Estimating the Innovation Distribution in Nonlinear Autoregressive Models
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DOI: 10.1023/A:1022413700321
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References listed on IDEAS
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- Gabe Chandler & Wolfgang Polonik, 2017. "Residual Empirical Processes and Weighted Sums for Time-Varying Processes with Applications to Testing for Homoscedasticity," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(1), pages 72-98, January.
- Natalie Neumeyer, 2009. "Smooth Residual Bootstrap for Empirical Processes of Non‐parametric Regression Residuals," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 204-228, June.
- Escanciano, Juan Carlos & Jacho-Chávez, David T., 2012. "n-uniformly consistent density estimation in nonparametric regression models," Journal of Econometrics, Elsevier, vol. 167(2), pages 305-316.
- Müller, Ursula U. & Schick, Anton & Wefelmeyer, Wolfgang, 2009. "Estimators for alternating nonlinear autoregression," Journal of Multivariate Analysis, Elsevier, vol. 100(2), pages 266-277, February.
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Keywords
Constrained model; empirical estimator; influence function; Markov chain model; semiparametric model;All these keywords.
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