IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login

Citations for "Improving forecast accuracy by combining recursive and rolling forecasts"

by Todd E. Clark & Michael W. McCracken

For a complete description of this item, click here. For a RSS feed for citations of this item, click here.
as in new window

  1. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
  2. Chanont Banternghansa & Michael W. McCracken, 2011. "Real-time forecast averaging with ALFRED," Review, Federal Reserve Bank of St. Louis, issue Jan, pages 49-66.
  3. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip, 2008. "How Much Can Outlook Forecasts be Improved? An Application to the U.S. Hog Market," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37620, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
  4. Todd E. Clark & Michael W. McCracken, 2006. "Forecasting of small macroeconomic VARs in the presence of instabilities," Research Working Paper RWP 06-09, Federal Reserve Bank of Kansas City.
  5. Todd E. Clark & Michael W. McCracken, 2010. "Averaging forecasts from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 5-29.
  6. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, Elsevier.
  7. Calista Cheung & Frédérick Demers, 2007. "Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation," Staff Working Papers 07-8, Bank of Canada.
  8. Jungmittag, Andre, 2014. "Combination of forecasts across estimation windows: An application to air travel demand," Working Paper Series: Business and Law 05, Frankfurt University of Applied Sciences, Faculty of Business and Law.
  9. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
  10. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
  11. Pesaran, M.H. & Pick, A., 2008. "Forecasting Random Walks Under Drift Instability," Cambridge Working Papers in Economics 0814, Faculty of Economics, University of Cambridge.
  12. Tan, Pei P. & Galagedera, Don U.A. & Maharaj, Elizabeth A., 2012. "A wavelet based investigation of long memory in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2330-2341.
  13. Dimitrios D. Thomakos & Fotis Papailias, 2013. "Covariance Averaging for Improved Estimation and Portfolio Allocation," Working Paper Series 66_13, The Rimini Centre for Economic Analysis.
  14. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2014. "Combined Density Nowcasting in an Uncertain Economic Environment," Tinbergen Institute Discussion Papers 14-152/III, Tinbergen Institute.
  15. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2010. "Out-of-sample equity premium prediction: economic fundamentals vs. moving-average rules," Working Papers 2010-008, Federal Reserve Bank of St. Louis.
  16. Marco Valerio Geraci & Jean-Yves Gnabo, 2015. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying VARs," Working Papers ECARES 2015-51, ULB -- Universite Libre de Bruxelles.
  17. Leonardo Morales-Arias & Alexander Dross, 2010. "Adaptive Forecasting of Exchange Rates with Panel Data," Research Paper Series 285, Quantitative Finance Research Centre, University of Technology, Sydney.
  18. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
  19. Fabio Busetti & Juri Marcucci & Giovanni Veronese, 2009. "Comparing forecast accuracy: A Monte Carlo investigation," Temi di discussione (Economic working papers) 723, Bank of Italy, Economic Research and International Relations Area.
  20. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2011. "Nowcasting GDP in real-time: A density combination approach," Working Paper 2011/11, Norges Bank.
  21. Marie Bessec, 2010. "Etalonnages du taux de croissance du PIB français sur la base des enquêtes de conjoncture," Economie & Prévision, La Documentation Française, vol. 0(2), pages 77-99.
  22. Prasad S Bhattacharya & Dimitrios D Thomakos, 2011. "Improving forecasting performance by window and model averaging," Economics Series 2011_1, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  23. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, Elsevier.
  24. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
  25. Craig S. Hakkio, 2008. "PCE and CPI inflation differentials: converting inflation forecasts," Economic Review, Federal Reserve Bank of Kansas City, issue Q I, pages 51-68.
  26. Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, Open Access Journal, vol. 3(1), pages 2, January.
  27. Mihaela SIMIONESCU, 2014. "Improving The Inflation Rate Forecasts Of Romanian Experts Using A Fixed-Effects Models Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 13, pages 87-102, June.
  28. Johannes Mayr & Dirk Ulbricht, 2007. "VAR Model Averaging for Multi-Step Forecasting," Ifo Working Paper Series Ifo Working Paper No. 48, Ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  29. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2012. "Forecasting UK GDP growth, inflation and interest rates under structural change: a comparison of models with time-varying parameters," Bank of England working papers 450, Bank of England.
  30. Leung, Charles Ka Yui & Tang, Edward Chi Ho, 2014. "Availability, Affordability and Volatility: the case of Hong Kong Housing Market," MPRA Paper 58770, University Library of Munich, Germany.
  31. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip & Etienne, Xiaoli, 2012. "Composite and Outlook Forecast Accuracy," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(2), August.
  32. Ahmed, Shamim & Liu, Xiaoquan & Valente, Giorgio, 2016. "Can currency-based risk factors help forecast exchange rates?," International Journal of Forecasting, Elsevier, vol. 32(1), pages 75-97.
  33. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2011. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Working Papers CoFie-02-2011, Sim Kee Boon Institute for Financial Economics.
  34. Morales-Arias, Leonardo & Moura, Guilherme V., 2013. "Adaptive forecasting of exchange rates with panel data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 493-509.
  35. Todd E. Clark & Kenneth D. West, 2005. "Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference," NBER Technical Working Papers 0305, National Bureau of Economic Research, Inc.
  36. Ke Yang & Langnan Chen & Fengping Tian, 2015. "Realized Volatility Forecast of Stock Index Under Structural Breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(1), pages 57-82, 01.
  37. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
  38. Henzel, Steffen R. & Mayr, Johannes, 2013. "The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 1-24.
This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.