Abstract
In the application of econometric model, the error distribution is unknown and is not easily to specify in the likelihood function. In some situations, there might exist a mixture distribution in the errors and thus the traditional estimation method would probably yield a biased result. In this study, this mixture distribution of the error term is taken into account and applied in kink regression model. We also use simulation study and the real application analysis to check the performance of this estimator in regression model. The performance of this estimation is then compared with that of conventional least squares, Bayesian, maximum likelihood, generalized maximum entropy methods.
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Chakpitak, N., Yamaka, W., Maneejuk, P. (2019). Predictive Recursion Maximum Likelihood for Kink Regression Model. In: Kreinovich, V., Sriboonchitta, S. (eds) Structural Changes and their Econometric Modeling. TES 2019. Studies in Computational Intelligence, vol 808. Springer, Cham. https://doi.org/10.1007/978-3-030-04263-9_45
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DOI: https://doi.org/10.1007/978-3-030-04263-9_45
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