ScholiaQ129559370MaRDI QIDQ4960736
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Publication date: 23 April 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949655.2018.1498094
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Cites Work
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- Improved Peňa-Rodriguez portmanteau test
- Consistency properties of least squares estimates of autoregressive parameters in ARMA models
- On the residual autocorrelation of the autoregressive conditional duration model
- Mixed portmanteau test for diagnostic checking of time series models
- The log of the determinant of the autocorrelation matrix for testing goodness of fit in time series
- A mixed portmanteau test for ARMA-GARCH models by the quasi-maximum exponential likelihood estimation approach
- Improved multivariate portmanteau test
- ON WEIGHTED PORTMANTEAU TESTS FOR TIME-SERIES GOODNESS-OF-FIT
- On a measure of lack of fit in time series models
- Asymptotic Statistics
- ON THE SQUARED RESIDUAL AUTOCORRELATIONS IN NON-LINEAR TIME SERIES WITH CONDITIONAL HETEROSKEDASTICITY
- A proposal for a residual autocorrelation test in linear models
- Further results on the finite-sample distribution of Monti's portmanteau test for the adequacy of an ARMA (p,q) model
- A Powerful Portmanteau Test of Lack of Fit for Time Series
- Introduction to Time Series and Forecasting
- New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing
- Mixed Portmanteau Tests for Time‐Series Models
- Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models
- Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification
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