IDEAS home Printed from https://ideas.repec.org/r/bes/jnlbes/v11y1993i2p143-44.html
   My bibliography  Save this item

Calculating Interval Forecasts: Reply

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Borbély, Dóra & Meier, Carsten-Patrick, 2003. "Macroeconomic interval forecasting: the case of assessing the risk of deflation in Germany," Kiel Working Papers 1153, Kiel Institute for the World Economy (IfW Kiel).
  2. Shang, Han Lin & Hyndman, Rob.J., 2011. "Nonparametric time series forecasting with dynamic updating," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1310-1324.
  3. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
  4. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
  5. Kim, Jae H. & Wong, Kevin & Athanasopoulos, George & Liu, Shen, 2011. "Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals," International Journal of Forecasting, Elsevier, vol. 27(3), pages 887-901.
  6. 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, January.
  7. Gourieroux, C. & Monfort, A., 2021. "Model risk management: Valuation and governance of pseudo-models," Econometrics and Statistics, Elsevier, vol. 17(C), pages 1-22.
  8. Shang, Han Lin & Haberman, Steven, 2017. "Grouped multivariate and functional time series forecasting:An application to annuity pricing," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 166-179.
  9. Charles, Amelie & Darne, Olivier & Kim, Jae, 2016. "Stock Return Predictability: Evaluation based on Prediction Intervals," MPRA Paper 70143, University Library of Munich, Germany.
  10. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, "undated". "Evaluating Density Forecasts," CARESS Working Papres 97-18, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
  11. Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "Nearest-neighbor methodology for prediction of intra-hour global horizontal and direct normal irradiances," Renewable Energy, Elsevier, vol. 80(C), pages 770-782.
  12. Dimingo, Roselyn & Muteba Mwamba, John W. & Bonga-Bonga, Lumengo, 2021. "Prediction of Stock Market Direction: Application of Machine Learning Models," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 74(4), pages 499-536.
  13. Jae H. Kim, 2004. "Bias-corrected bootstrap prediction regions for vector autoregression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 141-154.
  14. Pascual, Lorenzo & Romo, Juan & Ruiz, Esther, 2005. "Bootstrap prediction intervals for power-transformed time series," International Journal of Forecasting, Elsevier, vol. 21(2), pages 219-235.
  15. Sizhe Chen & Han Lin Shang & Yang Yang, 2025. "Is the age pension in Australia sustainable and fair? Evidence from forecasting the old-age dependency ratio using the Hamilton-Perry model," Journal of Population Research, Springer, vol. 42(1), pages 1-27, March.
  16. Griffiths, William E. & Newton, Lisa S. & O'Donnell, Christopher J., 2010. "Predictive densities for models with stochastic regressors and inequality constraints: Forecasting local-area wheat yield," International Journal of Forecasting, Elsevier, vol. 26(2), pages 397-412, April.
  17. repec:lan:wpaper:539557 is not listed on IDEAS
  18. Clements, Michael P. & Taylor, Nick, 2001. "Bootstrapping prediction intervals for autoregressive models," International Journal of Forecasting, Elsevier, vol. 17(2), pages 247-267.
  19. Berrin Aytac & S. Wu, 2013. "Characterization of demand for short life-cycle technology products," Annals of Operations Research, Springer, vol. 203(1), pages 255-277, March.
  20. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
  21. Pascual, Lorenzo & Romo, Juan & Ruiz, Esther, 2001. "Effects of parameter estimation on prediction densities: a bootstrap approach," International Journal of Forecasting, Elsevier, vol. 17(1), pages 83-103.
  22. Dhaoui, Iyad, 2015. "Climat des Affaires et Compétitivité de l’Entreprise Tunisienne Après la Révolution : Analyses et Perspectives [Business Climate and Competitiveness of the Tunisian Enterprise After the Revolution:," MPRA Paper 87331, University Library of Munich, Germany.
  23. Francis X. Diebold & Jinyong Hahn & Anthony S. Tay, 1998. "Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange," Center for Financial Institutions Working Papers 99-05, Wharton School Center for Financial Institutions, University of Pennsylvania.
  24. Saoud, Patrick & Kourentzes, Nikolaos & Boylan, John E., 2022. "Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation," Omega, Elsevier, vol. 110(C).
  25. repec:ntu:ntugeo:vol2-iss1-14-054 is not listed on IDEAS
  26. Wolfgang Nierhaus, 2017. "Wirtschaftskonjunktur 2016: Prognose und Wirklichkeit," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 70(02), pages 72-78, January.
  27. Wolfgang Nierhaus, 2013. "Konjunkturprognosen heute – Möglichkeiten und Probleme," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(01), pages 25-32, January.
  28. Wolfgang Nierhaus, 2019. "Wirtschaftskonjunktur 2018: Prognose und Wirklichkeit," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 72(03), pages 22-29, February.
  29. Maheu, John M. & McCurdy, Thomas H., 2000. "Volatility dynamics under duration-dependent mixing," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 345-372, November.
  30. Elena-Ivona DUMITRESCU & Christophe HURLIN & Jaouad MADKOUR, 2011. "Testing Interval Forecasts: A New GMM-based Test," LEO Working Papers / DR LEO 1549, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
  31. Felix Wick & Ulrich Kerzel & Martin Hahn & Moritz Wolf & Trapti Singhal & Daniel Stemmer & Jakob Ernst & Michael Feindt, 2021. "Demand Forecasting of Individual Probability Density Functions with Machine Learning," SN Operations Research Forum, Springer, vol. 2(3), pages 1-39, September.
  32. Chan, W.S & Cheung, S.H & Wu, K.H, 2004. "Multiple forecasts with autoregressive time series models: case studies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(3), pages 421-430.
  33. Sinan Q. Salih & Intisar Alakili & Ufuk Beyaztas & Shamsuddin Shahid & Zaher Mundher Yaseen, 2021. "Prediction of dissolved oxygen, biochemical oxygen demand, and chemical oxygen demand using hydrometeorological variables: case study of Selangor River, Malaysia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(5), pages 8027-8046, May.
  34. Clements Michael P. & Hendry David F., 2008. "Economic Forecasting in a Changing World," Capitalism and Society, De Gruyter, vol. 3(2), pages 1-20, October.
  35. Yan, Jie & Liu, Yongqian & Han, Shuang & Wang, Yimei & Feng, Shuanglei, 2015. "Reviews on uncertainty analysis of wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1322-1330.
  36. Guy Melard & Jean-Michel Pasteels, 2000. "Automatic ARIMA modeling including interventions, using time series expert software," ULB Institutional Repository 2013/13744, ULB -- Universite Libre de Bruxelles.
  37. Sulandari, Winita & Subanar, & Lee, Muhammad Hisyam & Rodrigues, Paulo Canas, 2020. "Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks," Energy, Elsevier, vol. 190(C).
  38. Wolfgang Nierhaus, 2006. "Wirtschaftskonjunktur 2005: Prognose und Wirklichkeit," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 59(02), pages 37-43, January.
  39. Silvano Bordignon & Francesco Lisi, 2001. "Interval prediction for chaotic time series," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3-4), pages 117-140.
  40. Lauren K. Fine & Stephen K. McNees, 1994. "Diversity, uncertainty, and accuracy of inflation forecasts," New England Economic Review, Federal Reserve Bank of Boston, issue Jul, pages 33-44.
  41. Snyder, Ralph D. & Koehler, Anne B. & Hyndman, Rob J. & Ord, J. Keith, 2004. "Exponential smoothing models: Means and variances for lead-time demand," European Journal of Operational Research, Elsevier, vol. 158(2), pages 444-455, October.
  42. Li, Yushu & Andersson, Jonas, 2014. "A Likelihood Ratio and Markov Chain Based Method to Evaluate Density Forecasting," Discussion Papers 2014/12, Norwegian School of Economics, Department of Business and Management Science.
  43. Wolfgang Nierhaus, 2018. "Wirtschaftskonjunktur 2017: Prognose und Wirklichkeit," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(03), pages 35-42, February.
  44. Dhaoui, Elwardi, 2015. "Climat des Affaires et Compétitivité de l’Entreprise Tunisienne Après la Révolution : Analyses et Perspectives [Business Climate and Competitiveness of the Tunisian Enterprise After the Revolution:," MPRA Paper 70675, University Library of Munich, Germany.
  45. Diego Fresoli, 2022. "Bootstrap VAR forecasts: The effect of model uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 279-293, March.
  46. Klaus Abberger, 2006. "Kernel smoothed prediction intervals for ARMA models," Statistical Papers, Springer, vol. 47(1), pages 1-15, January.
  47. repec:lan:wpaper:413 is not listed on IDEAS
  48. Melard, G. & Pasteels, J. -M., 2000. "Automatic ARIMA modeling including interventions, using time series expert software," International Journal of Forecasting, Elsevier, vol. 16(4), pages 497-508.
  49. Ashkan Zarnani & Soheila Karimi & Petr Musilek, 2019. "Quantile Regression and Clustering Models of Prediction Intervals for Weather Forecasts: A Comparative Study," Forecasting, MDPI, vol. 1(1), pages 1-20, October.
  50. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
  51. Wolfgang Nierhaus, 2015. "Wirtschaftskonjunktur 2014: Prognose und Wirklichkeit," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 68(02), pages 43-49, January.
  52. Roberto Buizza & James W. Taylor, 2004. "A comparison of temperature density forecasts from GARCH and atmospheric models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(5), pages 337-355.
  53. M. Chudý & S. Karmakar & W. B. Wu, 2020. "Long-term prediction intervals of economic time series," Empirical Economics, Springer, vol. 58(1), pages 191-222, January.
  54. Chen, Yi-Hsuan & Tu, Anthony H., 2013. "Estimating hedged portfolio value-at-risk using the conditional copula: An illustration of model risk," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 514-528.
  55. Andrés Alonso & Daniel Peña & Juan Romo, 2006. "Introducing model uncertainty by moving blocks bootstrap," Statistical Papers, Springer, vol. 47(2), pages 167-179, March.
  56. Jing Li, 2021. "Block bootstrap prediction intervals for parsimonious first‐order vector autoregression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 512-527, April.
  57. Clements, Michael P. & Kim, Jae H., 2007. "Bootstrap prediction intervals for autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3580-3594, April.
  58. Caspar V. C. Geelen & Doekle R. Yntema & Jaap Molenaar & Karel J. Keesman, 2021. "Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1183-1196, March.
  59. Bratu, Mihaela, 2013. "The Assessment And Improvement Of The Accuracy For The Forecast Intervals," Working Papers of Macroeconomic Modelling Seminar 132602, Institute for Economic Forecasting.
  60. Annika Homburg & Christian H. Weiß & Layth C. Alwan & Gabriel Frahm & Rainer Göb, 2021. "A performance analysis of prediction intervals for count time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 603-625, July.
  61. Jing, Li, 2009. "Bootstrap prediction intervals for threshold autoregressive models," MPRA Paper 13086, University Library of Munich, Germany.
  62. Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004. "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, Elsevier, vol. 20(2), pages 169-183.
  63. Griffiths, William E. & Newton, Lisa S. & O'Donnell, Christopher J., 2001. "Predictive Densities for Shire Level Wheat Yield in Western Australia," 2001 Conference (45th), January 23-25, 2001, Adelaide, Australia 125645, Australian Agricultural and Resource Economics Society.
  64. Daniel W. Apley & Hyun Cheol Lee, 2010. "The effects of model parameter deviations on the variance of a linearly filtered time series," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(5), pages 460-471, August.
  65. Yushu Li & Jonas Andersson, 2020. "A likelihood ratio and Markov chain‐based method to evaluate density forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 47-55, January.
  66. James W. Taylor & Derek W. Bunn, 1999. "A Quantile Regression Approach to Generating Prediction Intervals," Management Science, INFORMS, vol. 45(2), pages 225-237, February.
  67. Guillaume Chevillon, 2007. "Direct Multi‐Step Estimation And Forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 746-785, September.
  68. Wolfgang Nierhaus, 2020. "Wirtschaftskonjunktur 2019: Prognose und Wirklichkeit," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 73(01), pages 51-57, January.
  69. Phathutshedzo Mpfumali & Caston Sigauke & Alphonce Bere & Sophie Mulaudzi, 2019. "Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data," Energies, MDPI, vol. 12(18), pages 1-28, September.
  70. Taylor, James W., 2007. "Forecasting daily supermarket sales using exponentially weighted quantile regression," European Journal of Operational Research, Elsevier, vol. 178(1), pages 154-167, April.
  71. Vahid Nourani & Nardin Jabbarian Paknezhad & Hitoshi Tanaka, 2021. "Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
  72. Polanski, Arnold & Stoja, Evarist, 2012. "Efficient evaluation of multidimensional time-varying density forecasts, with applications to risk management," International Journal of Forecasting, Elsevier, vol. 28(2), pages 343-352.
  73. Kim, Jae H., 1999. "Asymptotic and bootstrap prediction regions for vector autoregression," International Journal of Forecasting, Elsevier, vol. 15(4), pages 393-403, October.
  74. Philip Hans Franses, 2018. "Prediction Intervals For Expert-Adjusted Forecasts," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 308-320, December.
  75. Amendola, Alessandra & Christian, Francq, 2009. "Concepts and tools for nonlinear time series modelling," MPRA Paper 15140, University Library of Munich, Germany.
  76. J. Scott Armstrong & Ruth Pagell, 2003. "The Ombudsman: Reaping Benefits from Management Research: Lessons from the Forecasting Principles Project," Interfaces, INFORMS, vol. 33(6), pages 91-111, December.
  77. Han Lin Shang & Yang Yang & Fearghal Kearney, 2019. "Intraday forecasts of a volatility index: functional time series methods with dynamic updating," Annals of Operations Research, Springer, vol. 282(1), pages 331-354, November.
  78. Ord, J. Keith, 2022. "The uncertainty track: Machine learning, statistical modeling, synthesis," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1526-1530.
  79. 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.
  80. Taylor, James W. & Bunn, Derek W., 1999. "Investigating improvements in the accuracy of prediction intervals for combinations of forecasts: A simulation study," International Journal of Forecasting, Elsevier, vol. 15(3), pages 325-339, July.
  81. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332, April.
  82. Quan, Hao & Srinivasan, Dipti & Khosravi, Abbas, 2014. "Uncertainty handling using neural network-based prediction intervals for electrical load forecasting," Energy, Elsevier, vol. 73(C), pages 916-925.
  83. Lee, Yun Shin & Scholtes, Stefan, 2014. "Empirical prediction intervals revisited," International Journal of Forecasting, Elsevier, vol. 30(2), pages 217-234.
  84. Cheng, R. & Pourahmadi, M., 1997. "Prediction with incomplete past and interpolation of missing values," Statistics & Probability Letters, Elsevier, vol. 33(4), pages 341-346, May.
  85. Mihaela Simionescu, 2014. "M1 and M2 indicators- new proposed measures for the global accuracy of forecast intervals," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 2(1), pages 54-59, June.
  86. Ly, Sel & Xie, Jiahang & Wolter, Franz-Erich & Nguyen, Hung D. & Weng, Yu, 2023. "T-shape data and probabilistic remaining useful life prediction for Li-ion batteries using multiple non-crossing quantile long short-term memory," Applied Energy, Elsevier, vol. 349(C).
  87. Harvey, David I. & Newbold, Paul, 2003. "The non-normality of some macroeconomic forecast errors," International Journal of Forecasting, Elsevier, vol. 19(4), pages 635-653.
  88. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number hsbook0601, December.
  89. Shang, Han Lin, 2017. "Functional time series forecasting with dynamic updating: An application to intraday particulate matter concentration," Econometrics and Statistics, Elsevier, vol. 1(C), pages 184-200.
  90. Ling He & Chenyi Hu, 2010. "Midpoint method and accuracy of variability forecasting," Empirical Economics, Springer, vol. 38(3), pages 705-715, June.
  91. Mirakyan, Atom & Meyer-Renschhausen, Martin & Koch, Andreas, 2017. "Composite forecasting approach, application for next-day electricity price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 228-237.
  92. Hansen, Bruce E., 2006. "Interval forecasts and parameter uncertainty," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 377-398.
  93. Veiga, Helena, 2015. "Model uncertainty and the forecast accuracy of ARMA models: A survey," DES - Working Papers. Statistics and Econometrics. WS ws1508, Universidad Carlos III de Madrid. Departamento de Estadística.
  94. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332.
  95. Annette Hofmann & Cristina Sattarhoff, 2023. "Underwriting Cycles in Property-Casualty Insurance: The Impact of Catastrophic Events," Risks, MDPI, vol. 11(4), pages 1-25, April.
  96. repec:lan:wpaper:470 is not listed on IDEAS
  97. Karamaziotis, Panagiotis I. & Raptis, Achilleas & Nikolopoulos, Konstantinos & Litsiou, Konstantia & Assimakopoulos, Vassilis, 2020. "An empirical investigation of water consumption forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(2), pages 588-606.
  98. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.
  99. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2021. "Point and interval forecasting of electricity supply via pruned ensembles," Energy, Elsevier, vol. 232(C).
  100. Polanski, Arnold & Stoja, Evarist & Zhang, Ren, 2013. "Multidimensional risk and risk dependence," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3286-3294.
  101. Romanus, Eduardo E. & Silva, Eugênio & Goldschmidt, Ronaldo R., 2024. "Empirical probabilistic forecasting: An approach solely based on deterministic explanatory variables for the selection of past forecast errors," International Journal of Forecasting, Elsevier, vol. 40(1), pages 184-201.
  102. He, Yaoyao & Liu, Rui & Li, Haiyan & Wang, Shuo & Lu, Xiaofen, 2017. "Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory," Applied Energy, Elsevier, vol. 185(P1), pages 254-266.
  103. Quan, Hao & Srinivasan, Dipti & Khambadkone, Ashwin M. & Khosravi, Abbas, 2015. "A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources," Applied Energy, Elsevier, vol. 152(C), pages 71-82.
  104. repec:lan:wpaper:425 is not listed on IDEAS
  105. Sayar Karmakar & Marek Chudý & Wei Biao Wu, 2022. "Long‐term prediction intervals with many covariates," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 587-609, July.
  106. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
  107. Michael P. Clements, 2020. "Are Some Forecasters’ Probability Assessments of Macro Variables Better Than Those of Others?," Econometrics, MDPI, vol. 8(2), pages 1-16, May.
  108. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
  109. Ling He & Chenyi Hu, 2009. "Impacts of Interval Computing on Stock Market Variability Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 33(3), pages 263-276, April.
  110. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
  111. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
  112. Wai-Sum Chan, 1999. "Exact joint forecast regions for vector autoregressive models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(1), pages 35-44.
  113. Wolfgang Nierhaus, 2016. "Wirtschaftskonjunktur 2015: Prognose und Wirklichkeit," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 69(03), pages 34-40, February.
  114. Dick van Dijk & Philip Hans Franses & Michael P. Clements & Jeremy Smith, 2003. "On SETAR non-linearity and forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 359-375.
  115. Shaokang Wang & Han Lin Shang & Leonie Tickle & Han Li, 2024. "Forecasting Age- and Sex-Specific Survival Functions: Application to Annuity Pricing," Risks, MDPI, vol. 12(7), pages 1-15, July.
  116. Tsuchiya, Yoichi, 2022. "Evaluating the European Central Bank’s uncertainty forecasts," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 321-330.
  117. Sayar Karmakar & Marek Chudy & Wei Biao Wu, 2020. "Long-term prediction intervals with many covariates," Papers 2012.08223, arXiv.org, revised Sep 2021.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.