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Computing Observation Weights for Signal Extraction and Filtering

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Cited by:

  1. Drew Creal & Siem Jan Koopman & Eric Zivot, 2008. "The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model," Tinbergen Institute Discussion Papers 08-069/4, Tinbergen Institute.
  2. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024.
  3. Beatrice Pierluigi & Jan Bruha & Roberta Serafini, 2014. "Euro area labour markets: Different reaction to shocks?," Journal of Banking and Financial Economics, University of Warsaw, Faculty of Management, vol. 2(2), pages 34-60, November.
  4. 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.
  5. Irma Hindrayanto & Siem Jan Koopman & Jasper de Winter, 2014. "Nowcasting and Forecasting Economic Growth in the Euro Area using Principal Components," Tinbergen Institute Discussion Papers 14-113/III, Tinbergen Institute.
  6. Banbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, vol. 27(2), pages 333-346, April.
  7. Fabio Busetti, 2001. "The use of preliminary data in econometric forecasting: an application with the Bank of Italy Quarterly Model," Temi di discussione (Economic working papers) 437, Bank of Italy, Economic Research and International Relations Area.
  8. Gianfreda, Angelica & Maranzano, Paolo & Parisio, Lucia & Pelagatti, Matteo, 2023. "Testing for integration and cointegration when time series are observed with noise," Economic Modelling, Elsevier, vol. 125(C).
  9. Robert J. Hill & Alicia N. Rambaldi & Michael Scholz, 2021. "Higher frequency hedonic property price indices: a state-space approach," Empirical Economics, Springer, vol. 61(1), pages 417-441, July.
  10. Thomas M. Trimbur, 2006. "Detrending economic time series: a Bayesian generalization of the Hodrick-Prescott filter," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(4), pages 247-273.
  11. Claudio BorioBy & Piti Disyatat & Mikael Juselius, 2017. "Rethinking potential output: embedding information about the financial cycle," Oxford Economic Papers, Oxford University Press, vol. 69(3), pages 655-677.
  12. Jan Brùha, 2011. "An Empirical Small Labor Market Model for the Czech Economy," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(5), pages 434-449, November.
  13. William R. Bell & Donald E. K. Martin, 2004. "Computation of asymmetric signal extraction filters and mean squared error for ARIMA component models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(4), pages 603-623, July.
  14. Elena Angelini & Gonzalo Camba‐Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2011. "Short‐term forecasts of euro area GDP growth," Econometrics Journal, Royal Economic Society, vol. 14, pages 25-44, February.
  15. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
  16. 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.
  17. Andrés González Gómez & Lavan Mahadeva & Diego Rodríguez & Luis Eduardo Rojas, 2009. "Monetary Policy Forecasting in a DSGE Model with Data that is Uncertain, Unbalanced and About the Future," Borradores de Economia 559, Banco de la Republica de Colombia.
  18. Scotti, Chiara, 2016. "Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 1-19.
  19. Alicia N. Rambaldi & Ryan R. J. McAllister & Cameron S. Fletcher, 2015. "Decoupling land values in residential property prices: smoothing methods for hedonic imputed price indices," Discussion Papers Series 549, School of Economics, University of Queensland, Australia.
  20. Vegard H�ghaug Larsen & Leif Anders Thorsrud, 2018. "Business cycle narratives," Working Papers No 6/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  21. 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.
  22. Claudio BorioBy & Piti Disyatat & Mikael Juselius, 2017. "Rethinking potential output: embedding information about the financial cycle," Oxford Economic Papers, Oxford University Press, vol. 69(3), pages 655-677.
  23. Andrew C. Harvey & Thomas M. Trimbur, 2003. "General Model-Based Filters for Extracting Cycles and Trends in Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 244-255, May.
  24. Siem Jan Koopman & Marius Ooms & Irma Hindrayanto, 2009. "Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 683-713, October.
  25. Luis E. Rojas, 2011. "Professional Forecasters: How to Understand and Exploit Them Through a DSGE Model," Borradores de Economia 8945, Banco de la Republica.
  26. Deicy J. Cristiano-Botia & Manuel Dario Hernandez-Bejarano & Mario A. Ramos-Veloza, 2021. "Labor Market Indicator for Colombia (LMI)," Borradores de Economia 1152, Banco de la Republica de Colombia.
  27. Danilo Leiva-Leon & Lorenzo Ductor, 2019. "Fluctuations in Global Macro Volatility," ThE Papers 19/09, Department of Economic Theory and Economic History of the University of Granada..
  28. Koopman, Siem Jan & Ooms, Marius, 2006. "Forecasting daily time series using periodic unobserved components time series models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 885-903, November.
  29. Fabio Busetti, 2006. "Preliminary data and econometric forecasting: an application with the Bank of Italy Quarterly Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 1-23.
  30. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
  31. Alain Galli, 2018. "Which Indicators Matter? Analyzing the Swiss Business Cycle Using a Large-Scale Mixed-Frequency Dynamic Factor Model," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(2), pages 179-218, November.
  32. Mahadeva, Lavan, 2007. "Monetary Policy and Data Uncertainty: A Case Study of Distribution, Hotels and Catering Growth," Discussion Papers 19, Monetary Policy Committee Unit, Bank of England.
  33. Weinert, Howard L., 2007. "Efficient computation for Whittaker-Henderson smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 959-974, October.
  34. Terence Mills, 2007. "A Note on Trend Decomposition: The 'Classical' Approach Revisited with an Application to Surface Temperature Trends," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(8), pages 963-972.
  35. Dias, Maria Helena Ambrosio & Dias, Joilson, 2010. "Measuring the Cyclical Component of a Time Series: a New Proposed Methodology," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(1), October.
  36. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
  37. Tommaso Proietti, 2006. "Measuring Core Inflation by Multivariate Structural Time Series Models," CEIS Research Paper 83, Tor Vergata University, CEIS.
  38. Alessandro Barbarino & Travis J. Berge & Han Chen & Andrea Stella, 2020. "Which Output Gap Estimates Are Stable in Real Time and Why?," Finance and Economics Discussion Series 2020-102, Board of Governors of the Federal Reserve System (U.S.).
  39. Roberto Iannaccone & Edoardo Otranto, 2003. "Signal Extraction in Continuous Time and the Generalized Hodrick- Prescott Filter," Econometrics 0311002, University Library of Munich, Germany.
  40. Alain Galli, 2018. "Which Indicators Matter? Analyzing the Swiss Business Cycle Using a Large-Scale Mixed-Frequency Dynamic Factor Model," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(2), pages 179-218, November.
  41. Harvey, Andrew & Oryshchenko, Vitaliy, 2012. "Kernel density estimation for time series data," International Journal of Forecasting, Elsevier, vol. 28(1), pages 3-14.
  42. Thomas Gilbert & Chiara Scotti & Georg H. Strasser & Clara Vega, 2015. "Is the Intrinsic Value of Macroeconomic News Announcements Related to Their Asset Price Impact?," Boston College Working Papers in Economics 874, Boston College Department of Economics, revised 23 Apr 2015.
  43. 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.
  44. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2017. "Using the payment system data to forecast the Italian GDP," Temi di discussione (Economic working papers) 1098, Bank of Italy, Economic Research and International Relations Area.
  45. Claudio Borio & Piti Disyatat & Mikael Juselius, 2014. "A parsimonious approach to incorporating economic information in measures of potential output," BIS Working Papers 442, Bank for International Settlements.
  46. Itkonen, Juha & Juvonen, Petteri, 2017. "Nowcasting the Finnish economy with a large Bayesian vector autoregressive model," BoF Economics Review 6/2017, Bank of Finland.
  47. Elena Angelini & Marta Banbura & Gerhard Rünstler, 2010. "Estimating and forecasting the euro area monthly national accounts from a dynamic factor model," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(1), pages 1-22.
  48. Gilbert, Thomas & Scotti, Chiara & Strasser, Georg & Vega, Clara, 2017. "Is the intrinsic value of a macroeconomic news announcement related to its asset price impact?," Journal of Monetary Economics, Elsevier, vol. 92(C), pages 78-95.
  49. Tallman, Ellis W. & Zaman, Saeed, 2017. "Forecasting inflation: Phillips curve effects on services price measures," International Journal of Forecasting, Elsevier, vol. 33(2), pages 442-457.
  50. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
  51. Charles S. Bos & Siem Jan Koopman, 2010. "Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production," Tinbergen Institute Discussion Papers 10-017/4, Tinbergen Institute.
  52. Ductor, Lorenzo & Leiva-León, Danilo, 2022. "Fluctuations in global output volatility," Journal of International Money and Finance, Elsevier, vol. 120(C).
  53. Tóth, Máté, 2021. "A multivariate unobserved components model to estimate potential output in the euro area: a production function based approach," Working Paper Series 2523, European Central Bank.
  54. Wegmüller, Philipp & Glocker, Christian & Guggia, Valentino, 2023. "Weekly economic activity: Measurement and informational content," International Journal of Forecasting, Elsevier, vol. 39(1), pages 228-243.
  55. Truong, Chi & Sheen, Jeffrey & Trück, Stefan & Villafuerte, James, 2022. "Early warning systems using dynamic factor models: An application to Asian economies," Journal of Financial Stability, Elsevier, vol. 58(C).
  56. Marc Francke, 2010. "Repeat Sales Index for Thin Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 41(1), pages 24-52, July.
  57. Andrew Harvey, 2010. "The local quadratic trend model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 94-108.
  58. Nicholas Sander, 2013. "Fresh perspectives on unobservable variables: Data decomposition of the Kalman smoother," Reserve Bank of New Zealand Analytical Notes series AN2013/09, Reserve Bank of New Zealand.
  59. Carlos Cuerpo & Ángel Cuevas & Enrique M. Quilis, 2018. "Estimating output gap: a beauty contest approach," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 9(3), pages 275-304, August.
  60. D.S. Prasada Rao & Alicia N. Rambaldi & K. Renuka Ganegodage & L. T. Huynh & Howard E. Doran, 2017. "UQICD v2.1.2 User Guide," Discussion Papers Series 534, School of Economics, University of Queensland, Australia.
  61. Sbrana, Giacomo & Silvestrini, Andrea, 2022. "Random coefficient state-space model: Estimation and performance in M3–M4 competitions," International Journal of Forecasting, Elsevier, vol. 38(1), pages 352-366.
  62. Andrle, Michal, 2012. "Understanding DSGE Filters in Forecasting and Policy Analysis," Dynare Working Papers 16, CEPREMAP.
  63. Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 935-958, October.
  64. Stéphanie Guichard & Elena Rusticelli, 2011. "A Dynamic Factor Model for World Trade Growth," OECD Economics Department Working Papers 874, OECD Publishing.
  65. Long, Wen & Zhao, Manyi & Tang, Yeran, 2021. "Can the Chinese volatility index reflect investor sentiment?," International Review of Financial Analysis, Elsevier, vol. 73(C).
  66. Alicia N. Rambaldi & Cameron S. Fletcher, 2014. "Hedonic Imputed Property Price Indexes: The Effects of Econometric Modeling Choices," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(S2), pages 423-448, November.
  67. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
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