Forecasting a Nonstationary Time Series Using a Mixture of Stationary and Nonstationary Factors as Predictors

References

• Bai, J. (2003), “Inferential Theory for Factor Models of Large Dimensions,” Econometrica, 71, 135–171. DOI: 10.1111/1468-0262.00392.
   Web of Science ®Google Scholar
• Bai, J. (2004), “Estimating Cross-Section Common Stochastic Trends in Nonstationary Panel Data,” Journal of Econometrics, 122, 137–183.
   Web of Science ®Google Scholar
• Bai, J., and Ng, S. (2002), “Determining the Number of Factors in Approximate Factor Models,” Econometrica, 70, 191–221. DOI: 10.1111/1468-0262.00273.
   Web of Science ®Google Scholar
• Bai, J. (2004), “A Panic Attack on Unit Roots and Cointegration,” Econometrica, 72, 1127–1177.
   Web of Science ®Google Scholar
• Bai, J. (2006), “Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions,” Econometrica, 74, 1133–1150.
   Web of Science ®Google Scholar
• Bai, J. (2013), “Principal Components Estimation and Identification of Static Factors,” Journal of Econometrics, 176, 18–29.
   Web of Science ®Google Scholar
• Banerjee, A., Marcellino, M., and Masten, I. (2008), “Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change,” in Forecasting in the Presence of Structural Breaks and Model Uncertainty (Frontiers of Economics and Globalization) (Vol. 3), eds. D. Rapach and M. Wohar, pp. 149–194, Bingley, UK: Emerald Group Publishing Limited.
   Google Scholar
• Bernanke, B. S., Boivin, J., and Eliasz, P. (2005), “Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (favar) Approach,” The Quarterly Journal of Economics, 120, 387–422. DOI: 10.1162/qjec.2005.120.1.387.
   Web of Science ®Google Scholar
• Breitung, J., and Eickmeier, S. (2011), “Testing for Structural Breaks in Dynamic Factor Models,” Journal of Econometrics, 163, 71–84. DOI: 10.1016/j.jeconom.2010.11.008.
   Web of Science ®Google Scholar
• Cheng, X., and Hansen, B. E. (2015a), “Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach,” Journal of Econometrics, 186, 280–293. DOI: 10.1016/j.jeconom.2015.02.010.
   Web of Science ®Google Scholar
• Cheng, X. (2015b), “Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach,” Journal of Econometrics, 186, 280–293.
   Web of Science ®Google Scholar
• Choi, I. (2017), “Efficient Estimation of Nonstationary Factor Models,” Journal of Statistical Planning and Inference, 183, 18–43. DOI: 10.1016/j.jspi.2016.10.003.
   Web of Science ®Google Scholar
• Corradi, V., and Swanson, N. R. (2014), “Testing for Structural Stability of Factor Augmented Forecasting Models,” Journal of Econometrics, 182, 100–118. DOI: 10.1016/j.jeconom.2014.04.011.
   Web of Science ®Google Scholar
• Djogbenou, A. A. (2021), “Model Selection in Factor-Augmented Regressions with Estimated Factors,” Econometric Reviews, 40, 470–503. DOI: 10.1080/07474938.2020.1808371.
   Web of Science ®Google Scholar
• Eickmeier, S. (2005), “Common Stationary and Non-stationary Factors in the Euro Area Analyzed in a Large-Scale Factor Model,” Discussion paper 2, Studies of the Economic Research Centre.
   Google Scholar
• Giraitis, L., Kapetanios, G., and Price, S. (2013), “Adaptive Forecasting in the Presence of Recent and Ongoing Structural Change,” Journal of Econometrics, 177, 153–170. DOI: 10.1016/j.jeconom.2013.04.003.
   Web of Science ®Google Scholar
• Gonçalves, S., and Perron, B. (2014), “Bootstrapping Factor-Augmented Regression Models,” Journal of Econometrics, 182, 156–173. DOI: 10.1016/j.jeconom.2014.04.015.
   Web of Science ®Google Scholar
• Groen, J. J. J., and Kapetanios, G. (2013), “Model Selection Criteria for Factor-Augmented Regressions,” Oxford Bulletin of Economics and Statistics, 75, 37–63. DOI: 10.1111/j.1468-0084.2012.00721.x.
   Web of Science ®Google Scholar
• Li, H., Li, Q., and Shi, Y. (2017), “Determining the Number of Factors when the Number of Factors can Increase with Sample Size,” Journal of Econometrics, 197, 76–86. DOI: 10.1016/j.jeconom.2016.06.003.
   Web of Science ®Google Scholar
• Ludvigson, S. C., and Ng, S. (2007), “The Empirical Risk–Return Relation: A Factor Analysis Approach,” Journal of Financial Economics, 83, 171–222. DOI: 10.1016/j.jfineco.2005.12.002.
   Web of Science ®Google Scholar
• Moon, H. R., and Perron, B. (2007), “An Empirical Analysis of Nonstationarity in a Panel of Interest Rates with Factors,” Journal of Applied Econometrics, 22, 383–400. DOI: 10.1002/jae.931.
   Web of Science ®Google Scholar
• Phillips, P. C. (2012), “Folklore Theorems, Implicit Maps, and Indirect Inference,” Econometrica, 80, 425–454.
   Web of Science ®Google Scholar
• Phillips, P. C. (2015), “Halbert White jr. Memorial JFEC Lecture: Pitfalls and Possibilities in Predictive Regression,” Journal of Financial Econometrics, 13, 521–555.
   Web of Science ®Google Scholar
• Rossi, B. (2021), “Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them,” Journal of Economic Literature, 59, 1135–90. DOI: 10.1257/jel.20201479.
   Web of Science ®Google Scholar
• Smeekes, S., and Wijler, E. (2019), “High-Dimensional Forecasting in the Presence of Unit Roots and Cointegration,” Working paper, Department of Quantitative Economics, Maastricht University.
   Google Scholar
• Stock, J. H., and Watson, M. W. (1998a), “A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series,” Technical Report, National Bureau of Economic Research.
   Google Scholar
• Stock, J. H. (1998b), “Diffusion Indexes,” Technical Report, National Bureau of Economic Research.
   Google Scholar
• Stock, J. H. (2002a), “Forecasting Using Principal Components from a Large Number of Predictors,” Journal of the American Statistical Association, 97, 1167–1179.
   Web of Science ®Google Scholar
• Stock, J. H. (2002b), “Macroeconomic Forecasting Using Diffusion Indexes,” Journal of Business & Economic Statistics, 20, 147–162.
   Web of Science ®Google Scholar
• Stock, J. H. (2009), “Forecasting in Dynamic Factor Models Subject to Structural Instability,” in The Methodology and Practice of Econometrics. A Festschrift in Honour of David F. Hendry (Vol. 173), eds. N. Shephard and J. Castle, p. 205, Oxford: Oxford University Press.
   Google Scholar
• Stock, J. H. (2012), “Generalized Shrinkage Methods for Forecasting Using Many Predictors,” Journal of Business & Economic Statistics, 30, 481–493.
   Web of Science ®Google Scholar
• Su, L., and Wang, X. (2017), “On Time-Varying Factor Models: Estimation and Testing,” Journal of Econometrics, 198, 84–101. DOI: 10.1016/j.jeconom.2016.12.004.
   Web of Science ®Google Scholar
• Wei, J., and Zhang, Y. (2020), “A Time-Varying Diffusion Index Forecasting Model,” Economics Letters, 193, 109337. DOI: 10.1016/j.econlet.2020.109337.
   Web of Science ®Google Scholar