A note on multi-step forecasting with functional coefficient autoregressive models
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References listed on IDEAS
- Clements, Michael P. & Smith, Jeremy, 1997. "The performance of alternative forecasting methods for SETAR models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 463-475, December.
- Potter, Simon M, 1995. "A Nonlinear Approach to US GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 109-125, April-Jun.
- Cai, Zongwu & Fan, Jianqing & Yao, Qiwei, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
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- Bruno, Giancarlo, 2008.
"Forecasting Using Functional Coefficients Autoregressive Models,"
42335, University Library of Munich, Germany.
- Giancarlo Bruno, 2008. "Forecasting Using Functional Coefficients Autoregressive Models," ISAE Working Papers 98, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
- De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
- Giancarlo Bruno, 2014.
"Consumer confidence and consumption forecast: a non-parametric approach,"
Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(1), pages 37-52, February.
- Bruno, Giancarlo, 2012. "Consumer confidence and consumption forecast: a non-parametric approach," MPRA Paper 41312, University Library of Munich, Germany.
- repec:gam:jsusta:v:10:y:2018:i:6:p:1691-:d:148435 is not listed on IDEAS
- Patrick, Joshua D. & Harvill, Jane L. & Hansen, Clifford W., 2016. "A semiparametric spatio-temporal model for solar irradiance data," Renewable Energy, Elsevier, vol. 87(P1), pages 15-30.
- Bruno, Giancarlo, 2009.
"Non-linear relation between industrial production and business surveys data,"
42337, University Library of Munich, Germany.
- Giancarlo Bruno, 2009. "Non-linear relation between industrial production and business surveys data," ISAE Working Papers 119, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
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