Reference: | 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. |