Testing for residual correlation of any order in the autoregressive process

Proïa, Frédéric

doi:10.1080/03610926.2017.1310240

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…Perform a Durbin-Watson (D-W) test on the model to test the first-order autocorrelation of the model and construct statistics [35,36]:…”

Section: Model Accuracy Testmentioning

confidence: 99%

EMD-GM-ARMA Model for Mining Safety Production Situation Prediction

et al. 2020

Complexity

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In order to improve the prediction accuracy of mining safety production situation and remove the difficulty of model selection for nonstationary time series, a grey (GM) autoregressive moving average (ARMA) model based on the empirical mode decomposition (EMD) is proposed. First of all, according to the nonstationary characteristics of the mining safety accident time series, nonstationary original time series were decomposed into high- and low-frequency signals using the EMD algorithm, which represents the overall trend and random disturbances, respectively. Subsequently, the GM model was used to predict high-frequency signal sequence, while the ARMA model was used to predict low-frequency signal sequence. Finally, aiming to predict the mining safety production situation, the EMD-GM-ARMA model was constructed via superimposing the prediction results of each subsequence, thereby compared to the ARIMA model, wavelet neural network model, and PSO-SVM model. The results demonstrated that the EMD-GM-ARMA model and the PSO-SVM model hold the highest prediction accuracy in the short-term prediction, and the wavelet neural network has the lowest prediction accuracy. The PSO-SVM model’s prediction accuracy decreases in medium- and long-term predictions while the EMD-GM-ARMA model still can maintain high prediction accuracy. Moreover, the relative error fluctuations of the EMD-GM-ARMA model are relatively stable in both short-term and medium-term predictions. This shows that the EMD-GM-ARMA model can provide high-precision predictions with high stability, proving the model to be feasible and effective in predicting the mining safety production situation.

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“…Perform a Durbin-Watson (D-W) test on the model to test the first-order autocorrelation of the model and construct statistics [35,36]:…”

Section: Model Accuracy Testmentioning

confidence: 99%

EMD-GM-ARMA Model for Mining Safety Production Situation Prediction

et al. 2020

Complexity

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Interest Rate Persistence and Monetary Policy Rule in Light of Model Uncertainty

Yin

Lin

Chang

2023

German Economic Review

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We study how model uncertainty affects the understanding of the interest rate persistence using a generalized Taylor-rule function covering numerous submodels via model average approach. The data-driven weights can be regarded as a measure of power-sharing across monetary policy committee members. We show that the model uncertainty is important in Canada, France, and Sweden, and the implied weights indicate that the U.K. and the U.S. have a lower model uncertainty caused either by an over-influential chairman or the consistent agreement of committee members. The importance of model uncertainty can be emphasized by sequential estimation during the 2008 financial crisis.

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