本論文使用狀態空間模型以及 sparse group LASSO MIDAS (sg-LASSO- MIDAS) 模型，即時預測預測美國 1996 年 5 月至 2023 年 12 月的通貨膨脹 率。實證結果顯示，使用高頻變數有助於提升美國通貨膨脹率的預測準確性，其 中 sg-LASSO-MIDAS 藉由對稀疏組的係數估計限制，將 28 筆日資料視為同一組別，並在係數估計時，對同一組別的係數估計進行相同限制，能更好的利用經狀態空間模型處理過後的高頻資料變數做出通膨預測，在本論文的五個預測期間預測結果比較中，取得最好的預測表現。

Atkeson, A., Ohanian, L. E., 2001. Are Phillips curves useful for forecasting inflation? Federal Reserve bank of Minneapolis quarterly review, 25(1), 2–11.

Babii, A., Ghysels, E., Striaukas, J., 2022. Machine learning time series regressions with an application to nowcasting. Journal of Business & Economic Statistics, 40(3), 1094–1106.

Barbaglia, L., Consoli, S., Manzan, S., 2023. Forecasting with economic news. Journal of Business & Economic Statistics, 41(3), 708–719.

Breitung, J. O., Roling, C., 2015. Forecasting inflation rates using daily data: A nonparametric MIDAS approach. Journal of Forecasting, 34(7), 588–603.

Bybee, L., Kelly, B. T., Manela, A., Xiu, D., 2020. The structure of economic news.
Cavallo, A., 2013. Online and official price indexes: Measuring Argentina’s inflation. Journal of

Monetary Economics, 60(2), 152–165.

Cavallo, A., Rigobon, R., 2016. The billion prices project: Using online prices for measurement and research. Journal of Economic Perspectives, 30(2), 151–178.

Durbin, J., Koopman, S. J., 2012. Time series analysis by state space methods.

Foroni, C., Marcellino, M., Schumacher, C., 2015. Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. Journal of the Royal Statistical Society Series A: Statistics in Society, 178(1), 57–82.

Funke, M., Mehrotra, A., Yu, H., 2015. Tracking Chinese CPI inflation in real time. Empirical Economics, 48, 1619–1641.

Ghysels, E., 2016. MIDAS matlab toolbox. URL: http://www.unc.edu/~eghysels/papers/MIDAS_Usersguide_V1.0.pdf.Lastaccessedon, 8(16), 2016.

Ghysels, E., Santa-Clara, P., Valkanov, R., 2004. The MIDAS touch: Mixed data sampling regression models.

Ghysels, E., Sinko, A., Valkanov, R., 2007. MIDAS regressions: Further results and new directions. Econometric reviews, 26(1), 53–90.

Giannone, D., Reichlin, L., Small, D., 2008. Nowcasting: The real-time informational content of macroeconomic data. Journal of monetary economics, 55(4), 665–676.

Knotek II, E. S., Zaman, S., 2017. Nowcasting US headline and core inflation. Journal of Money Credit and Banking, 49(5), 931–968.

Medeiros, M. C., Vasconcelos, G. F. R., Veiga, A., Zilberman, E., 2021. Forecasting inflation in a data-rich environment: the benefits of machine learning methods. Journal of Business & Economic Statistics, 39(1), 98–119.

Monteforte, L., Moretti, G., 2013. Real-time forecasts of inflation: The role of financial variables. Journal of Forecasting, 32(1), 51–61.

Schorfheide, F., Song, D., 2015. Real-time forecasting with a mixed-frequency VAR. Journal of Business & Economic Statistics, 33(3), 366–380.

Steindel, C., Cecchetti, S. G., Chu, R., 2005. The unreliability of inflation indicators. Available at SSRN 716681.

Stock, J. H., Watson, M. W., 1999. Forecasting inflation. Journal of monetary economics, 44(2), 293–335.

Torrontegui, E., Ibáñez, S., Martínez-Garaot, S., Modugno, M., del Campo, A., Guéry-Odelin, D., Ruschhaupt, A., Chen, X., Muga, J. G., 2013. Shortcuts to adiabaticity.

Zheng, T., Fan, X., Jin, W., Fang, K., 2024. Words or numbers? Macroeconomic nowcasting with textual and macroeconomic data. International Journal of Forecasting, 40(2), 746–761.