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An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm

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Cited by:

  1. Triantafyllopoulos, K., 2008. "Missing observation analysis for matrix-variate time series data," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2647-2653, November.
  2. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]," MPRA Paper 63713, University Library of Munich, Germany.
  3. Matteo Barigozzi & Matteo Luciani, 2019. "Quasi Maximum Likelihood Estimation of Non-Stationary Large Approximate Dynamic Factor Models," Papers 1910.09841, arXiv.org.
  4. Arias, Maria A. & Gascon, Charles S. & Rapach, David E., 2016. "Metro business cycles," Journal of Urban Economics, Elsevier, vol. 94(C), pages 90-108.
  5. De Blander, Rembert, 2020. "Iterative estimation correcting for error auto-correlation in short panels, applied to lagged dependent variable models," Econometrics and Statistics, Elsevier, vol. 15(C), pages 3-29.
  6. T.P.Koirala Ph.D., 2013. "Time-Varying Parameters of Inflation Model in Nepal: State Space Modeling," NRB Economic Review, Nepal Rastra Bank, Research Department, vol. 25(2), pages 66-77, October.
  7. Xinggang Zhang & Pan Li & Rui Tu & Xiaochun Lu & Maorong Ge & Harald Schuh, 2020. "Automatic Calibration of Process Noise Matrix and Measurement Noise Covariance for Multi-GNSS Precise Point Positioning," Mathematics, MDPI, vol. 8(4), pages 1-20, April.
  8. Mandrekar, V. & Naik-Nimbalkar, U.V., 2009. "Identification of a Markovian system with observations corrupted by a fractional Brownian motion," Statistics & Probability Letters, Elsevier, vol. 79(7), pages 965-968, April.
  9. Scott Brave & R. Andrew Butters, 2014. "Nowcasting Using the Chicago Fed National Activity Index," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q I, pages 19-37.
  10. Poncela, Pilar, 2021. "Dynamic factor models: does the specification matter?," DES - Working Papers. Statistics and Econometrics. WS 32210, Universidad Carlos III de Madrid. Departamento de Estadística.
  11. Bart Keijsers & Bart Diris & Erik Kole, 2018. "Cyclicality in losses on bank loans," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 533-552, June.
  12. Raïsa Basselier & David Antonio Liedo & Geert Langenus, 2018. "Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 1-46, April.
  13. Adrian Pizzinga & Marcelo Fernandes, 2021. "Extensions to the invariance property of maximum likelihood estimation for affine‐transformed state‐space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(3), pages 355-371, May.
  14. Tommaso Proietti & Alessandra Luati, 2013. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 15, pages 334-362, Edward Elgar Publishing.
  15. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
  16. Mazzocchi, Mario & Lobb, Alexandra E., 2005. "A Latent-Variable Approach to Modelling Multiple and Resurgent Meat Scares in Italy," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24509, European Association of Agricultural Economists.
  17. Shaojun Ma & Pengcheng Li, 2021. "Predicting Daily Trading Volume via Various Hidden States," Papers 2107.07678, arXiv.org.
  18. Aaron J. Amburgey & Michael W. McCracken, 2023. "On the real‐time predictive content of financial condition indices for growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 137-163, March.
  19. Pawel Krolikowski & Kurt Graden Lunsford & Meifeng dup Yang, 2019. "Using Advance Layoff Notices as a Labor Market Indicator," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2019(21), December.
  20. Jushan Bai & Serena Ng, 2021. "Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1746-1763, October.
  21. Rainer Schulz & Hizir Sofyan & Axel Werwatz & Rodrigo Witzel, 2003. "Online Prediction of Berlin Single-Family House Prices," Computational Statistics, Springer, vol. 18(3), pages 449-462, September.
  22. André Nunes Maranhão, 2024. "Brazilian Business Cycle Analysis in a High-Dimensional and Time-Irregular Span Context," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 20(1), pages 1-58, August.
  23. Poncela, Marta & Poncela, Pilar & Perán, José Ramón, 2013. "Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting," Applied Energy, Elsevier, vol. 108(C), pages 349-362.
  24. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
  25. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
  26. Flavio Cunha & James J. Heckman & Susanne M. Schennach, 2010. "Estimating the Technology of Cognitive and Noncognitive Skill Formation," Econometrica, Econometric Society, vol. 78(3), pages 883-931, May.
  27. Nataliya Chukhrova & Arne Johannssen, 2017. "State Space Models and the K alman -Filter in Stochastic Claims Reserving: Forecasting, Filtering and Smoothing," Risks, MDPI, vol. 5(2), pages 1-23, May.
  28. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
  29. Andrew J Fieldhouse & Karel Mertens & Morten O Ravn, 2018. "The Macroeconomic Effects of Government Asset Purchases: Evidence from Postwar U.S. Housing Credit Policy," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(3), pages 1503-1560.
  30. Dordonnat, V. & Koopman, S.J. & Ooms, M. & Dessertaine, A. & Collet, J., 2008. "An hourly periodic state space model for modelling French national electricity load," International Journal of Forecasting, Elsevier, vol. 24(4), pages 566-587.
  31. Necmettin Alpay Koçak, 2020. "The Role of Ecb Speeches in Nowcasting German Gdp," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2020(2), pages 05-20.
  32. Ricardo Reis & Mark W. Watson, 2007. "Measuring Changes in the Value of the Numeraire," Working Papers 2007-7, Princeton University. Economics Department..
  33. Fiorentini, Gabriele & Galesi, Alessandro & Sentana, Enrique, 2018. "A spectral EM algorithm for dynamic factor models," Journal of Econometrics, Elsevier, vol. 205(1), pages 249-279.
  34. Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised May 2024.
  35. Michael Ho & Jack Xin, 2016. "Sparse Kalman Filtering Approaches to Covariance Estimation from High Frequency Data in the Presence of Jumps," Papers 1602.02185, arXiv.org, revised Apr 2016.
  36. Hasenzagl, Thomas & Pellegrino, Filippo & Reichlin, Lucrezia & Ricco, Giovanni, 2022. "Monitoring the Economy in Real Time: Trends and Gaps in Real Activity and Prices," CEPR Discussion Papers 17111, C.E.P.R. Discussion Papers.
  37. Matteo Barigozzi & Marc Hallin, 2024. "The Dynamic, the Static, and the Weak Factor Models and the Analysis of High-Dimensional Time Series," Working Papers ECARES 2024-14, ULB -- Universite Libre de Bruxelles.
  38. Delle Monache, Davide & Petrella, Ivan, 2019. "Efficient matrix approach for classical inference in state space models," Economics Letters, Elsevier, vol. 181(C), pages 22-27.
  39. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
  40. Alberto Caruso, 2015. "Nowcasting Mexican GDP," Working Papers ECARES ECARES 2015-40, ULB -- Universite Libre de Bruxelles.
  41. Byeongchan Seong & Sung K. Ahn & Peter Zadrozny, 2007. "Cointegration Analysis with Mixed-Frequency Data," CESifo Working Paper Series 1939, CESifo.
  42. Shalini Sharma & Víctor Elvira & Emilie Chouzenoux & Angshul Majumdar, 2021. "Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting," Post-Print hal-03184841, HAL.
  43. Mazzocchi, Mario, 2006. "Time patterns in UK demand for alcohol and tobacco: an application of the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2191-2205, May.
  44. André Nunes Maranhão & Nicole Rennó Castro, 2023. "Dissecting Brazilian agriculture business cycles in high-dimensional and time-irregular span contexts," Empirical Economics, Springer, vol. 65(4), pages 1543-1578, October.
  45. Majewski, Adam A. & Ciliberti, Stefano & Bouchaud, Jean-Philippe, 2020. "Co-existence of trend and value in financial markets: Estimating an extended Chiarella model," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
  46. Schulz, Rainer, 2002. "Real estate valuation according to standardized methods: An empirical analysis," SFB 373 Discussion Papers 2002,55, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  47. Zirogiannis, Nikolaos & Tripodis, Yorghos, 2014. "Dynamic Factor Analysis for Short Panels: Estimating Performance Trajectories for Water Utilities," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170592, Agricultural and Applied Economics Association.
  48. Giuseppe Storti & Cosimo Vitale, 2003. "BL-GARCH models and asymmetries in volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 19-39, February.
  49. Jacob Fein-Ashley, 2024. "A Comparison of Traditional and Deep Learning Methods for Parameter Estimation of the Ornstein-Uhlenbeck Process," Papers 2404.11526, arXiv.org, revised Apr 2024.
  50. Korobilis, Dimitris & Koop, Gary, 2018. "Variational Bayes inference in high-dimensional time-varying parameter models," Essex Finance Centre Working Papers 22665, University of Essex, Essex Business School.
  51. Lasse Bork, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," CREATES Research Papers 2009-11, Department of Economics and Business Economics, Aarhus University.
  52. Reddy, Chandan K. & Rajaratnam, Bala, 2010. "Learning mixture models via component-wise parameter smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 732-749, March.
  53. Jo Thori Lind, 2005. "Repeated surveys and the Kalman filter," Econometrics Journal, Royal Economic Society, vol. 8(3), pages 418-427, December.
  54. Pawel M. Krolikowski & Kurt G. Lunsford, 2024. "Advance layoff notices and aggregate job loss," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 462-480, April.
  55. Dumas, Bernard & Harvey, Campbell R. & Ruiz, Pierre, 2003. "Are correlations of stock returns justified by subsequent changes in national outputs?," Journal of International Money and Finance, Elsevier, vol. 22(6), pages 777-811, November.
  56. Hideaki Shimazaki & Shun-ichi Amari & Emery N Brown & Sonja Grün, 2012. "State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-27, March.
  57. T.P.Koirala, Ph.D., 2013. "Time-Varying Parameters of Inflation Model in Nepal: State Space Modeling," NRB Economic Review, Nepal Rastra Bank, Economic Research Department, vol. 25(2), pages 66-77, October.
  58. Krane, Spencer & Wascher, William, 1999. "The cyclical sensitivity of seasonality in U.S. employment," Journal of Monetary Economics, Elsevier, vol. 44(3), pages 523-553, December.
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  61. Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper, 2016. "Forecasting and nowcasting economic growth in the euro area using factor models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1284-1305.
  62. Marta Bañbura, 2009. "Essays in dynamic macroeconometrics," ULB Institutional Repository 2013/210294, ULB -- Universite Libre de Bruxelles.
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  64. Sbrana, Giacomo & Pelagatti, Matteo, 2024. "Optimal hierarchical EWMA forecasting," International Journal of Forecasting, Elsevier, vol. 40(2), pages 616-625.
  65. Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020. "Deep Dynamic Factor Models," Papers 2007.11887, arXiv.org, revised May 2023.
  66. Mazzocchi, Mario, 2004. "Food Scares and Demand Recovery Patterns: An Econometric Investigation," 84th Seminar, February 8-11, 2004, Zeist, The Netherlands 24990, European Association of Agricultural Economists.
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