本課程著重於實務應用導向的預測模型介紹，首先瞭解何謂預測的概念，爾後介紹當代的預測方法應用，最後進行實務案例的預測研究分析。上課除了各種計量預測方法的介紹外，也會透過計量程式的操作示範實際案例加速同學理解，最後會請同學完成一份實務預測應用的研究報告作為本課程的學習成果展現，並冀望學生日後可以應用到實務工作或是理財規劃等相關規劃。
本課程包含下列幾個模型與應用主題：Trend and Seasonality, ARIMA model, GARCH model, Relative Standards for Point Forecasts, Regression-Based Forecast Combination, Interval Forecast Combination, Point Forecasts From Forward Markets。This course focuses on the introduction of practical application-oriented forecasting models. First, understand the concept of forecasting, then introduce the application of contemporary forecasting methods, and finally conduct forecasting research and analysis of practical cases. In addition to the introduction of various measurement prediction methods, the class will also use practical cases to demonstrate the operation of measurement programs to accelerate students' understanding. Finally, students will be asked to complete a research report on practical prediction applications as the learning results of this course, and it is hoped that students can Apply it to practical work or financial planning and other related planning.
This course covers the following model and application topics: Trend and Seasonality, ARIMA model, GARCH model, Relative Standards for Point Forecasts, Regression-Based Forecast Combination, Interval Forecast Combination, Point Forecasts From Forward Markets.
1. Book: Econometric Data Science: A Predictive Modeling Approach, Francis X. Diebold, Edition 2019.
2. Forecasting in Economics, Business, Finance and Beyond, Francis X. Diebold, Edition 2017.
1. Book: Econometric Data Science: A Predictive Modeling Approach, Francis X. Diebold, Edition 2019.
2. Forecasting in Economics, Business, Finance and Beyond, Francis X. Diebold, Edition 2017.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 課堂參與(含出席率與作業) 課堂參與(含出席率與作業) Class participation (including attendance and homework) |
30 | |
| 期中考試 期中考試 midterm exam |
30 | |
| 期末考試期末考試 final exam |
40 |
