## 4 Discussion  
In this study, we aimed to demonstrate that the threshold effects of the TAR model may be used for better livestock price predictions when epidemics such as ASF or AI occur since reduced supply and decreased consumption of livestock products can cause livestock prices to be volatile. TAR models have been influential in agricultural economics, including applications to pork price [18], and there have been a few reports of the use of threshold models to explain the nonlinear structure of livestock prices caused by infectious diseases in pigs and broilers [3,19]. For example, the TAR model was employed to divide the nonlinear dynamics of pork prices in China into two regimes [20,21]. There are many variables that can affect the livestock products, such as the numbers of pigs bred and slaughtered, consumption of livestock products, feed prices, substitute prices, and weather. These variables may be included only exogenously in a time-series TAR model. Exogeneous variables in time series models may cause selection bias, which can reduce its prediction ability [22]. Therefore, we focused on the threshold effects and levels that are determined within the model (endogenously) by omitting exogenous variables. This study analyzed whether threshold effects existed in the retail prices of livestock products by considering news and SNS frequency which can influence and represent consumer sentiment. Our findings suggest that when an infectious disease like ASF occurs for the first time, the threshold effects of the TAR model are observed and the price of pork shows a nonlinear relationship that can be divided into three regimes based on ASF-related news frequency, which indicates that the effect of disease-related news frequency on retail pork prices is significant. The impact response results showed that when ASF occurred for the first time on the Korean peninsula in 2019, the impact lasted for a long time in regime 1. Figure 4: Estimation of the impact response period of retail chicken prices according to the Avian Influenza (AI)-related news frequency. The \(x\) axis indicates period (day) and the \(y\) axis indicates retail price responded to impulse. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Price** & **Model** & **Root Mean Square Error (RMSE)** \\ \hline \multirow{2}{*}{Retail pork price} & Autoregressive & 267.002 \\ \cline{2-3}  & Threshold autoregressive & 261.639 \\ \cline{2-3}  & Autoregressive & 205.058 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the Prediction Accuracy of the Threshold Autoregressive (TAR) and Autoregressive (AR) Models. However, after AI occurred multiple times, our findings also suggested that retail chicken prices had a linear relationship but no threshold effects, which indicated a reduced effect of AI news frequency on price. In the event of infectious disease outbreaks in livestock, the impacts on livestock prices and response periods were different depending on the regimes. Based on these findings, applying different policies to stabilize prices of livestock products depending the severity of the impact is necessary. Recent reports have used unstructured data such as broadcast news, TV programs and social networks to predict pork demands or pork prices [12; 23]. We proposed the use of SNS to promote pork consumption using ASF-related SNS frequency and its relationship to retail pork prices. Our findings suggest that when there was an increase in an ASF-related SNS frequency differences, the retail price of pork price rose by as much as 1.1666 Korean Won (KRW) in regime 1. Our findings indicate that more than 247 SNS posts are needed to encourage pork consumption and change the retail pork price by as much as one SD, which amounts to 288.2169 KRW. To the best of our knowledge, this study is the first to suggest the use of SNS to promote livestock consumption after it has collapsed because of an epidemic.