請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54541完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 管中閔(Chung-Ming Kuan) | |
| dc.contributor.author | Ying-Chin Chen | en |
| dc.contributor.author | 陳映君 | zh_TW |
| dc.date.accessioned | 2021-06-16T03:03:09Z | - |
| dc.date.available | 2015-08-11 | |
| dc.date.copyright | 2015-08-11 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2015-06-30 | |
| dc.identifier.citation | BAI, J., AND S. NG (2008): Large dimensional factor analysis. Now Publishers Inc.
BAIR, E., T. HASTIE, D. PAUL, AND R. TIBSHIRANI (2006): “Prediction by supervised principal components,” Journal of the American Statistical Association, 101(473), 119– 137. BARCLAY, D., C. HIGGINS, AND R. THOMPSON (1995): “The partial least squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration,” Technology studies, 2(2), 285–309. BATES, J., AND C. GRANGER (1969): “The Combination of Forecasts,” Journal of the Operational Research Society, 20(4), 451–468. CHAMBERLAIN, G., AND M. ROTHSCHILD (1983): “Arbitrage, factor structure, and mean-variance analysis on large asset markets,” Econometrica, 51(50), 1281–1304. CHAN, Y. L., J. H. STOCK, AND M. W. WATSON (1999): “A dynamic factor model framework for forecast combination,” Spanish Economic Review, 1(2), 91–121. CHIN, W. W. (1998): “The partial least squares approach to structural equation modeling,” Modern methods for business research, 295(2), 295–336. CHIN, W. W., AND T. FRYE (2003): “PLS Graph–Version 3.0,” Soft Modeling Inc. URL http://www. plsgraph. com. CONNOR, G., AND R. A. KORAJCZYK (1993): “A test for the number of factors in an approximate factor model,” The Journal of Finance, 48(4), 1263–1291. DE JONG, S., AND H. A. KIERS (1992): “Principal covariates regression: part I. Theory,” Chemometrics and Intelligent Laboratory Systems, 14(1), 155–164. DIEBOLD, F. X., AND R. S. MARIANO (2002): “Comparing Predictive Accuracy,” Journal of Business & Economic Statistics, 20(1), 134–44. EDMONDS, J. (1971): “Matroids and the greedy algorithm,” Mathematical programming, 1(1), 127–136. EFRON, B., T. HASTIE, I. JOHNSTONE, R. TIBSHIRANI, ET AL. (2004): “Least angle regression,” The Annals of statistics, 32(2), 407–499. EXTERKATE, P., D. V. DIJK, C. HEIJ, AND P. J. GROENEN (2013): “Forecasting the Yield Curve in a Data-Rich Environment Using the Factor-Augmented Nelson–Siegel Model,” Journal of Forecasting, 32(3), 193–214. FISHER, R. A. (1936): “The use of multiple measurements in taxonomic problems,” Annals of eugenics, 7(2), 179–188. FORNI, M., M. HALLIN, M. LIPPI, AND L. REICHLIN (2000): “The generalized dynamic-factor model: Identification and estimation,” Review of Economics and statistics, 82(4), 540–554. GRANGER, C. W., AND R. RAMANATHAN (1984): “Improved methods of combining forecasts,” Journal of Forecasting, 3(2), 197–204. HAALAND, D. M., AND E. V. THOMAS (1988): “Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information,” Analytical Chemistry, 60(11), 1193–1202. HEIJ, C., P. GROENEN, AND D. VAN DIJK (2005): “Forecast comparison of principal component regression and principal covariate regression,” Discussion paper, Erasmus University Rotterdam, Econometric Institute. HEIJ, C., P. J. GROENEN, AND D. VAN DIJK (2006): “Time series forecasting by principal covariate regression.,” Discussion paper, Econometric Institute Research Papers. HEIJ, C., D. VAN DIJK, AND P. J. GROENEN (2011): “Forecasting with Leading Indicators by means of the Principal Covariate Index,” OECD Journal: Journal of Business Cycle Measurement and Analysis, 2011(1), 73–92. HぴO SKULDSSON, A. (1988): “PLS regression methods,” Journal of chemometrics, 2(3), 211–228. HOTELLING, H. (1933): “Analysis of a complex of statistical variables into principal components.,” Journal of educational psychology, 24(6), 417. (1936): “Relations between two sets of variates,” Biometrika, 28(3-4), 321–377. HSU, S.-H., C.-M. KUAN, AND Y.-H. LO (2005): “Macroeconomic Forecasting Based on Diffusion Indexes,” Taiwan Economic Forecast and Policy, 36(1), 1–28. HUANG, H., AND T.-H. LEE (2010): “To combine forecasts or to combine information?,” Econometric Reviews, 29(5-6), 534–570. (2013): “Forecasting Value-at-Risk Using High-Frequency Information,” Econometrics, 1(1), 127–140. NOVEMBRE, J., AND M. STEPHENS (2008): “Interpreting principal component analyses of spatial population genetic variation,” Nature genetics, 40(5), 646–649. RORNI, M., AND L. REICHLIN (1996): “Dynamic common factors in large crosssections,” Empirical economics, 21(1), 27–42. ROSS, S. A. (1976): “The arbitrage theory of capital asset pricing,” Journal of economic theory, 13(3), 341–360. SARGENT, T. J., AND C. A. SIMS (1977): “Business cycle modeling without pretending to have too much a priori economic theory,” New methods in business cycle research, 1, 145–168. STOCK, J. H., AND M. W. WATSON (2002): “Forecasting using principal components from a large number of predictors,” Journal of the American statistical association, 97(460), 1167–1179. (2006): “Forecasting with many predictors,” Handbook of economic forecasting, 1, 515–554. TIBSHIRANI, R. (1996): “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society. Series B (Methodological), pp. 267–288. TU, Y., AND T.-H. LEE (2012): “Forecasting using supervised factor models,” Discussion paper, Working paper, University of California, Riverside. WOLD, H. (1973): “Nonlinear Iterative Partial Least Squares (NIPLAS) Modelling: Some Current Developments,” in Multivariate Analysis II, Proceedings of an International Symposium on Multivariate Analysis, ed. by P. Krishnaiah, pp. 383–407. Wright State University, Dayton, Ohio, June 19-24, 1972, Univ., Department of Statistics. WOLD, H. (1979): “Model construction and evaluation when theoretical knowledge is scarce: An example of the use of Partial Least Squares,” Gen`eve: Facult´e des Sciences E´conomiques et Sociales Universite´ de Gene`ve, pp. 1–63. ZOU, H., T. HASTIE, AND R. TIBSHIRANI (2006): “Sparse principal component analysis,” Journal of computational and graphical statistics, 15(2), 265–286. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54541 | - |
| dc.description.abstract | 本論文討論三種考慮應變數之因素模型(supervised factor model): 偏最小平方回歸模型(Partial Least Square,PLS)、主共變量回歸模型(Principal Covariate Regression,PCovR)及組合預測之主成分分析模型(Combining Forecasts Principal Component Analysi,CFPC)。我們將上述三種考慮應變數之因素模型應用在台灣經濟成長率之預測，並以均方根預測誤差(RMSFE)及平均絕對預測誤差(MAFE)衡量其預測之優劣。我們發現，考慮應變數之因素模型通常較不考慮應變數的因素模型(Principal Component Analysis, PCA)在預測上有更小的預測誤差，其中又以CFPC表現最好。另外，我們也比較考慮應變數之因素模型與主計總處對經濟成長之預測。我們發現CFPC之預測與主計總處之預測能力不相上下，因此我們認為CFPC是一個能夠避免模型錯誤設定的簡化模型(reduce form)。 | zh_TW |
| dc.description.abstract | This thesis discusses three supervised factor models: Partial Least Square (PLS), Principal Covariate Regression (PCovR) and Combining Forecasts Principal Component Analysis (CFPC). We apply the supervised and unsupervised factor models to forecast Taiwan’s economic growth rates with 77 macroeconomic variables. We evaluate the performance of different models by comparing their RMSFE and MAFE. We found that the supervised factor models usually outperform unsupervised factor model (Principal Component Analysis, PCA) and that CFPC performs the best among the three supervised factor models. Besides, the forecasts of CFPC and Directorate General of Budget, Accounting and Statistics (DGBAS) have similar performance based on the Diebold-Mariano (DM) test, so CFPC may be a good alternative when we want to avoid ad hoc models. PCovR and PLS also have smaller RMSFE and MAFE than PCA, but they are not statistically significantly better than PCA. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T03:03:09Z (GMT). No. of bitstreams: 1 ntu-103-R01323010-1.pdf: 4247557 bytes, checksum: 46785ed000ee84f2b64aa9b63d261e90 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 口試委員會審定書. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
致謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii 中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3 Extracting Information From Variables . . . . . . . . . . . . . . . . . . . 6 3.1 Principal Component Analysis . . . . . . . . . . . . . . . . . . . 6 4 Factors as Regressors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4.1 Unsupervised Factor Model . . . . . . . . . . . . . . . . . . . . 10 4.2 Supervised Factor Model . . . . . . . . . . . . . . . . . . . . . . 12 5 Empirical Findings and Discussion . . . . . . . . . . . . . . . . . . . . . 18 5.1 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.2 Performance Comparison . . . . . . . . . . . . . . . . . . . . . . 20 5.3 Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Appendices 49 1 Forecasts of DGBAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 2 Robustness Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 2.1 Different Window Size for Out-of-Sample Forecast . . . . . . . . 52 2.2 Randomly Picked Periods . . . . . . . . . . . . . . . . . . . . . 56 3 Variable list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4 Forecast value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 | |
| dc.language.iso | en | |
| dc.subject | 偏最小平方回歸模型 | zh_TW |
| dc.subject | 主成份分析 | zh_TW |
| dc.subject | 因素模型 | zh_TW |
| dc.subject | 預測 | zh_TW |
| dc.subject | 組合 預測之主成分分析 | zh_TW |
| dc.subject | 主共變量回歸模型 | zh_TW |
| dc.subject | 偏最小平方回歸模型 | zh_TW |
| dc.subject | 主成份分析 | zh_TW |
| dc.subject | 因素模型 | zh_TW |
| dc.subject | 組合 預測之主成分分析 | zh_TW |
| dc.subject | 預測 | zh_TW |
| dc.subject | 主共變量回歸模型 | zh_TW |
| dc.subject | Forecast | en |
| dc.subject | Factor Analysis | en |
| dc.subject | Principal Component Analysis | en |
| dc.subject | Partial Least Square | en |
| dc.subject | Principal Covariate Regression | en |
| dc.subject | Combining Forecast Principal Component Analysis | en |
| dc.subject | Forecast | en |
| dc.subject | Factor Analysis | en |
| dc.subject | Principal Component Analysis | en |
| dc.subject | Partial Least Square | en |
| dc.subject | Principal Covariate Regression | en |
| dc.subject | Combining Forecast Principal Component Analysis | en |
| dc.title | 考慮應變數之因素模型：以預測台灣經濟成長率為例 | zh_TW |
| dc.title | Forecast Performance of Supervised Factor Models:
An Application to Taiwan’s Economic Growth Rates | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 徐之強,徐士勛,王泓仁 | |
| dc.subject.keyword | 因素模型,主成份分析,偏最小平方回歸模型,主共變量回歸模型,組合 預測之主成分分析,預測, | zh_TW |
| dc.subject.keyword | Factor Analysis,Principal Component Analysis,Partial Least Square,Principal Covariate Regression,Combining Forecast Principal Component Analysis,Forecast, | en |
| dc.relation.page | 71 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-07-01 | |
| dc.contributor.author-college | 社會科學院 | zh_TW |
| dc.contributor.author-dept | 經濟學研究所 | zh_TW |
| 顯示於系所單位： | 經濟學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-103-1.pdf 未授權公開取用 | 4.15 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。