Bias correction for time series factor models

References

• Peña D, Box G. Identifying a simplifying structure in time series. J Amer Statist Assoc. 1987;82:836–843.
   Web of Science ®Google Scholar
• Alonso A, García-Martos C, Rodríguez J, et al. Seasonal dynamic factor analysis and bootstrap inference: application to electricity market forecasting. Technometrics. 2011;53:137–151. doi: 10.1198/TECH.2011.09050
   Web of Science ®Google Scholar
• García-Martos C, Rodríguez J, Sánchez M. Forecasting electricity prices by extracting dynamic common factors: application to the Iberian market. IET Gener Transm Distrib. 2012;6:11–20. doi: 10.1049/iet-gtd.2011.0009
   Web of Science ®Google Scholar
• Alonso A, Bastos G, García-Martos C. Electricity price forecasting by averaging dynamic factor models. Energies. 2016;9:1–21. doi: 10.3390/en9080600
   Web of Science ®Google Scholar
• García-Martos C, Rodríguez J, Sánchez M. Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities. Appl Energy. 2013;101:363–375. doi: 10.1016/j.apenergy.2012.03.046
   Web of Science ®Google Scholar
• Poncela P, Senra E, Sierra L. Common dynamics of nonenergy commodity prices and their relation to uncertainty. Appl Econ. 2014;46:3724–3735. doi: 10.1080/00036846.2014.939377
   Web of Science ®Google Scholar
• Stock J, Watson M. Forecasting using principal components from a large number of predictors. J Amer Statist Assoc. 2002;97:1167–1179. doi: 10.1198/016214502388618960
   Web of Science ®Google Scholar
• Sargent T, Sims C. Business cycle modeling without pretending to have too much a priory economic theory. Working paper 55 Federal Reserve Bank of Minneapolis; 1977.
   Google Scholar
• Forni M, Hallin M, Lippi M, et al. Reference cycles: the NBER methodology revisited. NBER; 1999.
   Google Scholar
• Lee R, Carter L. Modeling and forecasting US mortality. J Amer Statist Assoc. 1992;87:659–671.
   Web of Science ®Google Scholar
• Alonso A, Peña D, Rodríguez J. A methodology for population projections: an application to Spain. Working Paper 08 – 45 Statistics and Econometrics Series 12; 2008. Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.584.2593&rep=rep1&type=pdf
   Google Scholar
• Jeong S, Bienkiewicz B. Application of autoregressive modeling in proper orthogonal decomposition of building wind pressure. J Wind Eng Ind Aerod. 1997;69:685–695. doi: 10.1016/S0167-6105(97)00197-9
   Google Scholar
• García-Ferrer A, González-Prieto E, Peña D. Exploring ICA for time series decomposition. Working Paper 11–16 Statistics and Econometrics Series 11 Departamento de Estadística Universidad Carlos III de Madrid May 2011; 2011.
   Google Scholar
• Peña D, Poncela P. Nonstationary dynamic factor analysis. J Statist Plann Inference. 2006;136:1237–1257. doi: 10.1016/j.jspi.2004.08.020
   Web of Science ®Google Scholar
• Clements M, Kim J. Bootstrap prediction intervals for autoregressive time series. Comput Statist Data Anal. 2007;51:3580–3594. doi: 10.1016/j.csda.2006.09.012
   Web of Science ®Google Scholar
• Roy A, Fuller W. Estimation for autoregressive time series with a root near 1. J Bus Econom Statist. 2001;19:482–493. doi: 10.1198/07350010152596736
   Web of Science ®Google Scholar
• Clements M, Taylor N. Bootstrapping prediction intervals for autoregressive models. Int J Forecast. 2001;17:247–267. doi: 10.1016/S0169-2070(00)00079-0
   Web of Science ®Google Scholar
• Gregory A, Head A. Common and country-specific fluctuations in productivity, investment, and the current account. J Monet Econ. 1999;44:423–451. doi: 10.1016/S0304-3932(99)00035-5
   Web of Science ®Google Scholar
• Geweke J, Singleton K. Maximum likelihood ‘confirmatory’ factor analysis of economic time series. Int Econ Rev (Philadelphia). 1981;22:37–54. doi: 10.2307/2526134
   Web of Science ®Google Scholar
• Bai J, Ng S. Large dimensional factor analysis. Found Trends Econom. 2008;3:89–163. doi: 10.1561/0800000002
   Google Scholar
• Stock J, Watson M Dynamic factor models. In: Clements MP, Hendry DF, editors. Oxford handbook of economic forecasting. New York: Oxford University Press; 2010. p. 35–60.
   Google Scholar
• Poncela P, Ruiz E. Small- versus big-data factor extraction in dynamic factor models: an empirical assesment. In: Hillebrand E, Koopman SJ, editors. Dynamic factor models (Advances in econometrics, Volume 35). West Yorkshire: Emerald Group Publishing Limited; 2016. p. 401–434.
   Google Scholar
• Mardia K, Kent T, Bibby M. Multivariate analysis. San Diego: Academic Press, Inc.; 1979. Chapter 8 and 9.
   Google Scholar
• Bai J, Ng S. Determining the number of factors in approximate factor models. Econometrica. 2002;70:191–221. doi: 10.1111/1468-0262.00273
   Web of Science ®Google Scholar
• Hurvich C, Tsai C. Regression and time series model selection in small samples. Biometrika. 1989;76:297–307. doi: 10.1093/biomet/76.2.297
   Web of Science ®Google Scholar
• Schwarz G. Estimating the dimension of a model. Ann Statist. 1978;6:461–464. doi: 10.1214/aos/1176344136
   Web of Science ®Google Scholar
• Ahn S, Horenstein A. Eigenvalue ratio test for the number of factors. Econometrica. 2013;81:1203–1227. doi: 10.3982/ECTA8968
   Web of Science ®Google Scholar
• Peña D, Safadi T. Bayesian analysis of dynamic factor models: an application to air pollution and mortality in São Paulo, Brazil. Environmetrics. 2008;19:582–601. doi: 10.1002/env.899
   Web of Science ®Google Scholar
• Doz C, Giannone D, Reichlin L. A quasi-maximum likelihood approach for large, approximate dynamic factor models. Rev Econ Stat. 2012;94:1014–1024. doi: 10.1162/REST_a_00225
   Web of Science ®Google Scholar
• García-Ferrer A, González-Prieto E, Peña D. A conditionally heteroskedastic independent factor model with an application to financial stock returns. Int J Forecast. 2012;28:70–93. doi: 10.1016/j.ijforecast.2011.02.010
   Web of Science ®Google Scholar
• Fiorentini G, Galesi A, Sentana E. A spectral EM algorithm for dynamic factor models. Documentos de Trabajo, Banco de España. 2016;1619:1–62.
   Google Scholar
• Alonso A, Bastos G, García-Martos C. Bias correction for dynamic factor models. UC3M Working Paper 17 – 02 Statistics and Econometrics; 2017. Available from: http://e-archivo.uc3m.es/handle/10016/240290:1--44
   Google Scholar
• Kilian L. Accounting for lag-order uncertainty in autoregressions: the endogenous lag order bootstrap algorithm. J Time Ser Anal. 1998;19:531–548. doi: 10.1111/1467-9892.00107
   Google Scholar
• MacKinnon J, Smith J. Approximate bias correction in econometrics. J Econometrics. 1998;85:205–230. doi: 10.1016/S0304-4076(97)00099-7
   Web of Science ®Google Scholar
• Kilian L. Small-sample confidence intervals for impulse response functions. Rev Econ Stat. 1998;80:218–230. doi: 10.1162/003465398557465
   Web of Science ®Google Scholar
• Pascual L, Romo J, Ruiz E. Bootstrap predictive inference for arima processes. J Time Ser Anal. 2004;25:449–465. doi: 10.1111/j.1467-9892.2004.01713.x
   Web of Science ®Google Scholar
• Pascual L, Romo J, Ruiz E. Effects of parameter estimation on prediction densities: a bootstrap approach. Int J Forecast. 2001;17:83–103. doi: 10.1016/S0169-2070(00)00069-8
   Web of Science ®Google Scholar
• Fresoli D, Ruiz E, Pascual L. Bootstrap multi-step forecasts of non-Gaussian VAR models. Int J Forecast. 2015;31:834–848. doi: 10.1016/j.ijforecast.2014.04.001
   Web of Science ®Google Scholar
• Ruiz E, Poncela P. More is not always better: Kalman filtering in dynamic factor models. In: Koopman S J, Shephard N, editors. Unobserved components and time series econometrics. New York: Oxford University Press; 2015; Chapter 11.
   Google Scholar
• Alonso A, Peña D, Romo J. Forecasting time series with sieve bootstrap. J Statist Plann Inference. 2002;100:1–11. doi: 10.1016/S0378-3758(01)00092-1
   Web of Science ®Google Scholar
• Marcellino M, Stock J, Watson M. A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. J Econometrics 2006;135:499–526. doi: 10.1016/j.jeconom.2005.07.020
   Web of Science ®Google Scholar
• Robinson P. Conditional-sum-of-squares estimation of models for stationary time series with long memory. IMS Lecture Notes-Monograph Series Time Series and Related Topics. 2006;52:130–137. doi: 10.1214/074921706000000996
   Google Scholar
• Egrioglu E, Aladag C, Kadilar C. The CSS and the two-staged methods for parameter estimation in SARFIMA models. J Probab Statist. 2011;2011:1–11. doi: 10.1155/2011/691058
   Google Scholar
• Alonso A, Peña D, Romo J. Introducing model uncertainty in time series bootstrap. Statist Sinica. 2004;14:155–174.
   Web of Science ®Google Scholar