• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.239-254
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• 2021

This paper studies time series analysis with estimation and forecasting for Korean COVID-19 confirmed cases, based on the approach of a heterogeneous autoregressive (HAR) model with two-piece t (TP-T) distributed errors. We consider HAR-TP-T time series models and suggest a step-by-step method to estimate HAR coefficients as well as TP-T distribution parameters. In our proposed step-by-step estimation, the ordinary least squares method is utilized to estimate the HAR coefficients while the maximum likelihood estimation (MLE) method is adopted to estimate the TP-T error parameters. A simulation study on the step-by-step method is conducted and it shows a good performance. For the empirical analysis on the Korean COVID-19 confirmed cases, estimates in the HAR-TP-T models of order p = 2, 3, 4 are computed along with a couple of selected lags, which include the optimal lags chosen by minimizing the mean squares errors of the models. The estimation results by our proposed method and the solely MLE are compared with some criteria rules. Our proposed step-by-step method outperforms the MLE in two aspects: mean squares error of the HAR model and mean squares difference between the TP-T residuals and their densities. Moreover, forecasting for the Korean COVID-19 confirmed cases is discussed with the optimally selected HAR-TP-T model. Mean absolute percentage error of one-step ahead out-of-sample forecasts is evaluated as 0.0953% in the proposed model. We conclude that our proposed HAR-TP-T time series model with optimally selected lags and its step-by-step estimation provide an accurate forecasting performance for the Korean COVID-19 confirmed cases.

• Journal of Korea Port Economic Association
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• v.40 no.1
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• pp.119-131
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• 2024

This study examines the interrelationship between coastal passenger demand and fares for 101 coastal passenger routes in Korea during the 2018 to 2022 period. The two-stage least squares method through a panel data simultaneous equations model was estimated to the effects of individual route characteristics and regional characteristics on the performance and fares of coastal passenger transportation. The estimated results indicate that the endogenous variable, fare, and the exogenous variables, route characteristics, route distance, and the instrumental variable, frequency, affect the demand for coastal passengers. In the short-run pricing function, the exogenous variables, capacity, speed, and route distance, as well as the endogenous variable, coastal passenger transportation performance, affect the coastal passenger fare. This study is expected to provide useful implications for domestic coastal passenger demand and pricing in relation to coastal passengers.