TY - CPAPER AB - Our study focuses on 3 vegetables mainly purchased in Korea; onion, garlic, and dried red pepper. We develop atypical index reflecting consumers’ attention on those vegetables from social network service (SNS) websites and major portal sites such as Google. Specifically, using text mining program, we gather associate web-search data, making simple query data measuring frequency on websites and Term Frequency – Inverse Document Frequency (TF-IDF) considering weights of core keywords on websites. We introduce those asymptotic indexes into the Bayesian structural time series models with climate factors impacting vegetable prices. Results show that the introduction of atypical web-search data can improve vegetable price prediction power compared to pure time-series models without atypical indexes. AU - Yoo, Do-il DA - 2016-05-26T04:50:03Z DA - 2016-05-26T04:50:03Z DO - 10.22004/ag.econ.236211 DO - doi ID - 236211 KW - Demand and Price Analysis KW - Research Methods/ Statistical Methods KW - Asymptotic data KW - Bayesian Structural Time Series Model KW - Price prediction L1 - https://ageconsearch.umn.edu/record/236211/files/_yoo__AAEA__2016_Vegetable_Price_Prediction_Atypical_Web_Search_Data.pdf L2 - https://ageconsearch.umn.edu/record/236211/files/_yoo__AAEA__2016_Vegetable_Price_Prediction_Atypical_Web_Search_Data.pdf L4 - https://ageconsearch.umn.edu/record/236211/files/_yoo__AAEA__2016_Vegetable_Price_Prediction_Atypical_Web_Search_Data.pdf LA - eng LA - English LK - https://ageconsearch.umn.edu/record/236211/files/_yoo__AAEA__2016_Vegetable_Price_Prediction_Atypical_Web_Search_Data.pdf N2 - Our study focuses on 3 vegetables mainly purchased in Korea; onion, garlic, and dried red pepper. We develop atypical index reflecting consumers’ attention on those vegetables from social network service (SNS) websites and major portal sites such as Google. Specifically, using text mining program, we gather associate web-search data, making simple query data measuring frequency on websites and Term Frequency – Inverse Document Frequency (TF-IDF) considering weights of core keywords on websites. We introduce those asymptotic indexes into the Bayesian structural time series models with climate factors impacting vegetable prices. Results show that the introduction of atypical web-search data can improve vegetable price prediction power compared to pure time-series models without atypical indexes. PY - 2016-05-26T04:50:03Z PY - 2016-05-26T04:50:03Z T1 - Vegetable Price Prediction Using Atypical Web-Search Data TI - Vegetable Price Prediction Using Atypical Web-Search Data UR - https://ageconsearch.umn.edu/record/236211/files/_yoo__AAEA__2016_Vegetable_Price_Prediction_Atypical_Web_Search_Data.pdf Y1 - 2016-05-26T04:50:03Z T2 - 9768 ER -