在本文中，我們評估總體資料、廠商資料和高頻資料能否幫助預測美國工業生產指數和通貨膨脹，並藉由動態因子模型和因子混頻抽樣迴歸模型(MIDAS)進行實證研究。研究結果顯示，除了廣泛使用於預測工業生產指數和通貨膨脹的總體資料外，廠商以及高頻資料可能也包含有助於長期預測的訊息。

In this thesis, we assess the performance of a large-dimensional set of macro-level, firm-level and daily predictors in forecasting the industrial production and inflation of the U.S. We base this empirical study on the dynamic factor model and the factor mixed data sampling regression (MIDAS). The empirical study shows that the firm-level and high-frequency predictors may contain useful information in addition to the widely used macro-level predictors in the long-term forecast of the industrial production and inflation.

論文口試委員審定書 i
摘要 ii
Abstract iii
Contents iv
List of Figures v
List of Tables vi
1 Introduction 1
2 Econometric Models 4
2.1 Dynamic factor model 4
2.2 Factor MIDAS 5
3 Empirical Analysis 7
3.1 Data 7
3.2 Empirical Design 8
3.3 Empirical Findings 10
4 Conclusion 21
References 22
A Appendix 25
A.1 Macro-level data 25
A.2 Firm-level data 25
A.3 Daily data 30
A.4 Model selection 34

Almon, S. (1965): “The distributed lag between capital appropriations and expenditures,” Econometrica, 33, 178-196.
Andreou, E., E. Ghysels, and A. Kourtellos (2013): “Should macroeconomic forecasters use daily financial data and how?” Journal of Business and Economic Statistics, 31, 240–251.
Aruoba, S. B., F. X. Diebold, and C. Scotti (2009): “Real-time measurement of business conditions,” Journal of Business and Economic Statistics, 27, 417–427.
Bai, J. and S. Ng (2002): “Determining the number of factors in approximate factor models,” Econometrica, 70, 191–221.
Bai, J. and S. Ng (2006): “Confidence intervals for diffusion index forecasts and inference for factor-augmented regressions,” Econometrica, 74, 1133–1150.
Bai, J. and S. Ng (2007): “Determining the number of primitive shocks in factor models,” Journal of Business and Economic Statistics, 25, 52–60.
Bai, J. and S. Ng (2008): “Large dimensional factor analysis,” Foundations and Trends in Econometrics, 3, 89–163.
Bartelsman, E. J. and Z. Wolf (2014): “Forecasting aggregate productivity using information from firm-level data,” The Review of Economics and Statistics, 96, 745-755.
Clements, M. P. and A. B. Galvo (2008): “Macroeconomic forecasting with mixed-frequency data,” Journal of Business & Economic Statistics, 26, 546–554.
Figlewski, S., H. Frydman, and W. Liang (2012): “Modeling the effect of macroeconomic factors on corporate default and credit rating transitions,” International Review of Economics and Finance, 21, 87–105.
Fornaro, P. (2016): “Predicting Finnish economic activity using firm-level data,” International Journal of Forecasting, 32, 10–19.
Forni, M., M. Hallin, M. Lippi, and L. Reichlin (2003): “Do financial variables help forecasting inflation and real activity in the euro area?” Journal of Monetary Economics, 50, 1243–1255.
Gabaix, X. (2011): “The granular origins of aggregate fluctuations,” Econometrica, 79, 733–772.
Ghysels, E., V. Kvedaras, and V. Zemlys (2016): “ Mixed frequency data sampling regression models: The R package midasr,” Journal of Statistical Software, 72.
Ghysels, E., P. Santa-Clara, and R. Valkanov (2004): “The MIDAS touch: Mixed data sampling regression models,” University of North Carolina and UCLA Discussion Paper.
Ghysels, E., P. Santa-Clara, and R. Valkanov (2006): “Predicting volatility: Getting the most out of return data sampled at different frequencies,” Journal of Econometrics, 131, 59–95.
Jurado, K., S. C. Ludvigson, and S. Ng (2015): “Measuring uncertainty,” American Economic Review, 105, 1177–1216.
Kim, H. H. and N. R. Swanson (2018): “Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods,” International Journal of Forecasting, 34, 339–354.
Korajczyk, R. A. and A. Levy (2003): “Capital structure choice: macroeconomic conditions and financial constraints,” Journal of Financial Economics, 68, 75–109.
Ludvigson, S. C. and S. Ng (2009): “A factor analysis of bond risk premia,” Working paper 15188, National Bureau of Economic Research.
Marcellino, M. and C. Schumacher (2010): “Factor MIDAS for nowcasting and forecasting with ragged-edge data: A model comparison for German GDP,” Oxford Bulletin of Economics and Statistics, 72, 518–550.
McCracken, M. W. and S. Ng (2016): “FRED-MD: A monthly database for macroeconomic research,” Journal of Business and Economic Statistics, 34, 574– 589.
Monteforte, L. and G. Moretti (2013): “Real-time forecasts of inflation: The role of financial variables,” Journal of Forecasting, 32, 51–61.
Nash, J. C. and R. Varadhan (2011): “Unifying optimization algorithms to aid software system users: optimx for R ,” Journal of Statistical Software, 43.
Stock, J. H. and M. W. Watson (1996): “Evidence on structural instability in macroeconomic time series relations,” Journal of Business and Economic Statistics, 14, 11–30.
Stock, J. H. and M. W. Watson (1998): “Diffusion indexes,” Working paper 6702, National Bureau of Economic Research.
Stock, J. H. and M. W. Watson (1999): “Forecasting inflation,” Journal of Monetary Economics, 44, 293–335.
Stock, J. H. and M. W. Watson (2002): “Macroeconomic forecasting using diffusion indexes,” Journal of Business and Economic Statistics, 20, 147–162.
Stock, J. H. and M. W. Watson (2003): “Forecasting output and inflation: The role of asset prices,” Journal of Economic Literature, 41, 788–829.