請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72920完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 楊曙榮(Sunny Yang) | |
| dc.contributor.author | Scott Schwartz | en |
| dc.contributor.author | 天空 | zh_TW |
| dc.date.accessioned | 2021-06-17T07:10:23Z | - |
| dc.date.available | 2019-07-31 | |
| dc.date.copyright | 2019-07-31 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-07-22 | |
| dc.identifier.citation | Altug, S. G. (2009). Business Cycles: Fact, Fallacy and Fantasy. World Scientific Pub Co Inc.
Benoit, S., & Raffinot, T. (2018). Investing Through Economic Cycles with Ensemble Machine Learning. Paris. Berge, T. (2014). Predicting Recessions with Leading Indicators: Model Averaging and Selection Over Business Cycles. Federal Reserve Bank of Kansas City. Berge, T. J., & Jorda, O. (2010). Evaluating the Classification of Economic Expansion into Recessions and Expansions. Davis: National Bureau of Economic Research . Burns, A. F., & Mitchell, W. C. (1946). Measuring Business Cycles. Naitonal Bureau of Economic Research. Carter, S., & Cox, A. (2011, September 8). One 9/11 Tally. Retrieved from The New York Times: https://archive.nytimes.com/www.nytimes.com/interactive/2011/09/08/us/sept-11-reckoning/cost-graphic.html?hp Estrella, A., & Mishkin, F. (1996). The Yield Curve as a Predictor of U.S Recessions. Federal Reserve Bank ofa New York. Ferrara, J. A. (2004). Detecting Cyclical Turning Points: The ABCD Approach. Journal of Business Cycle Measurement and Analysis – Vol. 1. Frumkin, N. (2015). Recession Prevention Handbook. New York: Routledge. Garbellano, J. (2016). Nowcasting Recessions with Machine Learning - New Tools for Predicting the Business Cycle. Hamilton, J. D. (2010). Calling Recessions in Real Time. National Bureau of Economics Research. Heilemann, U., & Weihs, C. (2007). Classification and Clustering in Business Cycle Analysis. Berlin: Duncker and Humblot. Ineichen, A. (2015). Nowcasting: A Risk Management Tool. Chartered Alternative Investment Analysis (CAIA). Liu, W., & Moench, E. (2014). What Predicts U.S. Recessions? Federal Reserve Bank of New York Staff Reports. Mintz, I. (1974). Dating United States Growth Cycles. In NBER, Explorations in Economics Research, Volume I, Number I (pp. 1-113). National Bureau of Economics Research . Mitchell, T. (1997). Machine Learning. McGraw-Hill . Ng, S. (2013). Boosting Recessions. New York: Columbia University. Ng, S., & Wright, J. H. (2013). Facts and Challenges From the Great Recession for Forecasting and Macroeconomic Modeling. Boston: National Bureau of Economics Research. Papadimitriou, T., Gogas, P., Matthaiou, M., & Chrysanthidou, E. (2007). Yield Curve and Recession Forecasting in a Machine Learning Framework . Rimini Centre for Economic Analysis. Raffinot, T. (2017). Asset Allocation, Economic Cycles and Machine Learning. PSL Research University. Silvia, J. E., & Iqbal, A. (2018). Can Machine Learning Improve Recession Predictions? Wells Fargo Security Economics Group. Stengel, D. N., & Chaffe-Stengel, P. (2011). Working with Economic Indicators: Interpretations and Sources. New York City: Business Expert Press. Stock, J. H., & Watson, M. W. (1989). New Indexes of Coincident and Leading Economic Indicators. NBER Macroenomics Annual, 351-409. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72920 | - |
| dc.description.abstract | The term “recession”is not only a sensitive topic to the workers, investors, and businesses that suffer immense loss during this time, but also economists whom continually struggle to predict them. Through the rise of Big Data, Machine Learning potentially gives statisticians and economists alike a new tool for predicting recessions. In order to explore this field, this paper asks two fundamental questions:
• Does machine learning help classify and forecast recessions within the business cycle? • Which models are most effective in predicting and classifying recessions? Applying the most common machine learning classification algorithms, we perform out-of-sample performance evaluations using a self-selected sample of macroeconomic indicators. Our findings imply that Random Forest, KNN, and Support Vector Machine models best classify and predict recessions. The analysis results suggest that machine learning has incredible potential in improving prediction accuracy. However, we believe these models can be further developed through additional research and application within the Deep Learning field of machine learning. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T07:10:23Z (GMT). No. of bitstreams: 1 ntu-108-R06749065-1.pdf: 3941329 bytes, checksum: 38e81fdb484496ecd632afa6362a2839 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | Abstract i
Acknowledgements ii Contents iii List of Tables v List of Figures vi 1. Introduction 1 2. Background 3 2.1. Seasonality and Cyclicity 3 2.2. GDP and the Economic Cycle 4 2.3. Potential Causes of the Business Cycle 7 2.4. Business Cycle Theory 9 2.5. Indicator Variables 11 2.6. Development of Business Cycle Modern Theory 12 2.7. Challenges in Forecasting Model Development 14 3. Machine Learning 17 3.1. What is Machine Learning? 17 3.2. Why Machine Learning? 19 3.3. Types of Machine Learning 19 3.4. Machine Learning in Economics 21 3.5. Machine Learning Models 21 3.5.1. Logistic Regression 22 3.5.2. Naïve Bayes 23 3.5.3. Support Vector Machine (SVM) 26 3.5.3.1. The Kernal Trick 27 3.5.4. K Nearest Neighbors (KNN) 28 3.5.5. Decision Trees 29 3.6. Ensemble Learning 31 3.6.1. Random Forest 31 3.7. Evaluating Machine Learning Models 32 3.7.1. Evaluation Techniques 32 3.7.1.1. Holdout 32 3.7.1.2. Cross-Validation 33 3.7.2. Evaluation Metrics 33 3.7.2.1. Confusion Matrix 35 3.7.2.2. F1 Score 36 3.7.2.3. ROC-AUC Score 37 4. Data Description 38 4.1. Variable Selection 38 4.2. Data Resource and Cleaning 40 5. Methodology and Results 42 5.1. Comparing Classification Models 42 5.1.1. Random Forest Classification 48 5.1.2. Logistic Regression Classification 50 5.2. Adding Features 51 5.3. The Question of Time 56 5.3.1. Linear Weighted Moving Average 57 5.3.2. Exponential Weighted Moving Average 58 5.3.3. Predictions 60 5.4. Future Forecasts 62 6. Conclusion and Future Areas of Research 64 References 66 Appendix 68 Appendix A: Significance Test Result 68 Appendix B: Weighted Moving Average Model Results 72 Appendix C: R Coding 74 C.1 Data Processing 74 C.2 Random Forest Model and Significance Test 76 C.3 Logistic Regression and Significance Test 78 C.4 KNN Model 80 C.5 SVM Model 81 C.6 Naïve Bayes Model 82 C.7 Evaluation Metrics 83 C.8 Graphs and Visuals 84 Appendix D: Future Forecasts 90 | |
| dc.language.iso | en | |
| dc.subject | 景氣衰退 | zh_TW |
| dc.subject | 商業週期 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 分類 | zh_TW |
| dc.subject | 預測 | zh_TW |
| dc.subject | Classification | en |
| dc.subject | Forecasting | en |
| dc.subject | Recession | en |
| dc.subject | Business Cycle | en |
| dc.subject | Prediction | en |
| dc.subject | Machine Learning | en |
| dc.title | 機器學習在商業週期的分類與預測之應用 | zh_TW |
| dc.title | Applications of Machine Learning in Classifying and Forecasting Business Cycles | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 羅明琇(Sonia Lo),陳立民(Li-Ming Chen) | |
| dc.subject.keyword | 機器學習,商業週期,景氣衰退,預測,分類, | zh_TW |
| dc.subject.keyword | Machine Learning,Forecasting,Recession,Business Cycle,Prediction,Classification, | en |
| dc.relation.page | 97 | |
| dc.identifier.doi | 10.6342/NTU201901681 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-07-22 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 企業管理碩士專班 | zh_TW |
| 顯示於系所單位： | 管理學院企業管理專班(Global MBA) | |
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|---|---|---|---|
| ntu-108-1.pdf 未授權公開取用 | 3.85 MB | Adobe PDF |
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