Earticle

This study evaluates the predictive performance of an extended Dynamic Factor Model (DFM) that incorporates port cargo throughput to reduce short-term information gaps caused by the publication lag of the Index of All Industry Production. Since the Index of All Industry Production is reported at a monthly frequency, it does not require the mixed-frequency aggregation or tent-loading restrictions typically used in quarterly GDP nowcasting. However, the ragged-edge problem remains because the release timing differs across input variables. To address this issue, this study applies a state-space DFM estimated using the Kalman filter and the Expectation-Maximization (EM) algorithm. The empirical results show that the Port-augmented DFM slightly reduces both RMSE and MAE compared with the benchmark DFM. The improvement is relatively more pronounced during the potential logistics shock period in 2021. However, since the overall improvement is limited and the Diebold-Mariano test is not conducted, the findings should be interpreted as exploratory evidence suggesting the complementary informational value of port cargo throughput.