This page offers some interesting empirical studies. The data sets, program codes, and demonstrations are free to download. All analyses were carried out with R, Stata, Octave, Matlab, Eviews, Python, and Gretl software (R, Octave, Python, and Gretl are free). The Octave is a free software and runs on GNU/Linux, macOS, BSD, and Microsoft Windows. Its syntax is largely compatible with Matlab. Note that using a different version of software maybe lead to compatibility issues, please feel free to contact me if you have any problem in use.
Topics:
總體經濟實證:
1. 非動態追蹤資料模型之門檻效果: (1) Data, (2) Stata code, (3) Slides
2. 台灣通貨膨脹預測之向量自我迴歸 (VAR) 模型應用: (1) Data, (2) Python_code, (3) Slides
3. 台灣具前瞻性泰勒法則之非線性最小平方法: (1) Data, (2) Gretl code, (3) Results
4. 估計台灣動態歐肯係數: (1) Data, (2) Eviews code, (3) Slides
5. 估計台灣動態犧牲比率 : (1) Data, (2) Eviews code, (3) Slides
1. 非動態追蹤資料模型之門檻效果: (1) Data, (2) Stata code, (3) Slides
2. 台灣通貨膨脹預測之向量自我迴歸 (VAR) 模型應用: (1) Data, (2) Python_code, (3) Slides
3. 台灣具前瞻性泰勒法則之非線性最小平方法: (1) Data, (2) Gretl code, (3) Results
4. 估計台灣動態歐肯係數: (1) Data, (2) Eviews code, (3) Slides
5. 估計台灣動態犧牲比率 : (1) Data, (2) Eviews code, (3) Slides
財務實證:
1. A Conditional Autoregressive Range (CARR) Model and A Generalized Autoregressive Conditional Heteroskedasticity
(GARCH) Model—Evidence from Taiwan index Market: (1) Data, (2) Eviews code, (3) Results, (4) Slides
2. A Multi-Horizon CARR (MHCARR) Model—Evidence from Taiwan Market: (1) Data, (2) Eviews code, (3) Results, (4) Slides
3. An Asymmetric CARR (ACARR) Model—Evidence from Taiwan Market: (1) Data, (2) R code, (3) Results, (4) Slides
4. A Heterogeneous Autoregressive of the High-Low Range (Range-HAR) Model—Evidence from Taiwan Market: (1) Data,
(2) Eviews code, (3) Results, (4) Slides
5. An Autoregressive Fractionally Integrated Moving Average of the Log-Range (ARFIMA) model—Evidence from Taiwan
Market: (1) Data, (2) Eviews code, (3) Results, (4) Slides
6. An Autoregressive Conditional Interval (ACI) Model—Evidence from Taiwan Market: (1) Data, (2) R code, (3) Results,
(4) Slides (ref. He et al., 2021, Econometric Reviews)
7. Value at Risk (VaR): (1) Data, (2) Eviews code, (3) Results, (4) Handout
1. A Conditional Autoregressive Range (CARR) Model and A Generalized Autoregressive Conditional Heteroskedasticity
(GARCH) Model—Evidence from Taiwan index Market: (1) Data, (2) Eviews code, (3) Results, (4) Slides
2. A Multi-Horizon CARR (MHCARR) Model—Evidence from Taiwan Market: (1) Data, (2) Eviews code, (3) Results, (4) Slides
3. An Asymmetric CARR (ACARR) Model—Evidence from Taiwan Market: (1) Data, (2) R code, (3) Results, (4) Slides
4. A Heterogeneous Autoregressive of the High-Low Range (Range-HAR) Model—Evidence from Taiwan Market: (1) Data,
(2) Eviews code, (3) Results, (4) Slides
5. An Autoregressive Fractionally Integrated Moving Average of the Log-Range (ARFIMA) model—Evidence from Taiwan
Market: (1) Data, (2) Eviews code, (3) Results, (4) Slides
6. An Autoregressive Conditional Interval (ACI) Model—Evidence from Taiwan Market: (1) Data, (2) R code, (3) Results,
(4) Slides (ref. He et al., 2021, Econometric Reviews)
7. Value at Risk (VaR): (1) Data, (2) Eviews code, (3) Results, (4) Handout
機器學習與深度學習:
1. 機器學習-線性迴歸 (Linear Regression, LR): (1) Python (2) Slides
2. 機器學習-羅吉斯迴歸 (Logistic Regression, LR): (1) Data, (2) Results
3. 機器學習-脊回歸 (Ridge Regression, RR ): (1) Data (2) Results
4. 機器學習-K最近鄰演算法 (K-Nearest Neighbors, KNN): (1) Data, (2) Results
5. 機器學習-支持向量機 (Support Vector Machine, SVM): (1) Data (2) Results
6. 機器學習-支持向量迴歸 (Support Vector Regression, SVR): (1) Results
7. 機器學習-多輸出支持向量迴歸 (Multi-Output Support Vector Regression, M-SVR): (1) Data, (2) Results
8. 機器學習-決策樹 (Decision Tree, DT): (1) Data, (2) Results
9. 機器學習-隨機森林 (Random Forest, RF): (1) Data, (2) Results
10. 機器學習-自適應提升脊迴歸 (AdaBoost Ridge Regression, ABRR): (1) Data, (2) Results
11. 機器學習-線性判别法 (Linear Discriminant Analysis, LDA): (1) Data, (2) Results
12. 機器學習-二次判別分析 (Quadratic Discriminant Analysis, QDA): (1) Data, (2) Results
13. 機器學習-關聯式規則 (Association Rule, AR): (1) Data, (2) Results
14. 深度學習-長短期記憶模型 (Long Short-Term Memory, LSTM): (1) Data, (2) Results
15. 深度學習-循環神經網路模型 (Gate Recurrent Unit, GRU): (1) Data, (2) Results
1. 機器學習-線性迴歸 (Linear Regression, LR): (1) Python (2) Slides
2. 機器學習-羅吉斯迴歸 (Logistic Regression, LR): (1) Data, (2) Results
3. 機器學習-脊回歸 (Ridge Regression, RR ): (1) Data (2) Results
4. 機器學習-K最近鄰演算法 (K-Nearest Neighbors, KNN): (1) Data, (2) Results
5. 機器學習-支持向量機 (Support Vector Machine, SVM): (1) Data (2) Results
6. 機器學習-支持向量迴歸 (Support Vector Regression, SVR): (1) Results
7. 機器學習-多輸出支持向量迴歸 (Multi-Output Support Vector Regression, M-SVR): (1) Data, (2) Results
8. 機器學習-決策樹 (Decision Tree, DT): (1) Data, (2) Results
9. 機器學習-隨機森林 (Random Forest, RF): (1) Data, (2) Results
10. 機器學習-自適應提升脊迴歸 (AdaBoost Ridge Regression, ABRR): (1) Data, (2) Results
11. 機器學習-線性判别法 (Linear Discriminant Analysis, LDA): (1) Data, (2) Results
12. 機器學習-二次判別分析 (Quadratic Discriminant Analysis, QDA): (1) Data, (2) Results
13. 機器學習-關聯式規則 (Association Rule, AR): (1) Data, (2) Results
14. 深度學習-長短期記憶模型 (Long Short-Term Memory, LSTM): (1) Data, (2) Results
15. 深度學習-循環神經網路模型 (Gate Recurrent Unit, GRU): (1) Data, (2) Results
貝氏定理:
1. 貝氏定理
2. Gibbs sampling (sampler) with Merton's jump diffusion model: (1) Data, (2) R code, (3) Results, (4) Slides
3. Gibbs sampling (sampler) with the term structure of interest rate: (1) Data, (2) R code, (3) Results, (4) Slides
1. 貝氏定理
2. Gibbs sampling (sampler) with Merton's jump diffusion model: (1) Data, (2) R code, (3) Results, (4) Slides
3. Gibbs sampling (sampler) with the term structure of interest rate: (1) Data, (2) R code, (3) Results, (4) Slides