Three-structured smooth transition regression models based on CART algorithm
In the present work, a tree-based model that combines aspects of CART (Classification and Regression Trees) and STR (Smooth Transition Regression) is proposed. The main idea relies on specifying a parametric nonlinear model through a tree-growing procedure. The resulting model can be analysed either as a fuzzy regression or as a smooth transition regression with multiple regimes. Decisions about splits are entirely based on statistical tests of hypotheses and confidence intervals are constructed for the parameters within the terminal nodes as well as the final predictions. A Monte Carlo Experiment shows the estimators’ properties and the ability of the proposed algorithm to identify correctly several tree architectures. An application to the famous Boston Housing dataset shows that the proposed model provides better explanation with the same number of leaves as the one obtained with the CART algorithm.
|Date of creation:||Jan 2003|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: 021 35271078
Fax: 021 35271084
Web page: http://www.econ.puc-rio.br
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Marcelo C. Medeiros & Timo Terasvirta & Gianluigi Rech, 2002.
"Building Neural Network Models for Time Series: A Statistical Approach,"
Textos para discussão
461, Department of Economics PUC-Rio (Brazil).
- Timo Teräsvirta & Marcelo C. Medeiros & Gianluigi Rech, 2006. "Building neural network models for time series: a statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 49-75.
- Medeiros, Marcelo C. & Teräsvirta, Timo & Rech, Gianluigi, 2002. "Building neural network models for time series: A statistical approach," SSE/EFI Working Paper Series in Economics and Finance 508, Stockholm School of Economics.
- Eitrheim, Oyvind & Terasvirta, Timo, 1996.
"Testing the adequacy of smooth transition autoregressive models,"
Journal of Econometrics,
Elsevier, vol. 74(1), pages 59-75, September.
- Eitrheim, Øyvind & Teräsvirta, Timo, 1995. "Testing the Adequacy of Smooth Transition Autoregressive Models," SSE/EFI Working Paper Series in Economics and Finance 56, Stockholm School of Economics.
- Cooper, Suzanne J, 1998. "Multiple Regimes in U.S. Output Fluctuations," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 92-100, January.
- Potscher, Benedikt M. & Prucha, Ingmar R., 1986. "A class of partially adaptive one-step m-estimators for the non-linear regression model with dependent observations," Journal of Econometrics, Elsevier, vol. 32(2), pages 219-251, July.
- White, Halbert & Domowitz, Ian, 1984. "Nonlinear Regression with Dependent Observations," Econometrica, Econometric Society, vol. 52(1), pages 143-61, January.
- Ciampi, Antonio, 1991. "Generalized regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 12(1), pages 57-78, August.
- White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
- Wooldridge, Jeffrey M., 1991. "On the application of robust, regression- based diagnostics to models of conditional means and conditional variances," Journal of Econometrics, Elsevier, vol. 47(1), pages 5-46, January.
When requesting a correction, please mention this item's handle: RePEc:rio:texdis:469. See general information about how to correct material in RePEc.
If references are entirely missing, you can add them using this form.