A Study on the Estimation of Ultrafine Dust (PM2.5) Prediction Model by Vector Error Correction (VECM)
Keywords:
Vector time series analysis, VECM, Ultrafine dust (PM2.5), Cointegration coefficient tests, Multivariate Portmanteau test Portmanteau testAbstract
This study presents an ultrafine dust (PM2.5) prediction model using a vector error correction model, and daily time series data of ultrafine dust (UFD), nitrogen dioxide (NO2), and carbon monoxide (CO) observed in Jung-gu, Seoul from January 1, 2017 to October 31, 2021. From the Granger causality test for prediction model estimation, it was found that the vector time series model can be applied, and the model was turned out as the VAR(2) model according to minimum information criterion. Using this, the prediction model was concluded as VECM(2), a model having intercept and no linear trend, as a result of performing three cointegration coefficient tests to select VECM(p). Therefore, the prediction model was presented by calculating the long-term parameter estimate, the error correction coefficient estimate, and the parameter estimate estimated by the model. And as a result of performing model diagnosis on the residual time series vector obtained after fitting the VECM(2) model, it was found that there was no cross-correlation until the lag 12, meaning that the VECM(2) prediction model in this study was a reliable model.
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