Factor Structural Time Series Models for Official Statistics with an Application to Hours Worked in Germany

Anderson, T.W. 1984. An Introduction to Multivariate Statistical Analysis. New York: Wiley.Search in Google Scholar

Bai, J. and S. Ng. 2002. “Determining the Number of Factors in Approximate Factor Models.” Econometrica 70(1): 191-221. Doi: http://dx.doi.org/10.1111/1468-0262.00273.10.1111/1468-0262.00273Open DOISearch in Google Scholar

Bai, J. and S. Ng. 2004. “A PANIC Attack on Unit Roots and Cointegration.” Econometrica 72(4): 1127-1177. Available at: http://www.columbia.edu/,sn2294/pub/ecta04.pdf (accessed December 2017).10.1111/j.1468-0262.2004.00528.xSearch in Google Scholar

Bai, J. and S. Ng. 2008. “Large Dimensional Factor Analysis.” Foundations and Trends in Econometrics 3(2): 89-163. Doi: http://dx.doi.org/10.1561/0800000002.10.1561/0800000002Open DOISearch in Google Scholar

Bai, J. and S. Ng. 2013. “Principal Components Estimation and Identification of Static Factors.” Journal of Econometrics 176(1): 18-29. Doi: https://doi.org/10.1016/j.jeconom.2013.03.007.10.1016/j.jeconom.2013.03.007Open DOISearch in Google Scholar

Banerjee, A., M. Marcellino, and I. Masten. 2014. “Forecasting with Factor-Augmented Error Correction Models.” International Journal of Forecasting 30(3): 589-612. Doi: https://doi.org/10.1016/j.ijforecast.2013.01.009.10.1016/j.ijforecast.2013.01.009Open DOISearch in Google Scholar

Bernanke, B.S., J. Boivin, and P. Eliasz. 2005. “Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach.” The Quarterly Journal of Economics 120(1): 387-422. Doi: https://doi.org/10.1162/0033553053327452.10.1162/0033553053327452Open DOISearch in Google Scholar

Bollineni-Balabay, O., J. van den Brakel, and F. Palm. 2015. “Multivariate State Space Approach to Variance Reduction in Series with Level and Variance Breaks Due to Survey Redesigns.” Journal of the Royal Statistical Society: Series A (Statistics in Society). Doi: http://dx.doi.org/10.1111/rssa.12117.10.1111/rssa.12117Open DOISearch in Google Scholar

Bräuning, F. and S.J. Koopman. 2014. “Forecasting Macroeconomic Variables Using Collapsed Dynamic Factor Analysis.” International Journal of Forecasting 30(3): 572-584. Doi: https://doi.org/10.1016/j.ijforecast.2013.03.004.10.1016/j.ijforecast.2013.03.004Open DOISearch in Google Scholar

Breitung, J. and J. Tenhofen. 2011. “GLS Estimation of Dynamic Factor Models.” Journal of the American Statistical Association 106(495): 1150-1166. Doi: https://doi.org/10.1198/jasa.2011.tm09693.10.1198/jasa.2011.tm09693Open DOISearch in Google Scholar

Burda, M.C. and J. Hunt. 2011. “What Explains the German Labor Market Miracle in the Great Recession?” Brookings Papers on Economic Activity, Spring 2011, 273-335. Available at: https://www.brookings.edu/wp-content/uploads/2011/03/2011a_bpea_burda.pdf (accessed December 2017).10.1353/eca.2011.0004Search in Google Scholar

Bureau of Economic Analysis. 2017. Concepts and Methods of the U.S. National Income and Product Accounts. Technical report. Available at: https://bea.gov/national/pdf/allchapters.pdf (accessed February 2018).Search in Google Scholar

Commandeur, J., S. Koopman, and M. Ooms. 2011. “Statistical Software for State Space Methods.” Journal of Statistical Software 41(1): 1-18. Available at: https://www.researchgate.net/publication/260335110_Statistical_Software_for_State_Space_Methods (accessed December 2017).10.18637/jss.v041.i01Search in Google Scholar

Croushore, D. 2011. “Frontiers of Real-Time Data Analysis.” Journal of Economic Literature 49(1): 72-100. Doi: http://dx.doi.org/10.1257/jel.49.1.72.10.1257/jel.49.1.72Open DOISearch in Google Scholar

Durbin, J. 2000. “The State Space Approach to Time Series Analysis and its Potential for Official Statistics.” The Australian and New Zealand Journal of Statistics 42(1): 1-23. Doi: http://dx.doi.org/10.1111/1467-842X.00104.10.1111/1467-842X.00104Open DOISearch in Google Scholar

Durbin, J. and S.J. Koopman. 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford: Oxford Statistical Science Series.10.1093/acprof:oso/9780199641178.001.0001Search in Google Scholar

Durbin, J. and B. Quenneville. 1997. “Benchmarking by State Space Models.” International Statistical Review 65(1): 23-48. Doi: http://dx.doi.org/10.1111/j.1751-5823.1997.tb00366.x.10.1111/j.1751-5823.1997.tb00366.xOpen DOISearch in Google Scholar

Eickmeier, S. 2009. “Co-Movements and Heterogeneity in the Euro Area Analyzed in a Nonstationary Dynamic Factor Model.” Journal of Applied Econometrics 24(6): 933-959. Doi: http://dx.doi.org/10.1002/jae.1068.10.1002/jae.1068Open DOISearch in Google Scholar

Federal Statistical Office. 2008. National Accounts: Quarterly Calculations of Gross Domestic Product in accordance with ESA 1995 - Methods and Data Sources. Subjectmatter Series 18, S. 23. Available at: https://www.destatis.de/EN/Publications/Specialized/Nationalaccounts/QuarterlyCalculationsGrossDomesticProductAccordance.pdf?__blob¼publicationFile (accessed December 2017).Search in Google Scholar

Giannone, D., L. Reichlin, and D. Small. 2008. “Nowcasting: The Real-Time Informational Content of Macroeconomic Data.” Journal of Monetary Economics 55(4): 665-676. Available at: https://EconPapers.repec.org/RePEc:eee:moneco:v:55:y:2008:i:4:p:665-676 (accessed December 2017).10.1016/j.jmoneco.2008.05.010Open DOISearch in Google Scholar

Harvey, A. and C.-H. Chung. 2000. “Estimating the Underlying Change in Unemployment in the UK.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 163(3): 303-339. Doi: http://dx.doi.org/10.1111/1467-985X.00171.10.1111/1467-985X.00171Open DOISearch in Google Scholar

Harvey, A. and S. Koopman. 1997. Multivariate Structural Time Series Models. Systematic Dynamics in Economic and Financial Models, (pp. 269-298). Available at: http://personal.vu.nl/s.j.koopman/old/publications/multi_stsm.pdf (accessed February 2018).Search in Google Scholar

Harvey, A.C. 1991. Forecasting, Structural Time Series Models and the Kalman Filter. New York: Cambridge University Press.10.1017/CBO9781107049994Search in Google Scholar

Helske, J. 2016. KFAS: Kalman Filter and Smoother for Exponential Family State Space Models. R package version 1.2.4.Search in Google Scholar

Koopman, S., A. Harvey, J. Doornik, and N. Shephard. 2009. STAMP 8.2: Structural Time Series Analyser, Modeler, and Predictor. London: Timberlake Consultants.Search in Google Scholar

Krieg, S. and J.A. van den Brakel. 2012. “Estimation of the Monthly Unemployment Rate for Six Domains through Structural Time Series Modelling with Cointegrated Trends.” Computational Statistics and Data Analysis 56(10): 2918-2933. Doi: https://doi.org/10.1016/j.csda.2012.02.008.10.1016/j.csda.2012.02.008Search in Google Scholar

Moauro, F. and G. Savio. 2005. “Temporal Disaggregation Using Multivariate Structural Time Series Models.” The Econometrics Journal 8(2): 214-234. Doi: http://dx.doi.org/10.1111/j.1368-423X.2005.00161.x.10.1111/j.1368-423X.2005.00161.xOpen DOISearch in Google Scholar

Morley, J.C., C.R. Nelson, and E. Zivot. 2003. “Why Are the Beveridge-Nelson and Unobserved Components Decompositions of GDP so Different?” Review of Economics and Statistics 85(2): 235-243. Available at: http://www.jstor.org/stable/3211575 (accessed December 2017).10.1162/003465303765299765Search in Google Scholar

Ohanian, L.E. and A. Raffo. 2012. “Aggregate Hours Worked in OECD Countries: New Measurement and Implications for Business Cycles.” Journal of Monetary Economics 59(1): 40-56. Doi: https://doi.org/10.1016/j.jmoneco.2011.11.005.10.1016/j.jmoneco.2011.11.005Open DOISearch in Google Scholar

Pfeffermann, D. 1991. “Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys.” Journal of Business and Economic Statistics 9(2): 163-175.10.1080/07350015.1991.10509840Search in Google Scholar

Pfeffermann, D. and R. Tiller. 2006. “Small-Area Estimation with State-Space Models Subject to Benchmark Constraints.” Journal of the American Statistical Association 101(476): 1387-1397. Doi: https://doi.org/10.1198/016214506000000591.10.1198/016214506000000591Open DOISearch in Google Scholar

Quenneville, B. and C. Gagne´. 2013. “Testing Time Series Data Compatibility for Benchmarking.” International Journal of Forecasting 29(4): 754-766. Doi: https://doi.org/10.1016/j.ijforecast.2011.10.001.10.1016/j.ijforecast.2011.10.001Open DOISearch in Google Scholar

Rünstler, G. 2004. “Modelling Phase Shifts Among Stochastic Cycles.” Econometrics Journal 7(1): 232-248. Doi: http://dx.doi.org/10.1111/j.1368-423X.2004.00129.x.10.1111/j.1368-423X.2004.00129.xOpen DOISearch in Google Scholar

Stock, J.H. and M.W. Watson. 2002. “Macroeconomic Forecasting Using Diffusion Indexes.” Journal of Business and Economic Statistics 20(2): 147-162. Doi: https://doi.org/10.1198/073500102317351921.10.1198/073500102317351921Open DOISearch in Google Scholar

Stock, J.H. and M.W. Watson. 2005. “Implications of Dynamic Factor Models for VAR Analysis.” NBER Working Paper 11467, NBER. Doi: http://dx.doi.or/10.3386/w11467.10.3386/w11467Open DOISearch in Google Scholar

Stock, J.H. and M.W. Watson. 2011. “Dynamic Factor Models.” In The Oxford Handbook of Economic Forecasting, edited by M. Clements and D. Hendry, 35-60. Oxford: Oxford University Press.10.1093/oxfordhb/9780195398649.013.0003Search in Google Scholar

Tiller, R.B. 1992. “Time Series Modeling of Sample Survey Data from the US Current Population Survey.” Journal of Official Statistics 8(2): 149-166. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/time-series-modelingof-sample-survey-data-from-the-u.s.-current-population-survey.pdf (accessed December 2017).Search in Google Scholar

Valle e Azevedo, J., S.J. Koopman, and A. Rua. 2006. “Tracking the Business Cycle of the Euro Area: a Multivariate Model-Based Bandpass Filter.” Journal of Business and Economic Statistics 24(3): 278-290. Available at: http://www.jstor.org/stable/27638878 (accessed December 2017).10.1198/073500105000000261Search in Google Scholar

Wanger, S. 2013. “Arbeitszeit und Arbeitsvolumen in Deutschland - Methodische Grundlagen und Ergebnisse der Arbeitszeitrechnung.” Wirtschafts- und Sozialstatistisches Archiv 7(1-2): 31-69. Doi: https://doi.org/10.1007/s11943-013-0127-0.10.1007/s11943-013-0127-0Open DOISearch in Google Scholar

Wanger, S., R. Weigand, and I. Zapf. 2016. “Measuring Hours Worked in Germany -Contents, Data and Methodological Essentials of the IAB Working Time Measurement Concept.” Journal for Labour Market Research 49(3): 213-238. Doi: https://doi.org/10.1007/s12651-016-0206-0.10.1007/s12651-016-0206-0Open DOISearch in Google Scholar

Wood, J. and D. Elliott. 2007. “Methods Explained: Forecasting.” Economic and Labour Market Review 1(12): 55-58. Doi: https://doi.org/10.1057/palgrave.elmr.1410188.10.1057/palgrave.elmr.1410188Open DOISearch in Google Scholar