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題名:台灣芒果批發價格預測模型之比較研究
作者:林銘昌 引用關係
作者(外文):Lin, Ming-Chang
校院名稱:國立臺灣大學
系所名稱:農業經濟學研究所
指導教授:蕭清仁
學位類別:博士
出版日期:2002
主題關鍵詞:芒果價格預測組合預測MangoPrice ForecastingCombining Forecasts
原始連結:連回原系統網址new window
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本研究主要在比較台灣芒果批發價格各種預測模型,並找出一較佳之預測模型,以提高預測的精確度。文中分別採用計量模型、時間數列、調適加權組合預測方法以及3種傳統迴歸式組合預測方法。有別於文獻中的組合預測方法,本研究另外提出加入季節誤差調整項之調適加權,以及3種季節迴歸式組合預測方法,分別進行台灣芒果批發價格預測。
本文以1993年至2001年,台北果菜運銷公司芒果及其重要競爭水果之批發價格與交易量資料,進行芒果批發價格預測實證分析。將上述10種預測模型實證結果以殘差均方根(RMS:Root mean square error )、殘差百分比均方根(RMSPE:Root mean square percentage error)、平均絕對誤差百分比(MAPE:Mean absolute percentage error)以及Theil U不等係數等統計指標進行預測值評估。結果顯示,本研究所提出加入季節調整項之迴歸式組合方法,比單一模型及其他傳統的組合方法,在所有統計評估指標上顯示其能提升預測效率,並能更進一步提升預測準確度,且在樣本期間內的模擬,以及樣本期間外的預測表現也相對穩定。
The purpose of this study is mainly to compare the several forecasting models of the wholesale price for mangos in Taiwan in order to increase the precision of the forecast. The combining forecasts use econometric model, time series, and adaptive weighting respectively in the content, and another combining forecasts consist of three traditional regression models. Other than the stated combining forecasts in the records, this study also suggests to add adaptive weighting from the seasonal inaccuracy adjustment and three other seasonal regression models of combining forecasts to proceed with the wholesale price forecasting of mangos in Taiwan.
Covered from 1993 to 2001, this article utilizes the information of the wholesale prices and the trading volumes of mangoes and other important competitive fruits of Taipei Fruits and Vegetables Transportation and Sale Corporation to progress the mango wholesale price forecasting demonstration analysis. Taking statistics indexes such as the root mean square error (RMS), the root mean square percentage error (RMSPE), the mean absolute percentage error (MAPE), and the Theil’s inequality coefficient, etc. of the ten forecasting demonstration results in order to determine the evaluation of the forecasting value. The end result shows that the addressed added regression model from the seasonal adjustment in this study is capable of mounting the forecast efficiency among all statistics evaluation indexes compared to a unitary model and other traditional combining methods. Furthermore, it can step forward to increase the forecast precision, and appear to be quite stable in the simulation within the sampling period and in the forecasting performance outside of the sampling period.
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