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Citations for "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks"

by Swanson, Norman R & White, Halbert

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  1. Francis X. Diebold & Canlin Li, 2002. "Forecasting the Term Structure of Government Bond Yields," Center for Financial Institutions Working Papers 02-34, Wharton School Center for Financial Institutions, University of Pennsylvania.
  2. Corina SAMAN, 2015. "Out-Of-Sample Forecasting Performance Of A Robust Neural Exchange Rate Model Of Ron/Usd," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 93-106, March.
  3. Martha Misas & Enrique López & Carlos Arango & Juan Nicolás Hernández, . "La Demanda de Efectivo en Colombia: Una Caja Negra a la Luz de las Redes Neuronales," Borradores de Economia 268, Banco de la Republica de Colombia.
  4. Qi, Min & Yang, Sha, 2003. "Forecasting consumer credit card adoption: what can we learn about the utility function?," International Journal of Forecasting, Elsevier, vol. 19(1), pages 71-85.
  5. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & Georgios P. Kouretas, 2006. "Regime switching and artificial neural network forecasting of the Cyprus Stock Exchange daily returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 11(4), pages 371-383.
  6. Oliver Blaskowitz & Helmut Herwartz, 2008. "Testing directional forecast value in the presence of serial correlation," SFB 649 Discussion Papers SFB649DP2008-073, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  7. PREMINGER, Arie & FRANCK, Raphael, . "Forecasting exchange rates: a robust regression approach," CORE Discussion Papers RP 1917, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  8. Corradi, Valentina & Swanson, Norman R., 2002. "A consistent test for nonlinear out of sample predictive accuracy," Journal of Econometrics, Elsevier, vol. 110(2), pages 353-381, October.
  9. Yvon Fauvel & Alain Paquet & Christian Zimmermann, 1999. "A Survey on Interest Rate Forecasting," Cahiers de recherche CREFE / CREFE Working Papers 87, CREFE, Université du Québec à Montréal.
  10. Lance J. Bachmeier & Norman R. Swanson, 2005. "Predicting Inflation: Does The Quantity Theory Help?," Economic Inquiry, Western Economic Association International, vol. 43(3), pages 570-585, July.
  11. Kiani, K.M., 2009. "Neural Networks to Detect Nonlinearities in Time Series: Analysis of Business Cycle in France and the United Kingdom," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 9(1).
  12. Blaskowitz, Oliver & Herwartz, Helmut, 2014. "Testing the value of directional forecasts in the presence of serial correlation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 30-42.
  13. repec:bdr:ensayo:v::y:2004:i:45:p:10-57 is not listed on IDEAS
  14. Bekdache, Basma, 2001. "Term Premia and the Maturity Composition of the Federal Debt: New Evidence from the Term Structure of Interest Rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(7), pages 519-39, November.
  15. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415, November.
  16. Qi, Min & Zhang, Guoqiang Peter, 2001. "An investigation of model selection criteria for neural network time series forecasting," European Journal of Operational Research, Elsevier, vol. 132(3), pages 666-680, August.
  17. M Sensier & M Artis & C R Birchenhall & D R Osborn, 2002. "Domestic and International Influences on Business Cycle Regimes in Europe," The School of Economics Discussion Paper Series 0202, Economics, The University of Manchester.
  18. Dilip M. Nachane & Jose G. Clavel, 2005. "Forecasting Interest Rates - A Comparative Assessment Of Some Second Generation Non-Linear Models," Finance Working Papers 22359, East Asian Bureau of Economic Research.
  19. Martha Misas Arango & Enrique López Enciso & Pablo Querubín Borrero, 2002. "La inflación en Colombia: una aproximación desde las redes neuronales," Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 20(41-42), pages 143-214, Junio-Dic.
  20. Angelos Kanas, 2003. "Non-linear forecasts of stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 299-315.
  21. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
  22. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, Department of Economics and Business Economics, Aarhus University.
  23. Richard G. Anderson & Jane M. Binner & Vincent A. Schmidt, 2011. "Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States," Working Papers 2011-007, Federal Reserve Bank of St. Louis.
  24. Granger, Clive W. J. & Swanson, Norman R., 1997. "An introduction to stochastic unit-root processes," Journal of Econometrics, Elsevier, vol. 80(1), pages 35-62, September.
  25. Kajal Lahiri & Liu Yang, 2012. "Forecasting Binary Outcomes," Discussion Papers 12-09, University at Albany, SUNY, Department of Economics.
  26. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, Elsevier.
  27. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
  28. Khurshid M. KIANI & Terry L. KASTENS, 2006. "Using Macro-Financial Variables To Forecast Recessions. An Analysis Of Canada, 1957-2002," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 6(3).
  29. Eric Ghysels & Norman R. Swanson & Myles Callan, 2002. "Monetary Policy Rules with Model and Data Uncertainty," Southern Economic Journal, Southern Economic Association, vol. 69(2), pages 239-265, October.
  30. Clive Bowsher & Roland Meeks, 2006. "High Dimensional Yield Curves: Models and Forecasting," Economics Papers 2006-W12, Economics Group, Nuffield College, University of Oxford.
  31. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen Miller, 2010. "Forecasting Nevada Gross Gaming Revenue and Taxable Sales Using Coincident and Leading Employment Indexes," Working Papers 15-01, Eastern Mediterranean University, Department of Economics.
  32. Corradi, Valentina & Swanson, Norman R. & Olivetti, Claudia, 2001. "Predictive ability with cointegrated variables," Journal of Econometrics, Elsevier, vol. 104(2), pages 315-358, September.
  33. Mayte Suarez -Farinas & Carlos E. Pedreira & Marcelo C. Medeiros, 2004. "Local Global Neural Networks: A New Approach for Nonlinear Time Series Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1092-1107, December.
  34. Huck, Nicolas, 2010. "Pairs trading and outranking: The multi-step-ahead forecasting case," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1702-1716, December.
  35. Bildirici, Melike & Ersin, Özgür, 2012. "Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models," MPRA Paper 40330, University Library of Munich, Germany, revised May 2012.
  36. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
  37. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
  38. Alfaro, Rodrigo & Becerra, Juan Sebastian & Sagner, Andres, 2010. "Estimación de la estructura de tasas utilizando el modelo Dinámico Nelson Siegel: resultados para Chile y EEUU
    [The Dynamic Nelson-Siegel model: empirical results for Chile and US]
    ," MPRA Paper 25912, University Library of Munich, Germany, revised 23 Jun 2010.
  39. Martha Alicia Misasarango & Enrique Antonio Lopezenciso & Carlos Arango & Juan Nicolashernandez, 2004. "No-Linealidades En La Demanada De Efectivo En Colombia: Las Redes Neuronales Como Herramienta De Pronostico," ENSAYOS SOBRE POLÍTICA ECONÓMICA, BANCO DE LA REPÚBLICA - ESPE, vol. 22(45), pages 10-57, June.
  40. Kanas, Angelos & Yannopoulos, Andreas, 2001. "Comparing linear and nonlinear forecasts for stock returns," International Review of Economics & Finance, Elsevier, vol. 10(4), pages 383-398, December.
  41. Bildirici, Melike & Alp, Aykaç, 2008. "The Relationship Between Wages and Productivity: TAR Unit Root and TAR Cointegration Approach," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 5(1), pages 93-110.
  42. Krishna, Kala & Ozyildirim, Ataman & Swanson, Norman R., 2003. "Trade, investment and growth: nexus, analysis and prognosis," Journal of Development Economics, Elsevier, vol. 70(2), pages 479-499, April.
  43. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
  44. Eisinga, R. & Franses, Ph.H.B.F. & van Dijk, D.J.C., 1997. "Timing of Vote Decision in First and Second Order Dutch Elections 1978-1995: Evidence from Artificial Neural Networks," Econometric Institute Research Papers EI 9733/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  45. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
  46. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
  47. Khurshid Kiani & Terry Kastens, 2008. "Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures," Computational Economics, Society for Computational Economics, vol. 32(4), pages 383-406, November.
  48. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
  49. Timmermann, Allan, 2008. "Elusive return predictability," International Journal of Forecasting, Elsevier, vol. 24(1), pages 1-18.
  50. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
  51. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Regime Switching and Artificial Neural Network Forecasting," Working Papers 0502, University of Crete, Department of Economics.
  52. McAleer, Michael & Medeiros, Marcelo C. & Slottje, Daniel, 2008. "A neural network demand system with heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 147(2), pages 359-371, December.
  53. Norman Swanson & Oleg Korenok, 2006. "How Sticky Is Sticky Enough? A Distributional and Impulse Response Analysis of New Keynesian DSGE Models. Extended Working Paper Version," Departmental Working Papers 200612, Rutgers University, Department of Economics.
  54. Kiani, Khurshid M., 2016. "On business cycle fluctuations in USA macroeconomic time series," Economic Modelling, Elsevier, vol. 53(C), pages 179-186.
  55. Ramazan Gencay & Aslihan Salih, 2003. "Degree of Mispricing with the Black-Scholes Model and Nonparametric Cures," Annals of Economics and Finance, Society for AEF, vol. 4(1), pages 73-101, May.
  56. Garcia, Rene & Gencay, Ramazan, 2000. "Pricing and hedging derivative securities with neural networks and a homogeneity hint," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 93-115.
  57. Khurshid Kiani, 2011. "Fluctuations in Economic and Activity and Stabilization Policies in the CIS," Computational Economics, Society for Computational Economics, vol. 37(2), pages 193-220, February.
  58. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
  59. Yang, Jian & Cabrera, Juan & Wang, Tao, 2010. "Nonlinearity, data-snooping, and stock index ETF return predictability," European Journal of Operational Research, Elsevier, vol. 200(2), pages 498-507, January.
  60. Qi, Min, 2001. "Predicting US recessions with leading indicators via neural network models," International Journal of Forecasting, Elsevier, vol. 17(3), pages 383-401.
  61. Vroomen, B.L.K. & Franses, Ph.H.B.F. & van Nierop, J.E.M., 2001. "Modeling Consideration Sets and Brand Choice Using Artificial Neural Networks," ERIM Report Series Research in Management ERS-2001-10-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  62. Juan Reboredo & José Matías & Raquel Garcia-Rubio, 2012. "Nonlinearity in Forecasting of High-Frequency Stock Returns," Computational Economics, Society for Computational Economics, vol. 40(3), pages 245-264, October.
  63. Ferland, Rene & Lalancette, Simon, 2006. "Dynamics of realized volatilities and correlations: An empirical study," Journal of Banking & Finance, Elsevier, vol. 30(7), pages 2109-2130, July.
  64. Oliver Blaskowitz & Helmut Herwatz, 2008. "Adaptive Forecasting of the EURIBOR Swap Term Structure," SFB 649 Discussion Papers SFB649DP2008-017, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  65. Arifovic, Jasmina & Gençay, Ramazan, 2001. "Using genetic algorithms to select architecture of a feedforward artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 289(3), pages 574-594.
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