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

Citations for "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination"

by Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C.

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. Peter Exterkate, 2012. "Model Selection in Kernel Ridge Regression," CREATES Research Papers 2012-10, School of Economics and Management, University of Aarhus.
  2. Gomes, Orlando, 2009. "Stability under learning: The endogenous growth problem," Economic Modelling, Elsevier, vol. 26(5), pages 807-816, September.
  3. Teddy, S.D. & Ng, S.K., 2011. "Forecasting ATM cash demands using a local learning model of cerebellar associative memory network," International Journal of Forecasting, Elsevier, vol. 27(3), pages 760-776, July.
  4. Kim, Sei-Wan & Mollick, André V. & Nam, Kiseok, 2008. "Common nonlinearities in long-horizon stock returns: Evidence from the G-7 stock markets," Global Finance Journal, Elsevier, vol. 19(1), pages 19-31.
  5. Yoon, Gawon, 2010. "Do real exchange rates really follow threshold autoregressive or exponential smooth transition autoregressive models?," Economic Modelling, Elsevier, vol. 27(2), pages 605-612, March.
  6. Giancarlo Bruno, 2008. "Forecasting Using Functional Coefficients Autoregressive Models," ISAE Working Papers 98, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
  7. Sermpinis, Georgios & Stasinakis, Charalampos & Dunis, Christian, 2014. "Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 30(C), pages 21-54.
  8. Guidolin, Massimo & Hyde, Stuart & McMillan, David & Ono, Sadayuki, 2009. "Non-linear predictability in stock and bond returns: When and where is it exploitable?," International Journal of Forecasting, Elsevier, vol. 25(2), pages 373-399.
  9. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen Miller, 2013. "Forecasting Nevada gross gaming revenue and taxable sales using coincident and leading employment indexes," Empirical Economics, Springer, vol. 44(2), pages 387-417, April.
  10. Ralf Becker & Denise R. Osborn, 2012. "Weighted Smooth Transition Regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 795-811, 08.
  11. Mihaela BRATU (SIMIONESCU), 2012. "A Strategy To Improve The Gdp Index Forcasts In Romania Using Moving Average Models Of Historical Errors Of The Dobrescu Macromodel," Romanian Journal of Economics, Institute of National Economy, vol. 35(2(44)), pages 128-138, December.
  12. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
  13. Peter Exterkate & Patrick J.F. Groenen & Christiaan Heij & Dick van Dijk, 2013. "Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression," CREATES Research Papers 2013-16, School of Economics and Management, University of Aarhus.
  14. Ana Beatriz Galv�o, 2007. "Changes in Predictive Ability with Mixed Frequency Data," Working Papers 595, Queen Mary University of London, School of Economics and Finance.
  15. Ubilava, David & Helmers, C Gustav, 2012. "Forecasting ENSO with a smooth transition autoregressive model," MPRA Paper 36890, University Library of Munich, Germany.
  16. Bruno, Giancarlo, 2012. "Consumer confidence and consumption forecast: a non-parametric approach," MPRA Paper 41312, University Library of Munich, Germany.
  17. repec:dgr:uvatin:20110138 is not listed on IDEAS
  18. Jonathan B. Hill, 2004. "Consistent and Non-Degenerate Model Specification Tests Against Smooth Transition Alternatives," Working Papers 0406, Florida International University, Department of Economics.
  19. repec:dgr:uvatin:2011138 is not listed on IDEAS
  20. Costas Milas & Philip Rothman, 2007. "Out-of-Sample Forecasting of Unemployment Rates with Pooled STVECM Forecasts," Working Paper Series 49-07, The Rimini Centre for Economic Analysis, revised Jul 2007.
  21. Florackis, Chris & Giorgioni, Gianluigi & Kostakis, Alexandros & Milas, Costas, 2014. "On stock market illiquidity and real-time GDP growth," Journal of International Money and Finance, Elsevier, vol. 44(C), pages 210-229.
  22. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 201230, University of Pretoria, Department of Economics.
  23. Balagtas, Joseph Valdes & Holt, Matthew T., 2006. "Unit Roots, TV-STARs, and the Commodity Terms of Trade: A Further Assessment of the Prebisch-Singer Hypothesis," 2006 Annual meeting, July 23-26, Long Beach, CA 21405, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  24. Vijverberg, Chu-Ping C., 2009. "A time deformation model and its time-varying autocorrelation: An application to US unemployment data," International Journal of Forecasting, Elsevier, vol. 25(1), pages 128-145.
  25. BRATU SIMIONESCU, Mihaela, 2012. "Two Quantitative Forecasting Methods For Macroeconomic Indicators In Czech Republic," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 3(1), pages 71-87.
  26. Francq, Christian & Horvath, Lajos & Zakoian, Jean-Michel, 2008. "Sup-tests for linearity in a general nonlinear AR(1) model when the supremum is taken over the full parameter space," MPRA Paper 16669, University Library of Munich, Germany.
  27. Mihaela Bratu, 2012. "A Strategy to Improve the Survey of Professional Forecasters (SPF) Predictions Using Bias-Corrected-Accelerated (BCA) Bootstrap Forecast Intervals," International Journal of Synergy and Research, ToKnowPress, vol. 1(2), pages 45-59.
  28. Collan, Mikael, 2004. "Giga-Investments: Modelling the Valuation of Very Large Industrial Real Investments," MPRA Paper 4328, University Library of Munich, Germany.
  29. Medeiros, Marcelo C. & McAleer, Michael & Slottje, Daniel & Ramos, Vicente & Rey-Maquieira, Javier, 2008. "An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals," Journal of Econometrics, Elsevier, vol. 147(2), pages 372-383, December.
  30. repec:dgr:uvatin:2011007 is not listed on IDEAS
  31. Emilio Zanetti Chini, 2013. "Generalizing smooth transition autoregressions," CEIS Research Paper 294, Tor Vergata University, CEIS, revised 25 Sep 2014.
  32. Anders Bredahl Kock & Timo Teräsvirta, 2011. "Forecasting Macroeconomic Variables using Neural Network Models and Three Automated Model Selection Techniques," CREATES Research Papers 2011-27, School of Economics and Management, University of Aarhus.
  33. Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer, vol. 96(1), pages 99-122, January.
  34. Nikolay Robinzonov & Klaus Wohlrabe, 2008. "Freedom of Choice in Macroeconomic Forecasting: An Illustration with German Industrial Production and Linear Models," Ifo Working Paper Series Ifo Working Paper No. 57, Ifo Institute for Economic Research at the University of Munich.
This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.