Flexible regression models and relative forecast performance
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- Christian M. Dahl, 2002. "An investigation of tests for linearity and the accuracy of likelihood based inference using random fields," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 263-284, 06.
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- 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.
- Hamilton, James D., 1999.
"A Parametric Approach to Flexible Nonlinear Inference,"
University of California at San Diego, Economics Working Paper Series
qt68s8157x, Department of Economics, UC San Diego.
- Hamilton, James D, 2001. "A Parametric Approach to Flexible Nonlinear Inference," Econometrica, Econometric Society, vol. 69(3), pages 537-73, May.
- Aldrin, Magne & Bolviken, Erik & Schweder, Tore, 1993. "Projection pursuit regression for moderate non-linearities," Computational Statistics & Data Analysis, Elsevier, vol. 16(4), pages 379-403, October.
- James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
- Pesaran, M.H. & Timmermann, A.G., 1992.
"A Generalisation of the Non-Parametric Henriksson-Merton Test of Market Timing,"
Cambridge Working Papers in Economics
9218, Faculty of Economics, University of Cambridge.
- Pesaran, M. Hashem & Timmermann, Allan G., 1994. "A generalization of the non-parametric Henriksson-Merton test of market timing," Economics Letters, Elsevier, vol. 44(1-2), pages 1-7.
- Henriksson, Roy D & Merton, Robert C, 1981. "On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills," The Journal of Business, University of Chicago Press, vol. 54(4), pages 513-33, October.
- Dahl, Christian M. & Gonzalez-Rivera, Gloria, 2003. "Testing for neglected nonlinearity in regression models based on the theory of random fields," Journal of Econometrics, Elsevier, vol. 114(1), pages 141-164, May.
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