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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳宜廷 | zh_TW |
| dc.contributor.advisor | Yi-Ting Chen | en |
| dc.contributor.author | 邱祥鴻 | zh_TW |
| dc.contributor.author | Hsiang-Hung Chiu | en |
| dc.date.accessioned | 2023-08-16T17:13:41Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-16 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-08 | - |
| dc.identifier.citation | Belloni, A., Chen, D., Chernozhukov, V., and Hansen, C. (2012). Sparse models and methods for optimal instruments with an application to eminent domain, Econometrica, 80(6), 2369--2429.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89123 | - |
| dc.description.abstract | 在各個研究中，測量因果關係是非常重要的。為了維持解釋變數的外生性，研究者可能需要考慮高維度的控制變數。在此架構下，傳統所使用的最小平方估計法並不適用。為了應對這些問題，Belloni等人（2014）提出了後雙重選擇（Post-Double Selection，PDS）方法，此方法在計量文獻中受到了的重視。雖然Belloni等人(2014)已經證明了PDS方法具有漸近常態性，但在實證使用上，研究者們需要理解PDS在有限樣本中的表現。本文探討在PDS分析中，不同的統計學習方法進行雙重選擇所得出的有限樣本性質，並且藉此重新檢驗技術指標在實證中的顯著性。 | zh_TW |
| dc.description.abstract | Measuring causal effects is crucial in various research. Understanding the impact of an explanatory variable on an outcome variable is essential for evaluating the effectiveness of policy changes or interventions. However, to maintain the exogeneity of explanatory variables, researchers may need to consider high-dimensional control variables. In this framework, the traditional least squares method is inapplicable. To address this problem, Belloni et al. (2014) proposed the post-double selection (PDS) method. This method has received a lot of attention in econometrics. Although Belloni et al. (2014) have proved that the PDS estimator is asymptotically normal under suitable conditions, it is still important to evaluate how the PDS method behaves in finite samples. This study explores the finite-sample performance of the PDS estimator under different choices of statistical learning methods for the double selection. I also apply the PDS method to assess the significance of technical indicators in explaining stock returns. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-16T17:13:41Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-16T17:13:41Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
Acknowledgements ii 摘要 iii Abstract iv Contents v List of Figures vii List of Tables viii Denotation ix Chapter 1 Introduction 1 Chapter 2 Econometric Methods 5 2.1 The PDS method 5 2.2 Statistical learning methods 7 2.2.1 Lasso 7 2.2.2 SCAD 9 2.2.3 MCP 9 2.2.4 Adaptive Lasso 10 2.3 Choices of tuning parameter 11 2.3.1 The ten-fold CV method 11 2.3.2 A BIC method 11 Chapter 3 Simulation 13 3.1 Simulation designs 14 3.2 Simulation results 15 3.2.1 Performance of the PDS estimator 16 3.2.2 Performance of variable selection 20 Chapter 4 Empirical Application 23 4.1 Technical indicators 24 4.2 Data 26 4.3 Predictive regression 27 4.4 Empirical findings 28 Chapter 5 Conclusions 34 References 36 | - |
| dc.language.iso | en | - |
| dc.subject | 因果推論 | zh_TW |
| dc.subject | 風險溢酬 | zh_TW |
| dc.subject | 統計學習方法 | zh_TW |
| dc.subject | 技術指標 | zh_TW |
| dc.subject | 模型選擇 | zh_TW |
| dc.subject | Statistical Learning | en |
| dc.subject | Risk Premium | en |
| dc.subject | Technical Indicators | en |
| dc.subject | Causal Inference | en |
| dc.subject | Model Selection | en |
| dc.title | 後雙重選擇估計式的有限樣本表現 | zh_TW |
| dc.title | On the Finite-Sample Performance of the Post-Double Selection Estimator | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 洪志清;羅秉政 | zh_TW |
| dc.contributor.oralexamcommittee | Chih-Ching Hung;Kendro Vincent | en |
| dc.subject.keyword | 因果推論,統計學習方法,風險溢酬,技術指標,模型選擇, | zh_TW |
| dc.subject.keyword | Causal Inference,Statistical Learning,Risk Premium,Technical Indicators,Model Selection, | en |
| dc.relation.page | 40 | - |
| dc.identifier.doi | 10.6342/NTU202301747 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-10 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 財務金融學系 | - |
| 顯示於系所單位： | 財務金融學系 | |
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