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Citations for "Further Results on Forecasting and Model Selection Under Asymmetric Loss"

by Christoffersen & Diebold

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  1. Carlos Capistrán & Allan Timmermann, 2006. "Disagreement and Biases in Inflation Expectations," Working Papers 2006-07, Banco de México.
  2. Philip Hans Franses & Rianne Legerstee & Richard Paap, 2011. "Estimating Loss Functions of Experts," Tinbergen Institute Discussion Papers 11-177/4, Tinbergen Institute.
  3. Christoffersen, Peter F & Diebold, Francis X, 1998. "Cointegration and Long-Horizon Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 450-58, October.
  4. Demetrescu, Matei, 2006. "An extension of the Gauss-Newton algorithm for estimation under asymmetric loss," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 379-401, January.
  5. 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.
  6. Mark A. Clatworthy & David A. Peel & Peter F. Pope, 2012. "Are Analysts' Loss Functions Asymmetric?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(8), pages 736-756, December.
  7. 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.
  8. Patton, Andrew J. & Timmermann, Allan, 2007. "Properties of optimal forecasts under asymmetric loss and nonlinearity," Journal of Econometrics, Elsevier, vol. 140(2), pages 884-918, October.
  9. Diebold, F.X. & Kilian, L. & Nerlove, Marc, 2006. "Time Series Analysis," Working Papers 28556, University of Maryland, Department of Agricultural and Resource Economics.
  10. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
  11. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207, April.
  12. Marcella Niglio, 2007. "Multi-step forecasts from threshold ARMA models using asymmetric loss functions," Statistical Methods and Applications, Springer, vol. 16(3), pages 395-410, November.
  13. Dell'Aquila, Rosario & Ronchetti, Elvezio, 2006. "Stock and bond return predictability: the discrimination power of model selection criteria," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1478-1495, March.
  14. Wolfgang Polasek, 2013. "Forecast Evaluations for Multiple Time Series: A Generalized Theil Decomposition," Working Paper Series 23_13, The Rimini Centre for Economic Analysis.
  15. Christoffersen, Peter F. & Diebold, Francis X., 1997. "Optimal Prediction Under Asymmetric Loss," Econometric Theory, Cambridge University Press, vol. 13(06), pages 808-817, December.
  16. Spyros Skouras, 1998. "Financial Returns and Efficiency as seen by an Artificial Technical Analyst," Finance 9808001, EconWPA, revised 24 Aug 1998.
  17. Philip Hans Franses & Rianne Legerstee & Richard Paap, 2011. "Estimating Loss Functions of Experts," Tinbergen Institute Discussion Papers 11-177/4, Tinbergen Institute.
  18. Rosario Dell'Aquila & Elvezio Ronchetti, 2004. "Stock and Bond Return Predictability : The Discrimination Power of Model Selection Criteria," Research Papers by the Institute of Economics and Econometrics, Geneva School of Economics and Management, University of Geneva 2004.05, Institut d'Economie et Econométrie, Université de Genève.
  19. Matthew L Higgins & Sagarika Mishra, . "State Dependent Asymmetric Loss and the Consensus Forecast of Real U.S. GDP Growth," Financial Econometics Series 2012_10, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  20. Francis X. Diebold & Lutz Kilian, 1997. "Measuring predictability: theory and macroeconomic applications," Working Papers 97-23, Federal Reserve Bank of Philadelphia.
  21. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.
  22. Goodwin, Paul, 2005. "Providing support for decisions based on time series information under conditions of asymmetric loss," European Journal of Operational Research, Elsevier, vol. 163(2), pages 388-402, June.
  23. María Clara Aristizábal Restrepo, . "Evaluación asimétrica de una red neuronal artificial:Aplicación al caso de la inflación en Colombia," Borradores de Economia 377, Banco de la Republica de Colombia.
  24. Sean D. Campbell & Francis X. Diebold, 2002. "Weather Forecasting for Weather Derivatives," Center for Financial Institutions Working Papers 02-42, Wharton School Center for Financial Institutions, University of Pennsylvania.
  25. Christoffersen, Peter F. & Diebold, Francis X., 2003. "Financial asset returns, direction-of-change forecasting, and volatility dynamics," CFS Working Paper Series 2004/08, Center for Financial Studies (CFS).
  26. Pesaran, M. H., 1999. "On Aggregation of Linear Dynamic Models," Cambridge Working Papers in Economics 9919, Faculty of Economics, University of Cambridge.
  27. Corradi, Valentina & Swanson, Norman R. & Olivetti, Claudia, 2001. "Predictive ability with cointegrated variables," Journal of Econometrics, Elsevier, vol. 104(2), pages 315-358, September.
  28. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, 1997. "Evaluating density forecasts," Working Papers 97-6, Federal Reserve Bank of Philadelphia.
  29. Schorfheide, Frank, 2005. "VAR forecasting under misspecification," Journal of Econometrics, Elsevier, vol. 128(1), pages 99-136, September.
  30. Brownlees, Christian T. & Gallo, Giampiero M., 2011. "Shrinkage estimation of semiparametric multiplicative error models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 365-378.
  31. Ulu, Yasemin, 2013. "Multivariate test for forecast rationality under asymmetric loss functions: Recent evidence from MMS survey of inflation–output forecasts," Economics Letters, Elsevier, vol. 119(2), pages 168-171.
  32. Trapani, Lorenzo & Urga, Giovanni, 2009. "Optimal forecasting with heterogeneous panels: A Monte Carlo study," International Journal of Forecasting, Elsevier, vol. 25(3), pages 567-586, July.
  33. Hashem Pesaran, M., 2003. "Aggregation of linear dynamic models: an application to life-cycle consumption models under habit formation," Economic Modelling, Elsevier, vol. 20(2), pages 383-415, March.
  34. Peter Christoffersen & Francis X. Diebold, 2002. "Financial Asset Returns, Market Timing, and Volatility Dynamics," CIRANO Working Papers 2002s-02, CIRANO.
  35. M A Clatworthy & D Peel & P F Pope, 2006. "Are analysts’ loss functions asymmetric?," Working Papers 574591, Lancaster University Management School, Economics Department.
  36. Valentina Corradi & Norman Swanson, 2003. "The Block Bootstrap for Parameter Estimation Error In Recursive Estimation Schemes, With Applications to Predictive Evaluation," Departmental Working Papers 200313, Rutgers University, Department of Economics.
  37. Valentina Corradi & Norman Swanson, 2013. "A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance," Departmental Working Papers 201309, Rutgers University, Department of Economics.
  38. Anatolyev, Stanislav, 2009. "Dynamic modeling under linear-exponential loss," Economic Modelling, Elsevier, vol. 26(1), pages 82-89, January.
  39. Ulu, Yasemin, 2007. "Optimal prediction under LINLIN loss: Empirical evidence," International Journal of Forecasting, Elsevier, vol. 23(4), pages 707-715.
  40. Granger, C.W.J. & Pesaran, M. H., 1999. "Economic and Statistical Measures of Forecast Accuracy," Cambridge Working Papers in Economics 9910, Faculty of Economics, University of Cambridge.
  41. Demosthenes N Tambakis, 2000. "On The Informational Content Of Asset Prices," Computing in Economics and Finance 2000 101, Society for Computational Economics.
  42. Alp, Tansel & Demetrescu, Matei, 2010. "Joint forecasts of Dow Jones stocks under general multivariate loss function," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2360-2371, November.
  43. Francis X. Diebold & Minchul Shin, 2014. "Assessing Point Forecast Accuracy by Stochastic Divergence from Zero," PIER Working Paper Archive 14-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  44. Stanislav Anatolyev & Natalia Kryzhanovskaya, 2009. "Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches," Working Papers w0136, Center for Economic and Financial Research (CEFIR).
  45. Valentina Corradi & Norman Swanson, 2004. "Bootstrap Procedures for Recursive Estimation Schemes With Applications to Forecast Model Selection," Departmental Working Papers 200418, Rutgers University, Department of Economics.